research about social media and mental health

Open access
Published: 06 July 2023

Pros & cons: impacts of social media on mental health

Ágnes Zsila 1 , 2 &
Marc Eric S. Reyes ORCID: orcid.org/0000-0002-5280-1315 3

BMC Psychology volume 11 , Article number: 201 ( 2023 ) Cite this article

496k Accesses

22 Citations

125 Altmetric

Metrics details

The use of social media significantly impacts mental health. It can enhance connection, increase self-esteem, and improve a sense of belonging. But it can also lead to tremendous stress, pressure to compare oneself to others, and increased sadness and isolation. Mindful use is essential to social media consumption.

Social media has become integral to our daily routines: we interact with family members and friends, accept invitations to public events, and join online communities to meet people who share similar preferences using these platforms. Social media has opened a new avenue for social experiences since the early 2000s, extending the possibilities for communication. According to recent research [ 1 ], people spend 2.3 h daily on social media. YouTube, TikTok, Instagram, and Snapchat have become increasingly popular among youth in 2022, and one-third think they spend too much time on these platforms [ 2 ]. The considerable time people spend on social media worldwide has directed researchers’ attention toward the potential benefits and risks. Research shows excessive use is mainly associated with lower psychological well-being [ 3 ]. However, findings also suggest that the quality rather than the quantity of social media use can determine whether the experience will enhance or deteriorate the user’s mental health [ 4 ]. In this collection, we will explore the impact of social media use on mental health by providing comprehensive research perspectives on positive and negative effects.

Social media can provide opportunities to enhance the mental health of users by facilitating social connections and peer support [ 5 ]. Indeed, online communities can provide a space for discussions regarding health conditions, adverse life events, or everyday challenges, which may decrease the sense of stigmatization and increase belongingness and perceived emotional support. Mutual friendships, rewarding social interactions, and humor on social media also reduced stress during the COVID-19 pandemic [ 4 ].

On the other hand, several studies have pointed out the potentially detrimental effects of social media use on mental health. Concerns have been raised that social media may lead to body image dissatisfaction [ 6 ], increase the risk of addiction and cyberbullying involvement [ 5 ], contribute to phubbing behaviors [ 7 ], and negatively affects mood [ 8 ]. Excessive use has increased loneliness, fear of missing out, and decreased subjective well-being and life satisfaction [ 8 ]. Users at risk of social media addiction often report depressive symptoms and lower self-esteem [ 9 ].

Overall, findings regarding the impact of social media on mental health pointed out some essential resources for psychological well-being through rewarding online social interactions. However, there is a need to raise awareness about the possible risks associated with excessive use, which can negatively affect mental health and everyday functioning [ 9 ]. There is neither a negative nor positive consensus regarding the effects of social media on people. However, by teaching people social media literacy, we can maximize their chances of having balanced, safe, and meaningful experiences on these platforms [ 10 ].

We encourage researchers to submit their research articles and contribute to a more differentiated overview of the impact of social media on mental health. BMC Psychology welcomes submissions to its new collection, which promises to present the latest findings in the emerging field of social media research. We seek research papers using qualitative and quantitative methods, focusing on social media users’ positive and negative aspects. We believe this collection will provide a more comprehensive picture of social media’s positive and negative effects on users’ mental health.

Data Availability

Not applicable.

Statista. (2022). Time spent on social media [Chart]. Accessed June 14, 2023, from https://www.statista.com/chart/18983/time-spent-on-social-media/ .

Pew Research Center. (2023). Teens and social media: Key findings from Pew Research Center surveys. Retrieved June 14, 2023, from https://www.pewresearch.org/short-reads/2023/04/24/teens-and-social-media-key-findings-from-pew-research-center-surveys/ .

Boer, M., Van Den Eijnden, R. J., Boniel-Nissim, M., Wong, S. L., Inchley, J. C.,Badura, P.,… Stevens, G. W. (2020). Adolescents’ intense and problematic social media use and their well-being in 29 countries. Journal of Adolescent Health , 66(6), S89-S99. https://doi.org/10.1016/j.jadohealth.2020.02.011.

Marciano L, Ostroumova M, Schulz PJ, Camerini AL. Digital media use and adolescents’ mental health during the COVID-19 pandemic: a systematic review and meta-analysis. Front Public Health. 2022;9:2208. https://doi.org/10.3389/fpubh.2021.641831 .

Article Google Scholar

Naslund JA, Bondre A, Torous J, Aschbrenner KA. Social media and mental health: benefits, risks, and opportunities for research and practice. J Technol Behav Sci. 2020;5:245–57. https://doi.org/10.1007/s41347-020-00094-8 .

Article PubMed PubMed Central Google Scholar

Harriger JA, Thompson JK, Tiggemann M. TikTok, TikTok, the time is now: future directions in social media and body image. Body Image. 2023;44:222–6. https://doi.org/10.1016/j.bodyim.2021.12.005 .

Article PubMed Google Scholar

Chi LC, Tang TC, Tang E. The phubbing phenomenon: a cross-sectional study on the relationships among social media addiction, fear of missing out, personality traits, and phubbing behavior. Curr Psychol. 2022;41(2):1112–23. https://doi.org/10.1007/s12144-022-0135-4 .

Valkenburg PM. Social media use and well-being: what we know and what we need to know. Curr Opin Psychol. 2022;45:101294. https://doi.org/10.1016/j.copsyc.2020.101294 .

Bányai F, Zsila Á, Király O, Maraz A, Elekes Z, Griffiths MD, Urbán R, Farkas J, Rigó P Jr, Demetrovics Z. Problematic social media use: results from a large-scale nationally representative adolescent sample. PLoS ONE. 2017;12(1):e0169839. https://doi.org/10.1371/journal.pone.0169839 .

American Psychological Association. (2023). APA panel issues recommendations for adolescent social media use. Retrieved from https://apa-panel-issues-recommendations-for-adolescent-social-media-use-774560.html .

Download references

Acknowledgements

Ágnes Zsila was supported by the ÚNKP-22-4 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund.

Author information

Authors and affiliations.

Institute of Psychology, Pázmány Péter Catholic University, Budapest, Hungary

Ágnes Zsila

Institute of Psychology, ELTE Eötvös Loránd University, Budapest, Hungary

Department of Psychology, College of Science, University of Santo Tomas, Manila, 1008, Philippines

Marc Eric S. Reyes

You can also search for this author in PubMed Google Scholar

Contributions

AZ conceived and drafted the Editorial. MESR wrote the abstract and revised the Editorial. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Marc Eric S. Reyes .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The authors have no competing interests to declare relevant to the content of this article.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Zsila, Á., Reyes, M.E.S. Pros & cons: impacts of social media on mental health. BMC Psychol 11 , 201 (2023). https://doi.org/10.1186/s40359-023-01243-x

Download citation

Received : 15 June 2023

Accepted : 03 July 2023

Published : 06 July 2023

DOI : https://doi.org/10.1186/s40359-023-01243-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Social media
Mental health

BMC Psychology

ISSN: 2050-7283

General enquiries: [email protected]

research about social media and mental health

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

View all journals
Explore content
About the journal
Publish with us
Sign up for alerts
Open access
Published: 07 September 2022

The mental health and well-being profile of young adults using social media

Nina H. Di Cara 1 , 2 ,
Lizzy Winstone 1 ,
Luke Sloan 3 ,
Oliver S. P. Davis 1 , 2 , 4 na1 &
Claire M. A. Haworth 4 , 5 na1

npj Mental Health Research volume 1 , Article number: 11 ( 2022 ) Cite this article

18k Accesses

4 Citations

26 Altmetric

Metrics details

Human behaviour
Interdisciplinary studies
Medical research
Psychiatric disorders

The relationship between mental health and social media has received significant research and policy attention. However, there is little population-representative data about who social media users are which limits understanding of confounding factors between mental health and social media. Here we profile users of Facebook, Twitter, Instagram, Snapchat and YouTube from the Avon Longitudinal Study of Parents and Children population cohort ( N = 4083). We provide estimates of demographics and mental health and well-being outcomes by platform. We find that users of different platforms and frequencies are not homogeneous. User groups differ primarily by sex and YouTube users are the most likely to have poorer mental health outcomes. Instagram and Snapchat users tend to have higher well-being than the other social media sites considered. Relationships between use-frequency and well-being differ depending on the specific well-being construct measured. The reproducibility of future research may be improved by stratifying by sex and being specific about the well-being constructs used.

Cross-platform- and subgroup-differences in the well-being effects of Twitter, Instagram, and Facebook in the United States

The effect of social media on well-being differs from adolescent to adolescent

Variation in social media sensitivity across people and contexts

Introduction.

The trails of data left online by our digital footprints are increasingly being used to measure and understand our health and well-being. Data sourced from social media platforms has been of particular interest given their potential to be used as a form of ‘natural’ observational data about anything from our voting intentions to symptoms of disease. There is not a single, widely agreed definition of the term ‘social media’ 1 , but for the purposes of this study we understand it to be a broad category of internet-based platforms that allow for the exchange of user-generated content by ‘users’ of that platform 2 . Both the huge volumes of data available on such platforms, and their increasing uptake across the population 3 have led to two main fields of interest in the intersections of social media and mental health. These are the prediction of mental health and well-being from our online data 4 and, somewhat reciprocally, the influence of social media on our mental health, particularly in the case of children and young people 5 , 6 . These fields both ask fundamental questions about the mental health and well-being of social media users, to either understand the ways our mental health influences our social media behaviour, or how our social media behaviours influence our mental health.

Across both contexts a wide range of psychological outcomes have been studied, including predicting suicide at a population-level 7 and individually 8 , mapping the influences of social media platforms on disordered eating 9 and self-harm 10 , understanding the impacts of cyberbullying through social media platforms 11 , 12 , and even ethnographic research into online support networks 13 . As highlighted in a recent review which considered research on the relationship between social media use and well-being in adolescents 14 , there has tended to be an inherent assumption that social media is the cause of harm when examining the effect of social media on our health. However, recent investigations such as those by Orben and Przybylski 15 , 16 and Appel and colleagues 17 illustrate that the role of social media in causing harm may be over-estimated. It seems likely that there is some reciprocal relationship between mental health and social media, that requires longitudinal research studies to begin to understand the complexity, coupled with large representative samples to explore the heterogeneity 18 , 19 . Further, there is increasing attention on the role of within-person effects that see impact change between contexts 20 , 21 , as well as individual differences 22 . Meanwhile, attention has also been drawn to the comparative lack of investigation into the potential benefits of social media, such as access to peer support and the ability to readily connect with friends and family, or into the psychological well-being of social media users as opposed to focusing on pathology. Similarly, most psychological prediction tasks using social media focus on predicting illness rather than wellness 4 , 23 .

Regardless of the direction of interest in the relationship between social media and psychological outcomes, researchers face common challenges, with one of the primary issues being a lack of high-quality information on the characteristics of the whole population of social media users 24 . Valuable demographic information on social media users in the United States is regularly produced by the Pew Research Centre 25 , but often researchers rely on algorithmic means to make predictions about the demographics of the groups they study online if they are not recruiting a participant sample whose demographics are known and can be recorded 4 , 24 , 26 . What we do know about social media users is that they are not homogeneous. The demographic features of populations using them vary across platforms and do not tend to be consistent with the characteristics of the general population 25 , 26 , 27 , 28 . This work on the demographic context has been important in understanding the samples that can be drawn from social media platforms, but there remains a lack of information about other characteristics of social media users that are relevant to study outcomes, including mental health and well-being. Consequently, attempts to compare user well-being and mental health between platforms may be unknowingly confounded by differences in the mental health profile of each individual platform. Mellon and Prosser 28 investigated this form of selection bias with respect to differences in political opinion between Facebook and Twitter, and noted the potential for study outcomes to be biased when the outcome variable of interest is associated with the probability of being included in the sample 29 . This also has implications for our assessment of mental health and well-being classification algorithms 30 . For instance, if using Twitter data to classify depression in a random sample of users how many of these users should we expect to be depressed? Should we expect to find more depressed users on Facebook or Instagram? This bench-marking would allow the research community, who frequently face the challenge of establishing reliable ground truth in social media research, to contextualise the sensitivity and specificity of developed models 4 , 24 .

This study aimed to address the gap in the availability of high-quality descriptive data about social media users by describing social media use in a representative UK population cohort study, the Avon Longitudinal Study of Parents and Children (ALSPAC) 31 . We aimed to profile the users of the social media platforms Facebook, Instagram, Twitter, Snapchat and YouTube by considering a range of mental health and well-being measures that are regularly studied, with the objective of better characterising social media users against variables of interest to researchers. These measures included disordered eating, self-harm, suicidal thoughts, and depression as well as positive well-being outcomes which are sometimes neglected in the context of social media research 14 , 16 , 22 like subjective happiness, mental well-being and fulfilment of basic psychological needs. In answering our research questions we also sought to illustrate how cross-sectional data from a representative population cohort can provide meaningful contextual information that informs the way we interpret past and future research about social media users and their mental health. Unlike other studies using cross-sectional data 14 we had no intention of exploring causal questions, but aimed to address unanswered questions of who social media users are, and whether selection bias across platforms may have the potential to unintentionally bias outcome statistics about mental health and well-being.

Specifically, our research questions were:

Are there demographic differences in patterns of social media use (e.g. frequency)?

Are there demographic differences in the user groups of different social media platforms?

Are there differences in the mental health and well-being of those using social media sites at different frequencies?

Are there differences in the mental health and well-being of user groups of different social media platforms?

Sample description

The sample for this study is drawn from the Avon Longitudinal Study of Parents and Children (ALSPAC) 31 , 32 , 33 . Pregnant women resident in Avon, UK with expected dates of delivery from 1st April 1991 to 31st December 1992 were invited to take part in the study. The initial number of pregnancies enrolled was 14,541. Of these initial pregnancies, 13,988 children were alive at 1 year of age. When the oldest children were ~7 years of age an additional 913 children were enrolled. The total sample size for ALSPAC of children alive at one year of age is 14,901. However, since this time there has been a reduction in the sample due to withdrawals, deaths of those in the cohort and also people simply being lost to follow-up. As such the exact number of participants invited to each data collection activity changes with time. Please note that the ALSPAC study website contains details of all the data that is available through a data dictionary and variable search tool ( http://www.bristol.ac.uk/alspac/researchers/our-data/ ). Study data were collected and managed using REDCap electronic data capture tools hosted at the University of Bristol 34 .

The analysis presented in this study is based on a sub-sample of 4083 participants who responded to a self-report questionnaire at a mean age of 24 years old in 2016/17. The survey was sent to 9211 currently enrolled and contactable participants, of whom 4345 (47%) returned it. To maintain a consistent sample throughout the following analyses we considered the 4083 observations with complete cases for questions related to self-harm, suicidal thoughts, disordered eating, and social media use, and without the respondents who said that they ‘didn’t know’ whether they had a social media account ( n < 5); no respondents stated that they did not have a social media account. As well as the survey at age 24, we considered the responses by those in our main sample to a survey one year previously, at age 23, which collected the well-being measures and the Moods and Feelings Questionnaire, matched to their social media use responses at age 24. This resulted in a sub-sample of 2991 participants who had responded to both surveys. Table 1 gives a comparison of the demographic breakdowns across these samples.

This study considered the participants’ responses to a range of mental health and well-being measures, as well as demographic data. A brief overview of each of the measures used is given below.

Throughout this paper, we used Male and Female to refer to the participant’s assigned sex at birth. Participant ethnicity was reported by their parent/s, and is available in the data as White , Ethnic Minority Group , or Unknown , where Ethnic Minority Group was only available as one group rather than broken down into specific ethnicities. There were two variables relevant to socio-economic status. The first was whether the participant had achieved an A Level or equivalent qualification by age 20, the second was their parents’ occupation. Parental occupation was measured using the Registrar General’s Social Class schema 35 , and was collected prior to the birth of the index cohort; we took the higher occupational class of the participant’s parents where available and grouped the overall schema of six categories into those in manual work , and those in non-manual work .

Social media use was measured using three questions. These were: (1) Do you have a social media profile or account on any sites or apps? with possible responses of ‘Yes’, ‘No’ or ‘Don’t know’; (2) Given a list of social media sites, Do you have a page or profile on these sites or apps, and how often do you use them? , where the social media sites were listed and response options were ‘Daily’, ‘Weekly’, ‘Monthly’, ‘Less Than Monthly’ or ‘Never’; (3) How often do you visit any social media sites or apps, using any device? with response options being ‘More than 10 times per day’, ‘2 to 10 times per day’, ‘Once per day’ or ‘Less than once per day’. Here, the definition of ‘social media sites’ in questions (1) and (3) was left to the participant to interpret, whereas in (2) a specific list was provided. In the following analyses, we have summed responses for the use frequencies per platform from question (2) so that ‘Weekly’, ‘Monthly’ and ‘Less than monthly’ are combined to represent ‘Less than daily’.

Depressive symptoms were measured using the short Mood and Feelings Questionnaire (MFQ) 36 , a 13-item scale that has been validated for measuring depressive symptoms in adolescents 37 and in young adulthood 38 . It asks respondents to rate statements, such as I cried a lot and I thought nobody really loved me , as Not true , Sometimes or True based on how they felt over the past two weeks. Missing items were filled with the mode of the individual’s other responses, provided 50% or more of the items were completed. Scores range from 0 to 26, with a higher score indicating more severe depressive symptoms 37 . Here we applied a cut-off score of 12 or above as indicating depression 38 .

Suicidal thoughts were assessed with the question Have you ever thought of killing yourself, even if you would not really do it? with those who indicated that they had ‘within the past year’ being included. Similarly, intentional self-harm was assessed by asking if participants had hurt [themselves] on purpose in any way and we included those who said this had happened at least once within the last year.

Disordered eating was a composite variable that included participants who indicated that they had been told by a healthcare professional that they had an eating disorder (anorexia nervosa, bulimia nervosa, binge eating disorder or another unspecified eating disorder). Participants were also included if they indicated they had engaged in any of the following behaviours at least once a month over the past year with the intention of losing weight or avoiding weight gain: fasting, throwing up, taking laxatives or medication. This classification of disordered eating followed a similar methodology to that used by Micali and colleagues 39 .

Well-being was measured using seven questionnaires. The Warwick Edinburgh Mental Well-being Scale (WEMWBS) is a fourteen-item questionnaire that has been validated for measuring general well-being in the general population 40 , 41 , as well as in young people 42 , 43 . It asks respondents to rate statements such as I’ve been dealing with problems well and I’ve been feeling cheerful , on a five-point Likert-type scale. The total score is between 14 and 70. All items in the WEMWBS are positively worded, and it is focused on measuring positive mental health.

The Satisfaction with Life Scale 44 , 45 is five-item questionnaire designed to measure global cognitive judgements of satisfaction with one’s life, which includes statements such as If I could live my life over, I would change almost nothing . Each question uses a seven-point Likert-type measure and the total score is between 5 and 35. The Subjective Happiness Scale 46 is a four-item questionnaire based on seven-point Likert-type questions, with the overall score being a mean of the four questions, lying in the range of 1 to 7. Respondents answer questions such as whether they consider themselves to be more or less happy than their peers.

The Gratitude Questionnaire (GQ-6) is a six-item measure that uses a seven-point Likert-type scale to assess individual differences in proneness to experiencing gratitude in daily life 47 . This scale includes statements such as I have so much in life to be thankful for and I am grateful to a wide variety of people . Each score is summed to a total between 6 and 42. The Life Orientation Test (LOT-R) is a measure of dispositional optimism that has ten items asked on a 5-point Likert-type scale 48 , though only four of these items are ‘filler’ questions that do not contribute to the final score. The overall score is in the range of 0 to 24, and items that contribute to this include In uncertain times, I usually expect the best and I hardly ever expect things to go my way .

The Meaning in Life questionnaire has 10 items designed to measure two dimensions of meaning in life: (1) Presence of Meaning (how much respondents feel their lives have meaning), and (2) Search for Meaning (how much respondents strive to find meaning and understanding in their lives) 49 . Statements include I understand my life’s meaning in the Presence sub-scale, and I am looking for something that makes my life feel meaningful in the Search sub-scale. Respondents answered each item on a 7-point Likert-type scale, with the two sub-scales scored in total between 5 and 35.

The psychological constructs of autonomy, competence and relatedness associated with self-determination theory were measured using the Basic Psychological Needs in General (BPN) questionnaire 50 . This questionnaire has 21 seven-point Likert-style questions with the final score for each of the three sub-domains being the mean of the responses for that sub-domain. As such each of autonomy, competence and relatedness were scored overall from 1 to 7. Example items include People in my life care about me and I often do not feel very capable .

For all measures missing items were filled with the person-level average, provided that half or more of the items were completed. All of the well-being measures listed were scored in a positive direction, where higher scores indicate higher alignment with the construct being measured.

The descriptive statistics were calculated using the R programming language (v4.0.1) 51 in RStudio (v1.3), primarily using the tidyverse (v1.3.0) package 52 for data manipulation and ggplot2 (v3.3.1) 53 for visualisation. A reproducible version of the manuscript and supporting code can be found from the Code availability statement.

Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. Informed consent for the use of data collected via questionnaires and clinics was obtained from participants following the recommendations of the ALSPAC Ethics and Law Committee at the time. The full list of ethical approval references for ALSPAC can be found on their website ( https://www.bristol.ac.uk/alspac/researchers/research-ethics/ ).

Demographics

We first consider the demographics of social media users across different frequencies of use, and across the five social media platforms: Facebook, Twitter, Instagram, Snapchat and YouTube. These are both taken from the main sample, as described in our ‘Methods’. Table 2 presents the frequency that participants reported using any social media sites each day, based on sex, ethnicity, education, and their parents’ occupational group.

Table 3 gives the percentage of participants from each demographic group who reported being a user of each platform with any use frequency.

The breakdown of every demographic by frequency of use on each platform is provided in full in Supplementary Table 1 . Figure 1 illustrates this breakdown for sex, which is the demographic by which all our following results are stratified due to the imbalance in our sample and the results in Tables 2 and 3 . Social media use and mental health and well-being outcomes are also known to vary according to gender 54 , 55 , 56 .

All social media users in the sample ( N = 4083) are split by female ( N = 2698) and male ( N = 1385), and the frequency with which they use each social media platform given as either ‘Daily’, ‘Less than daily’ or ‘Never’. Labels on the stacked charts give the precise percentage of the group in each of the frequencies for each platform.

Mental health and well-being

First we will consider well-being and indicators of poor mental health across different use frequencies. Figure 2 shows how indicators of poor mental health vary across the three frequencies of use, which are more than 10 times a day, 2–10 times a day and once per day or less; no participants reported using no social media at all. These frequencies are contextualised by the prevalence of each outcome in all users of social media. This figure shows that the lowest category of social media use, that is once per day or less, has the highest proportions of disordered eating, self-harm and suicidal thoughts among women. As seen in Table 2 , only 7.1% of women and 12% of men used social media less than once per day, and so these measurements are subject to wider confidence intervals. Here, depression is defined as being present in those who scored above the cut-off score of 12 in the Short Mood and Feelings Questionnaire (MFQ) 38 . Additional descriptive data about mental health outcomes in the sample is also available in Supplementary Figure 1 and in Supplementary Tables 2 to 6 .

The frequency with which participants used any social media is reported as ‘more than ten times a day’, ‘between two and ten times a day’ or ‘once or less per day’, and the percentage of participants in that group who reported each mental health outcome is given in each sub-plot, with 95% confidence intervals. Disordered eating, self-harm and suicidal thoughts were assessed in the main sample alongside the social media questions ( N = 4083) and included for those participants who reported them in the past year. Depression ( N = 2991) was measured in the sub-sample with the Moods and Feelings questionnaire in the year prior to the social media measurement, and uses a cut off of 12 or more to indicate the presence of depression.

Similarly, each well-being construct is presented in Fig. 3 , and contextualised by the result for all users of social media, regardless of frequency. Separate outcomes are presented for the three sub-scales of the Basic Psychological Needs (BPN) scale and the two sub-scales of the Meaning in Life (MIL) scale. The Life Orientation Test measures optimism, and the Warwick Edinburgh Mental Well-being Scale (WEMWBS) measures overall positive well-being.

Each sub-graph presents each of the seven well-being measures, including the Basic Psychological Needs scale (BPN) sub-scales autonomy, relatedness and competence, and the Meaning In Life (MIL) scale’s two sub-scales of presence and search. Satisfaction With Life, the Life Orientation Test, the Gratitude Questionnaire, Subjective Happiness Scale and the Warwick Edinburgh Mental Wellbeing Scale (WEMWBS) are also included. The mean of each scale is given for all participants ( N = 2991) with 95% confidence intervals, split by male and female, and then for each dichotomous category of use-frequency which is one of ‘more than ten times a day’, ‘between two and ten times a day’ or ‘once or less per day’.

Next we consider the characteristics of daily users of each platform. The relative percentage of daily users against other types of users for each platform can be referred to in Fig. 1 , and versions of Figs. 4 and 5 for all users of each platform are given in Supplementary Figures 2 and 3 .

The percentage of daily users of each platform who have reported each symptom is given in each sub-graph, with 95% confidence intervals. Disordered eating, self-harm and suicidal thoughts were assessed in the main sample alongside the social media questions ( N = 4083) and included for those participants who reported them in the past year. Depression ( N = 2991) was measured in the sub-sample with the Moods and Feelings questionnaire in the year prior to the social media measurement, and uses a cut off of 12 or more to indicate the presence of depression. Participants can belong to the daily user group of more than one platform.

Each sub-graph presents each of the seven well-being measures, including the Basic Psychological Needs scale (BPN) sub-scales autonomy, relatedness and competence, and the Meaning In Life (MIL) scale’s two sub-scales of presence and search. Satisfaction With Life, the Life Orientation Test, the Gratitude Questionnaire, Subjective Happiness Scale and the Warwick Edinburgh Mental Wellbeing Scale (WEMWBS) are also included. The mean of each scale is given for all daily users of each platform from the sub-sample ( N = 2991) with 95% confidence intervals, split by male and female.

Finally Fig. 5 gives the mean well-being score across each platform for each of the seven well-being measures.

This study used data from a UK population cohort study to describe the demographics and key mental health and well-being indicators of social media users by their self-reported frequency of using social media and five different platforms used at ages 23 and 24. Overall, we saw that there were differences in demographics and mental states of users across use-patterns and platforms used. In the following sections, we detail and discuss the implications of these findings for future research across the themes of demographics, use-frequency and platform used.

In general, just over half of participants reported using social media 2–10 times per day, with more than ten times per day still being common at 39%, and only approximately one in ten participants using social media once per day or less. The results showed that those who rated their social media use at the highest frequency (more than ten times per day) were more likely to be women, more likely to be White and more likely have parents who worked in manual occupations. However, sex was the only demographic that appeared to have a statistical relationship with frequency of use, based on a Chi-squared test. Davies and colleagues 57 saw similar results from a Welsh population survey of social media use that found there was a difference in social media use across genders, but not by measures of deprivation.

Figure 1 showed that Facebook is, unsurprisingly, the most popular platform both in being used by 97% of the participants and being the most used platform on a daily basis. Instagram and YouTube showed substantial differences in use patterns across male and female users, with approximately double the percentage of women using Instagram daily as men and, conversely, approximately double the percentage of men using YouTube daily as women. Snapchat also saw higher proportions of daily and overall female users, though this difference between sexes was not as dramatic as for Instagram and YouTube. These patterns of use generally agree with the demographics of users on these sites reported for 18–29-year-old US adults by the Pew Research Center 25 , although our sample saw slightly more Twitter users than their estimated 38%, and fewer YouTube users than their estimated 91% (see Table 3 ). This difference in YouTube users may be partly explained by the fact that it is the only platform with a substantially higher proportion of men than women using it (68% of women vs 83% of men), and that men were under represented in our sample overall compared to women. This emphasises the importance of stratifying results by sex.

Previous research into the demographics of UK Twitter users also aligns with our findings that men and people from higher socio-economic backgrounds are more likely to be Twitter users than women 26 , 28 . Here, we also saw that those from ethnic minority groups are more likely to be Twitter users than White participants, though this is limited by the fact that we could not further separate out results for people with different ethnicities due to the variables available. Across our sample, Twitter was the only social media platform that had a noticeably higher proportion of both A Level educated participants and parents in non-manual occupations. Snapchat saw the reverse pattern with a higher proportion of participants who did not have A Level qualifications and a higher proportion of participants whose parents worked in manual occupations.

Overall, the sex differences between all male and female users varied across outcomes. For instance, a higher percentage of women experienced depression, disordered eating and self-harm overall, but the gap in the prevalence of suicidal thoughts between men and women was much smaller. This concurs with evidence from the last UK-wide psychiatric morbidity survey, in that ‘common mental health disorders’ are more prevalent in women than men 58 . When it came to well-being, we saw that women also displayed higher mean levels of well-being across most measures. Exceptions were the Life Orientation Test, which showed men generally had higher levels of optimism, the Subjective Happiness Scale where scores were roughly equivalent, and the WEMWBS where men’s general well-being was slightly higher. These results, apart from the WEMWBS, are consistent with findings on UK-wide well-being at the time of the survey, and that men tend to have higher optimism in general 59 , 60 . Previous research into the WEMWBS has not generally found large sex differences, but there is evidence that in younger samples there are differences that may be explained by socio-economic status 40 , 41 , 61 ; we note that higher attrition of men in our sample was likely to lead to a bias towards men who are more socio-economically privileged, which may explain why they had higher well-being.

The patterns of mental health outcomes by use frequency displayed in Fig. 2 showed some support for the so-called ‘Goldilocks theory’ of social media use that hypothesises a quadratic, rather than linear, stimulus-response relationship between social media use and mental well-being 62 . This would mean that moderate use of social media, rather than very little or excessive use, is best for well-being. However, this pattern did not consistently apply. For instance, there was an inverse relationship between social media use and percentage of women who self-harm, and in men only the group with the highest level of social media use had more severe depressive symptoms. Previous research has found that in young women higher social media use was associated with increased risk of self-harm 63 , which is in contrast to our results. Similarly, research using the Millennium Cohort Study also found an increasing relationship between objectively measured number of hours spent on social media and how many respondents had clinically relevant symptoms of depression 64 , with a greater increase for girls than boys. Our findings roughly concur with those for the boys, but in women we found that those who used social media the least had the highest rates of depression. However, these differences in findings could reflect the difference in the age of participants or the ways that social media was measured differently across studies. Here we were using use-frequency as categorised into three groups which, as we discuss further in our limitations, may be more reflective of the individual’s mental health and relationship with social media than how frequently they use it 65 .

When considering the results by well-being measure in Fig. 3 we saw that subjective happiness and optimism as measured by the Life Orientation Test both appeared relatively consistent across use categories. Relatedness presented the clearest difference across use categories, with relatedness in women being higher for the two most frequent use frequencies. However, perhaps the most notable outcome was the inconsistency between well-being scales which implies that the choice of scale could affect the interpretation of the impact of well-being on social media use. Research into the relationship between social media use and well-being has been said to suffer from what is known as the ‘jingle-jangle’ paradox where the term ‘well-being’ is used as a catch-all for anything from depression rates to life satisfaction 66 , 67 . This conflation of different well-being measures leads to comparisons of different psychological constructs which may interact differently with social media use: this is hypothesised as one of the reasons that researchers find conflicting evidence for this relationship 66 , which our results support. This also adds to the picture of researcher degrees of freedom in choosing how to measure psychological constructs, which has been shown to have a substantial impact on the outcome of analyses of social media and mental health 15 . Subjective well-being is a complex and multi-faceted psychological concept 68 , 69 , and these findings illustrate the importance of recognising that different measures of well-being could imply different relationships between social media and “well-being”.

When considering participant outcomes by daily users of each platform more consistent patterns emerge than for use-frequencies. We saw that, particularly for women, YouTube had the highest proportion of users reporting disordered eating, self-harm, suicidal thoughts and depression, with higher prevalence of depression in female users of YouTube compared to male users (Fig. 4 ). Whilst overall mental well-being across platforms, as measured by the WEMWBS in Fig. 5 , shows YouTube as being marginally but not drastically lower than other platforms, other well-being measures illustrated some key differences. For instance, YouTube users had lower life satisfaction, relatedness and, particularly for female users, levels of competence (Fig. 5 ). Conversely, daily users of Instagram, and in some cases Snapchat, appeared to have the highest subjective well-being across most measures, with this being particularly noticeable for relatedness, gratitude and happiness (Fig. 5 ). The role of self-determination theory in social media use has previously been explored for Facebook and social media in general 70 with relatedness hypothesised as a key motivating factor for social media use. Previous findings have shown that Instagram and Snapchat are used more for social interaction than Twitter and Facebook 71 , and so our results may corroborate the importance of relatedness in the use of particular platforms. Regardless of the specific measure, our results have illustrated that there is variation amongst platforms which further challenges the idea that ‘social media’ or ‘social networking sites’ are a homogeneous group, and reiterates the importance of understanding the context of research about or using social media 28 , 71 .

At face value, our results appear to directly contrast with the outcomes of the Status of Mind report published by the Royal Society for Public Health 72 , where young people rated YouTube as being the most beneficial site for their well-being and Instagram as the worst, based on health-related outcomes such as their anxiety and depression. Our findings that a higher prevalence of YouTube users suffer from poorer mental health and well-being may mean that whilst some platforms are seen as ‘worse’ for young people’s mental health, that does not equate to finding more unwell young people on those platforms. One explanation may be that those experiencing poorer mental health are more likely to use YouTube because they experience more benefits to their mental health from YouTube, such as community building and peer support 13 , than they do from spending time on sites like Instagram. However, this is certainly an interesting area for further exploration in future quantitative and qualitative research.

Whilst this research draws evidence from a robust and well-documented study and the sample being from a birth cohort means that our results are not confounded by age, there are limitations to the cohort sample that we have used. Firstly, the cohort measures a specific age group so we can only infer information about a single age group at each measurement time point. We suspect that different patterns might be found at different ages, knowing that rates of various mental health conditions such as anxiety, depression and suicidality change over the course of childhood, adolescence and adulthood 73 , and since each generation may use social media differently 74 . It is also important to note that the two data collection points used in this study were taken a year apart, and so not all measures were taken exactly at the same time. This means that although we have primarily considered the data cross-sectionally there is a potential for some longitudinal effects to have influenced the data. Secondly, as discussed in the ‘Methods’ section, there was also a limitation in that ethnicity was only available as two categories (White or Ethnic Minority Groups) and so it was not possible to look further into differences in social media by users of difference ethnicities. Additionally, the make up of the area of Bristol that ALSPAC represents is predominantly White. Given these limitations of the sample it would be valuable to conduct similar research in other cohorts that represent more diverse areas. Thirdly, ALSPAC has seen differential attrition over time and so, as seen in Table 1 , the sample for this study when the index cohort were in their early twenties has fewer men than women, and more participants from privileged socio-economic groups in terms of education and class background 31 . As well as this, typical social media use changes over time and by age 25 , and so further assessment of social media use across a variety of population-representative age groups would be the most effective way to understand differences between generations.

Another limitation of this study is a lack of specificity about the nature of social media use that participants are referring to when responding. It is possible that activities related to ‘using’ social media, such as posting content versus passive use, change depending on platform used and that there are individual preferences to account for 54 , 71 , 75 , 76 . For instance, YouTube is distinct from other platforms in this study in that its primary function is passive content consumption as opposed to social networking. Previous research has suggested a reciprocal association between passive social media use and lower subjective well-being 75 , whilst using social media for direct communication has been positively associated with perceived friend support 77 . This may better reflect the uses of platforms like Snapchat. As well as the subjective nature of ‘use’, there are also ongoing concerns about using self-reported measures of use-frequency to measure social media behaviours 78 , 79 , 80 . Emerging evidence is showing that self-reports do not align well with objective measurement due to recall bias and differences in interpreting how to include notifications or fleeting checks of social media 79 , 80 with self-reported smartphone pickups underestimating associations with mental health compared to objective measures of use 65 . It might be that different ways of measuring social media use, such as types of use, are more useful when considering associations with mental health and well-being outcomes 54 . It is worth noting that the use-frequency measures used in this study are distinct from screen-time, and equivalent use-frequency across platforms may have different time implications; someone may spend short amounts of time on Instagram or Snapchat checking notifications, but do so frequently, versus visiting YouTube once in a day but spending several hours watching content. These nuances are challenging to capture, but by reporting on mental health prevalence across the available responses in a cohort study we can add to the growing understanding of how self-reported social media use frequency is related to mental health. Statistical modelling to test the extent of the differences observed between mental health constructs, use-frequencies and platforms would be valuable future research.

In summary, our results amplify the importance of attending to complexity when measuring and analysing social media use and mental health and well-being. It is important to note that our results do not, and cannot, imply that different types of social media use cause poorer or better health outcomes in young people, but they do provide vital contextual information on user groups that can help us better understand the reasons that previous research has found conflicting results. We have provided estimates of seven well-being measures and the prevalence of four key mental health outcomes (depression, disordered eating, suicidal thoughts and self-harm) across the five platforms Facebook, Twitter, Instagram, Snapchat and YouTube, as well as across three use frequencies. Our findings have shown that the demographic and mental health foot-print of each platform is different. Primarily users differ by sex, but when it comes to platforms YouTube is particularly likely to have both male and female users with poorer mental health and well-being across a range of indicators, alongside evidence that daily Instagram users have better overall well-being than daily users of other platforms. Our findings also indicate that relationships between use-frequency and multiple mental health and well-being outcomes are often non-linear, which supports the importance of considering non-linear dose-response relationships between social media and mental health and well-being in future research. Lastly, we saw that the relationship between use-frequencies and well-being changes depending on the measure of well-being used. This means that we cannot conflate different types of well-being, and doing so will likely result in low replicability.

This research has implications for both those who conduct research on the relationship between social media and mental health, and those who study mental health prediction. We must ensure we are considering both platform-specific and outcome-specific effects rather than conflating types of social media use, social media sites and well-being as single entities. Future research should also stratify results by sex since it is unlikely that studies with differently balanced samples will replicate. Our findings on use-frequencies also suggest that we cannot assume linear relationships between social media use and mental health. Our understanding of these methodological issues would be improved by examining profiles of different user age-groups, as well as examining relationships between these variables longitudinally to understand the potential for reciprocal effects. The differences between platforms should be further considered too, as to how different content types and communication modes on different platforms may affect mental health differently.

Data availability

The datasets analysed during the current study are not publicly available as the informed consent obtained from ALSPAC participants does not allow data to be made freely available through any third party maintained public repository. However, data used for this submission can be made available on request to the ALSPAC Executive, with reference to project number B3227. The ALSPAC data management plan describes in detail the policy regarding data sharing, which is through a system of managed open access. Full instructions for applying for data access can be found here: http://www.bristol.ac.uk/alspac/researchers/access/ . The ALSPAC study website contains details of all the data that are available ( http://www.bristol.ac.uk/alspac/researchers/our-data/ ).

Code availability

The code used to produce the results in this study can be found at https://doi.org/10.17605/OSF.IO/RKXM6 .

Bayer, J. B., Trieu, P. & Ellison, N. B. Social media elements, ecologies, and effects. Annu. Rev. Psychol. 71 , 471–497 (2020).

Carr, C. T. & Hayes, R. A. Social media: defining, developing, and divining. Atl. J. Commun. 23 , 46–65 (2015).

Article Google Scholar

Office for National Statistics. Internet Access: Households and Individuals (2020).

Chancellor, S. & De Choudhury, M. Methods in predictive techniques for mental health status on social media: a critical review. NPJ Digit. Med. 3 , 1–11 (2020).

Hollis, C., Livingstone, S. & Sonuga-Barke, E. The role of digital technology in children and young people’s mental health—a triple-edged sword? J. Child Psychol. Psychiatry https://doi.org/10.1111/jcpp.13302 (2020).

Dubicka, B. & Theodosiou, L. Technology Use and the Mental Health of Children and Young People . https://www.rcpsych.ac.uk/docs/default-source/improving-care/better-mh-policy/college-reports/college-report-cr225.pdf (2020).

Lee, K. S. et al. Advanced daily prediction model for national suicide numbers with social media data. Psychiatry Investig. 15 , 344 (2018).

Article CAS PubMed PubMed Central Google Scholar

Roy, A. et al. A machine learning approach predicts future risk to suicidal ideation from social media data. NPJ Digit. Med. 3 , 1–12 (2020).

Santarossa, S. & Woodruff, S. J. # SocialMedia: exploring the relationship of social networking sites on body image, self-esteem, and eating disorders. Soc. Media Soc. 3 , 2056305117704407 (2017).

Google Scholar

Arendt, F., Scherr, S. & Romer, D. Effects of exposure to self-harm on social media: evidence from a two-wave panel study among young adults. New Media Soc. 21 , 2422–2442 (2019).

Hamm, M. P. et al. Prevalence and effect of cyberbullying on children and young people: a scoping review of social media studies. JAMA Pediatr. 169 , 770–777 (2015).

Article PubMed Google Scholar

Craig, W. et al. Social media use and cyber-bullying: a cross-national analysis of young people in 42 countries. J. Adolesc. Health 66 , S100–S108 (2020).

Naslund, J. A., Grande, S. W., Aschbrenner, K. A. & Elwyn, G. Naturally occurring peer support through social media: the experiences of individuals with severe mental illness using YouTube. PLOS ONE 9 , e110171 (2014).

Schønning, V., Hjetland, G. J., Aarø, L. E. & Skogen, J. C. Social media use and mental health and well-being among adolescents—a scoping review. Front. Psychol. 11 , 1949 (2020).

Article PubMed PubMed Central Google Scholar

Orben, A. & Przybylski, A. K. The association between adolescent well-being and digital technology use. Nat. Hum. Behav. 3 , 173–182 (2019).

Orben, A. Teenagers, screens and social media: A narrative review of reviews and key studies. Soc. Psychiatry Psychiatr. Epidemiol. 55 , 407–414 (2020).

Appel, M., Marker, C. & Gnambs, T. Are social media ruining our lives? A review of meta-analytic evidence. Rev. Gen. Psychol. 24 , 60–74 (2020).

Coyne, S. M., Rogers, A. A., Zurcher, J. D., Stockdale, L. & Booth, M. Does time spent using social media impact mental health?: an eight year longitudinal study. Comput. Hum. Behav. 104 , 106160 (2020).

Primack, B. A., Shensa, A., Sidani, J. E., Escobar-Viera, C. G. & Fine, M. J. Temporal associations between social media use and depression. Am. J. Prev. Med. 60 , 179–188 (2021).

Valkenburg, P., Beyens, I., Pouwels, J. L., Driel, I. I. van & Keijsers, L. Social media use and adolescents’ self-esteem: heading for a person-specific media effects paradigm. J. Commun. https://doi.org/10.1093/joc/jqaa039 (2021).

Beyens, I., Pouwels, J. L., Driel, I. I. van, Keijsers, L. & Valkenburg, P. M. The effect of social media on well-being differs from adolescent to adolescent. Sci. Rep. 10 , 1–11 (2020).

Article CAS Google Scholar

Weinstein, E. The social media see-saw: positive and negative influences on adolescents’ affective well-being. New Media Soc. 20 , 3597–3623 (2018).

Guntuku, S. C., Yaden, D. B., Kern, M. L., Ungar, L. H. & Eichstaedt, J. C. Detecting depression and mental illness on social media: An integrative review. Curr. Opin. Behav. Sci. 18 , 43–49 (2017).

Amir, S., Dredze, M. & Ayers, J. W. Mental health surveillance over social media with digital cohorts. In Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology 114–120 (2019).

Center, Pew Research. Demographics of Social Media Users and Adoption in the United States (2021).

Sloan, L. Who tweets in the united kingdom? Profiling the Twitter population using the British social attitudes survey 2015. Soc. Media Soc. 3 , 2056305117698981 (2017).

Sloan, L., Morgan, J., Burnap, P. & Williams, M. Who tweets? Deriving the demographic characteristics of age, occupation and social class from Twitter user meta-data. PLoS ONE 10 , e0115545 (2015).

Mellon, J. & Prosser, C. Twitter and Facebook are not representative of the general population: political attitudes and demographics of British social media users. Res. Politics 4 , 2053168017720008 (2017).

Heckman, J. J. Sample selection bias as a specification error. Econometrica: J. Econometric Soc. 153–161. https://doi.org/10.2307/1912352 (1979).

Aguirre, C., Harrigian, K. & Dredze, M. Gender and racial fairness in depression research using social media. In The 16th Conference of the European Chapter of the Association for Computational Linguistics (2021).

Boyd, A. et al. Cohort profile: The “children of the 90 s”—the index offspring of the Avon longitudinal study of parents and children. Int. J. Epidemiol. 42 , 111–127 (2013).

Fraser, A. et al. Cohort profile: the Avon longitudinal study of parents and children: ALSPAC mothers cohort. Int. J. Epidemiol. 42 , 97–110 (2013).

Northstone, K. et al. The Avon longitudinal study of parents and children (ALSPAC): an update on the enrolled sample of index children in 2019. Wellcome Open Res. 4 , 51 (2019).

Harris, P. A. et al. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J. Biomed. Inform. 42 , 377–381 (2009).

Szreter, S. R. The genesis of the registrar-general’s social classification of occupations. Brit. J. Soc. 522–546. https://doi.org/10.2307/590433 (1984).

Costello, E. J. & Angold, A. Scales to assess child and adolescent depression: checklists, screens, and nets. J. Am. Acad. Child Adolesc. Psychiatry 27 , 726–737 (1988).

Article CAS PubMed Google Scholar

Angold, A. et al. Development of a short questionnaire for use in epidemiological studies of depression in children and adolescents: factor composition and structure across development. Int. J. Methods Psychiatr. Res. 5 , 237–249 (1995).

Eyre, O. et al. Validation of the short Mood and Feelings Questionnaire in young adulthood. J. Affect. Disord. 294 , 883–888 (2021).

Micali, N. et al. Eating disorder behaviours amongst adolescents: investigating classification, persistence and prospective associations with adverse outcomes using latent class models. Eur. Child Adolesc. Psychiatry 26 , 231–240 (2017).

Tennant, R. et al. The Warwick-Edinburgh mental well-being scale (WEMWBS): development and UK validation. Health Qual. Life Outcomes 5 , 63 (2007).

Ng Fat, L., Scholes, S., Boniface, S., Mindell, J. & Stewart-Brown, S. Evaluating and establishing national norms for mental wellbeing using the short Warwick–Edinburgh mental well-being scale (SWEMWBS): findings from the health survey for England. Qual. Life Res. 26 , 1129–1144 (2017).

Ringdal, R., Bradley Eilertsen, M.-E., Bjørnsen, H. N., Espnes, G. A. & Moksnes, U. K. Validation of two versions of the Warwick-Edinburgh mental well-being scale among Norwegian adolescents. Scand. J. Public Health 46 , 718–725 (2018).

McKay, M. T. & Andretta, J. R. Evidence for the psychometric validity, internal consistency and measurement invariance of Warwick Edinburgh mental well-being scale scores in Scottish and Irish adolescents. Psychiatry Res. 255 , 382–386 (2017).

Diener, E., Emmons, R. A., Larsen, R. J. & Griffin, S. The satisfaction with life scale. J. Pers. Assess. 49 , 71–75 (1985).

Pavot, W. & Diener, E. The satisfaction with life scale and the emerging construct of life satisfaction. J. Posit. Psychol. 3 , 137–152 (2008).

Lyubomirsky, S. & Lepper, H. S. A measure of subjective happiness: preliminary reliability and construct validation. Soc. Indic. Res. 46 , 137–155 (1999).

McCullough, M. E., Emmons, R. A. & Tsang, J.-A. The grateful disposition: a conceptual and empirical topography. J. Pers. Soc. Psychol. 82 , 112 (2002).

Scheier, M. F., Carver, C. S. & Bridges, M. W. Distinguishing optimism from neuroticism (and trait anxiety, self-mastery, and self-esteem): a reevaluation of the life orientation test. J. Pers. Soc. Psychol. 67 , 1063 (1994).

Steger, M. F., Frazier, P., Oishi, S. & Kaler, M. The meaning in life questionnaire: assessing the presence of and search for meaning in life. J. Couns. Psychol. 53 , 80 (2006).

Deci, E. L. & Ryan, R. M. The” what” and” why” of goal pursuits: human needs and the self-determination of behavior. Psychol. Inq. 11 , 227–268 (2000).

R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2020).

Wickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. 4 , 1686 (2019).

Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).

Winstone, L., Mars, B., Haworth, C. M., Heron, J. & Kidger, J. Adolescent social media user types and their mental health and well-being: results from a longitudinal survey of 13–14-year-olds in the United Kingdom. https://doi.org/10.31234/osf.io/cg86n (2021).

Boyd, A. et al. Gender differences in mental disorders and suicidality in Europe: results from a large cross-sectional population-based study. J. Affect. Disord. 173 , 245–254 (2015).

Matud, M. P., López-Curbelo, M. & Fortes, D. Gender and psychological well-being. Int. J. Environ. Res. Public Health 16 , 3531 (2019).

Article PubMed Central Google Scholar

Davies, A., Song, J. & Sharp, C. Social media engagement and health. Int. J. Popul. Data Sci. 4 https://doi.org/10.23889/ijpds.v4i3.1278 (2019).

McManus, S., Bebbington, P. E., Jenkins, R. & Brugha, T. Mental Health and Wellbeing in England: the Adult Psychiatric Morbidity Survey 2014 . https://files.digital.nhs.uk/pdf/q/3/mental_health_and_wellbeing_in_england_full_report.pdf (2016).

Tabor, D. & Stockley, L. Personal Well-being in the UK: October 2016 to September 2017 . https://www.ons.gov.uk/peoplepopulationandcommunity/wellbeing/bulletins/measuringnationalwellbeing/october2016toseptember2017 (2018).

Glaesmer, H. et al. Psychometric properties and population-based norms of the life orientation test revised (LOT-r). Brit. J. Health Psychol. 17 , 432–445 (2012).

Clarke, A. et al. Warwick-Edinburgh mental well-being scale (WEMWBS): Validated for teenage school students in England and Scotland. A mixed methods assessment. BMC Public Health 11 , 487 (2011).

Przybylski, A. K. & Weinstein, N. A large-scale test of the goldilocks hypothesis: quantifying the relations between digital-screen use and the mental well-being of adolescents. Psychol. Sci. 28 , 204–215 (2017).

Barthorpe, A., Winstone, L., Mars, B. & Moran, P. Is social media screen time really associated with poor adolescent mental health? A time use diary study. J. Affect. Disord. 274 , 864–870 (2020).

Kelly, Y., Zilanawala, A., Booker, C. & Sacker, A. Social media use and adolescent mental health: findings from the UK millennium cohort study. EClinicalMedicine 6 , 59–68 (2018).

Shaw, H. et al. Quantifying smartphone ‘use’: choice of measurement impacts relationships between ‘usage’ and health. Technol. Mind Behav. 1 https://doi.org/10.1037/tmb0000022 (2020).

Kross, E. et al. Social media and well-being: Pitfalls, progress, and next steps. Trend. Cog. Sci. https://doi.org/10.1016/j.tics.2020.10.005 (2020).

Meier, A. & Reinecke, L. Computer-mediated communication, social media, and mental health: a conceptual and empirical meta-review. Commun. Res. https://doi.org/10.1177/0093650220958224 (2020).

Diener, E., Oishi, S. & Tay, L. Advances in subjective well-being research. Nat. Hum. Behav. 2 , 253–260 (2018).

Lin, J.-H. Need for relatedness: a self-determination approach to examining attachment styles, facebook use, and psychological well-being. Asian J. Commun. 26 , 153–173 (2016).

Berezan, O., Krishen, A. S., Agarwal, S. & Kachroo, P. The pursuit of virtual happiness: exploring the social media experience across generations. J. Bus. Res. 89 , 455–461 (2018).

Alhabash, S. & Ma, M. A tale of four platforms: motivations and uses of Facebook, Twitter, Instagram, and Snapchat among college students? Soc. Media Soc. 3 , 2056305117691544 (2017).

Royal Society for Public Health. StatusOfMind: Social Media and Young People’s Mental Health and Wellbeing . https://ed4health.co.uk/wp-content/uploads/2018/12/RSPH-Status-of-Mind-report.pdf (2018).

Maughan, B., Collishaw, S. & Stringaris, A. Depression in childhood and adolescence. J. Can. Acad. Child Adolesc. Psychiatry 22 , 35 (2013).

PubMed PubMed Central Google Scholar

Di Cara, N. H. et al. Views on social media and its linkage to longitudinal data from two generations of a UK cohort study. Wellcome Open Res. 5 https://doi.org/10.12688/wellcomeopenres.15755.2 (2020).

Wang, J.-L., Gaskin, J., Rost, D. H. & Gentile, D. A. The reciprocal relationship between passive social networking site (SNS) usage and users’ subjective well-being. Soc. Sci. Comput. Rev. 36 , 511–522 (2018).

Winstone, L., Mars, B., Haworth, C. M. & Kidger, J. Types of social media use and digital stress in early adolescence. J. Early Adolesc. https://doi.org/10.1177/02724316221105560 (2022).

Frison, E. & Eggermont, S. Toward an integrated and differential approach to the relationships between loneliness, different types of Facebook use, and adolescents’ depressed mood. Commun. Res. 47 , 701–728 (2015).

Scharkow, M. The accuracy of self-reported internet use—a validation study using client log data. Commun. Methods Meas. 10 , 13–27 (2016).

Ernala, S. K., Burke, M., Leavitt, A. & Ellison, N. B. How well do people report time spent on Facebook? An evaluation of established survey questions with recommendations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems 1–14 (2020).

Parry, D. A. et al. A systematic review and meta-analysis of discrepancies between logged and self-reported digital media use. Nat. Hum. Behav. 5 , 1535–1547 (2021).

Download references

Acknowledgements

We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. The UK Medical Research Council and Wellcome (Grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. A comprehensive list of grants funding is available on the ALSPAC website ( http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf ). The data used in this research was specifically funded by the NIHR (1215-20011), the Wellcome Trust (SSCM.RD1809) and the MRC (102215/2/13/2, MR/M006727/1). N.D. is supported by an MRC GW4 BioMed studentship in Data Science and AI (MR/N013794/1). C.M.A.H. is supported by a Philip Leverhulme Prize. N.H., O.S.P.D. and C.M.A.H. will serve as guarantors for the contents of this paper.

Author information

These authors jointly supervised this work: Oliver S. P. Davis, Claire M. A. Haworth.

Authors and Affiliations

Department of Population Health Science, University of Bristol, Bristol, UK

Nina H. Di Cara, Lizzy Winstone & Oliver S. P. Davis

MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK

Nina H. Di Cara & Oliver S. P. Davis

Cardiff University, Cardiff, Wales, UK

The Alan Turing Institute, London, UK

Oliver S. P. Davis & Claire M. A. Haworth

Department of Psychological Science, University of Bristol, Bristol, UK

Claire M. A. Haworth

You can also search for this author in PubMed Google Scholar

Contributions

N.D. was responsible for data curation, formal analysis, investigation, methodology, visualisation and writing (original draft and reviewing and editing). L.W. was responsible for methodology, investigation and writing (reviewing and editing). L.S. was responsible for methodology, investigation, supervision and writing (reviewing and editing). O.D. and C.H. were responsible for funding acquisition, conceptualisation, methodology, investigation, supervision and writing (reviewing and editing).

Corresponding author

Correspondence to Nina H. Di Cara .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Di Cara, N.H., Winstone, L., Sloan, L. et al. The mental health and well-being profile of young adults using social media. npj Mental Health Res 1 , 11 (2022). https://doi.org/10.1038/s44184-022-00011-w

Download citation

Received : 12 November 2021

Accepted : 04 May 2022

Published : 07 September 2022

DOI : https://doi.org/10.1038/s44184-022-00011-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

From temperament to youtube: exploring the link between childhood temperament, youtube usage patterns, and emotional/behavioral problems among children.

Donghee Kim

BMC Public Health (2024)

Quick links

Explore articles by subject
Guide to authors
Editorial policies

Social Media and Mental Health

January 2021
SSRN Electronic Journal
This person is not on ResearchGate, or hasn't claimed this research yet.

Massachusetts Institute of Technology

Discover the world's research

25+ million members
160+ million publication pages
2.3+ billion citations
Nalongo Ruth

Anke Oenema

Jenny Radesky
Jessica Pater

Bahadur Rahut

Richard Harvey

Duygu Balan
Yener Balan
Nadir Balan
Genta Rizki Alfaridzi
Elisabet Mediana Putri
Sulistiasih Sulistiasih

Carlo Schwarz
Kirill Borusyak
Xavier Jaravel
Jann Spiess
Marika Cabral
Bokyung Kim

Hannes Schwandt
Thomas Fujiwara
J ECONOMETRICS
Andrew Goodman-Bacon
Brantly Callaway

Sarah Abraham

Frank Schilbach
Vikram Patel
Desmond Ang
Clément de Chaisemartin
Xavier d'Haultfoeuille
Recruit researchers
Join for free
Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Publications
Account settings
My Bibliography
Collections
Citation manager

Save citation to file

Email citation, add to collections.

Create a new collection
Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

Search in PubMed
Search in NLM Catalog
Add to Search

Social Media Use and Mental Health: A Global Analysis

Affiliations.

1 Department of Public Health & Prevention Science, Baldwin Wallace University, Berea, OH 44017, USA.
2 Department of Sociology and Anthropology, St Louis University, St. Louis, MO 63108, USA.
3 Department of Business Development, Ofogh Kourosh Chain Stores, Tehran 1433894961, Iran.
4 Department of Public Health, Amherst College, Amherst, MA 01002, USA.
5 Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, North Texas, Fort Worth, TX 76107, USA.
PMID: 36417264
PMCID: PMC9620890
DOI: 10.3390/epidemiologia3010002

Research indicates that excessive use of social media can be related to depression and anxiety. This study conducted a systematic review of social media and mental health, focusing on Facebook, Twitter, and Instagram. Based on inclusion criteria from the systematic review, a meta-analysis was conducted to explore and summarize studies from the empirical literature on the relationship between social media and mental health. Using PRISMA guidelines on PubMed and Google Scholar, a literature search from January 2010 to June 2020 was conducted to identify studies addressing the relationship between social media sites and mental health. Of the 39 studies identified, 20 were included in the meta-analysis. Results indicate that while social media can create a sense of community for the user, excessive and increased use of social media, particularly among those who are vulnerable, is correlated with depression and other mental health disorders.

Keywords: Facebook; Instagram; Twitter; mental health; social media; systematic review.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

PRISMA 2009 flowchart showing research…

PRISMA 2009 flowchart showing research of records.

Forest plot of the studies.

Forest plot of the studies.…

Forest plot of the studies. Grouped by social media platforms.

Forest plot of the studies. Grouped by sample size.

Forest plot of the studies. Grouped by year of publication.

Funnel plot for publication bias.

Publication types

Search in MeSH

Related information

Linkout - more resources, full text sources.

Europe PubMed Central
PubMed Central

Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

Social media brings benefits and risks to teens. Psychology can help identify a path forward

New psychological research exposes the harms and positive outcomes of social media. APA’s recommendations aim to add science-backed balance to the discussion

Vol. 54 No. 6 Print version: page 46

Social Media and Internet
Technology and Design

teens with skateboards looking at smartphones

This was the year that social media itself went viral—and not in a good way. In March, President Joe Biden threatened to ban the Chinese-owned video-sharing site TikTok. In April, a bipartisan group of senators introduced legislation to ban kids under 13 from joining social media. In May, the U.S. surgeon general issued an advisory urging action to protect children online ( Social Media and Youth Mental Health: The U.S. Surgeon General’s Advisory , 2023 ). Just days earlier, APA issued its first-ever health advisory, providing recommendations to protect youth from the risks of social media ( Health Advisory on Social Media Use in Adolescence , 2023 ).

As youth mental health continues to suffer, parents, teachers, and legislators are sounding the alarm on social media. But fear and misinformation often go hand in hand. APA’s recommendations aim to add science-backed balance to the discussion. “There’s such a negative conversation happening around social media, and there is good reason for that. However, it’s important to realize there can be benefits for many teens,” said Jacqueline Nesi, PhD, an assistant professor of psychology at Brown University who studies technology use in youth, and a member of the APA panel that produced the health advisory. “Teens (and adults) obviously get something out of social media. We have to take a balanced view if we want to reach teens and help them use these platforms in healthier ways.”

[ Related: What parents should know to keep their teens safe on social media ]

In 2023, an estimated 4.9 billion people worldwide are expected to use social media. For teens who grew up with technology, those digital platforms are woven into the fabric of their lives. “Social media is here to stay,” said Mary Alvord, PhD, a clinical psychologist in Maryland and adjunct professor at George Washington University, and a member of the APA panel. That doesn’t mean we have to accept its dangers, however. “Just as we decide when kids are old enough to drive, and we teach them to be good drivers, we can establish guidelines and teach children to use social media safely,” Alvord said.

Social media charms and harms

Even before the COVID-19 pandemic, rates of depression, anxiety, and suicide in young people were climbing. In 2021, more than 40% of high school students reported depressive symptoms, with girls and LGBTQ+ youth reporting even higher rates of poor mental health and suicidal thoughts, according to data from the U.S. Centers for Disease Control and Prevention ( American Economic Review , Vol. 112, No. 11, 2022 ).

Young people may be particularly vulnerable to social media’s charms—as well as its harms. During adolescent development, brain regions associated with the desire for attention, feedback, and reinforcement from peers become more sensitive. Meanwhile, the brain regions involved in self-control have not fully matured. That can be a recipe for disaster. “The need to prioritize peers is a normal part of adolescent development, and youth are turning to social media for some of that longed-for peer contact,” said clinical psychologist Mary Ann McCabe, PhD, ABPP, a member-at-large of APA’s Board of Directors, adjunct associate professor of pediatrics at George Washington University School of Medicine, and cochair of the expert advisory panel. “The original yearning is social, but kids can accidentally wander into harmful content.”

[ Related: Potential risks of content, features, and functions: The science of how social media affects youth ]

The potential risks of social media may be especially acute during early adolescence when puberty delivers an onslaught of biological, psychological, and social changes. One longitudinal analysis of data from youth in the United Kingdom found distinct developmental windows during which adolescents are especially sensitive to social media’s impact. During those windows—around 11 to 13 for girls and 14 to 15 for boys—more social media use predicts a decrease in life satisfaction a year later, while lower use predicts greater life satisfaction ( Orben, A., et al., Nature Communications , Vol. 13, No. 1649, 2022 ).

One takeaway from such research is that adults should monitor kids’ social media use closely in early adolescence, between the ages of 10 and 14 or so. As kids become more mature and develop digital literacy skills, they can earn more autonomy.

The cost of connection

The internet is at its best when it brings people together. Adults can help kids get the most out of social media by encouraging them to use online platforms to engage with others in positive ways. “The primary benefit is social connection, and that’s true for teens who are connecting with friends they already have or making new connections,” Nesi said. “On social media, they can find people who share their identities and interests.”

Online social interaction can promote healthy socialization among teens, especially when they’re experiencing stress or social isolation. For youth who have anxiety or struggle in social situations, practicing conversations over social media can be an important step toward feeling more comfortable interacting with peers in person. Social media can also help kids stay in touch with their support networks. That can be especially important for kids from marginalized groups, such as LGBTQ+ adolescents who may be reluctant or unable to discuss their identity with caregivers ( Craig, S. L., et al., Social Media + Society , Vol. 7, No. 1, 2021 ). In such cases, online support can be a lifeline.

“We know from suicide prevention research that it’s critical for people to know they aren’t alone,” Alvord said.

Kids also learn about themselves online. “Social media provides a lot of opportunities for young people to discover new information, learn about current events, engage with issues, and have their voices heard,” Nesi added. “And it gives them an opportunity to explore their identities, which is an important task of the adolescent years.”

Yet all those opportunities come at a cost. “There is a lot of good that can come from social media. The problem is, the algorithms can also lead you down rabbit holes,” Alvord said. Technology is expertly designed to pull us in. Features such as “like” buttons, notifications, and videos that start playing automatically make it incredibly hard to step away. At the extreme, social media use can interfere with sleep, physical activity, schoolwork, and in-person social interactions. “The risk of technologies that pull us in is that they can get in the way of all the things we know are important for a teen’s development,” Nesi said.

Research suggests that setting limits and boundaries around social media, combined with discussion and coaching from adults, is the best way to promote positive outcomes for youth ( Wachs, S., et al., Computers & Education , Vol. 160, No. 1, 2021 ). Parents should talk to kids often about social media and technology and also use strategies like limiting the amount of time kids can use devices and removing devices from the bedroom at night. Caregivers should also keep an eye out for problematic behaviors, such as strong cravings to use social media, an inability to stop, and lying or sneaking around in order to use devices when they aren’t allowed.

[ Related: How much is too much social media use: A Q&A with Mitch Prinstein, PhD ]

In helping to set boundaries around social media, it’s important that parents don’t simply limit access to devices, Alvord added. “Removing devices can feel punitive. Instead, parents should focus on encouraging kids to spend time with other activities they find valuable, such as movement and art activities they enjoy,” she said. “When kids are spending more time on those things, they’re less likely to be stuck on social media.”

Dangerous content

Spending too much time on social media is one cause for concern. Dangerous content is another. Despite efforts by caregivers and tech companies to protect kids from problematic material, they still encounter plenty of it online—including mis- and disinformation, racism and hate speech, and content that promotes dangerous behaviors such as disordered eating and self-harm.

During the first year of the pandemic, when kids were spending more time at home and online, McCabe saw a flurry of new diagnoses of eating disorders in her teen patients and their friends. “These kids often reported that they started by watching something relatively benign, like exercise videos,” she said. But their social media algorithms doubled down on that content, offering up more and more material related to body image and weight. “It was an echo chamber,” McCabe added. “And several of my patients attributed their eating disorders to this online behavior.”

Unfortunately, McCabe’s observations seem to be part of a common pattern. A large body of research, cited in APA’s health advisory, suggests that using social media for comparisons and feedback related to physical appearance is linked to poorer body image, disordered eating, and depressive symptoms, especially among girls.

Other research shows that when youth are exposed to unsafe behaviors online, such as substance use or self-harm, they may be at greater risk of engaging in similar behaviors themselves. In a longitudinal study of high school students, Nesi and colleagues showed that kids who saw their peers drinking alcohol on social media were more likely to start drinking and to binge drink 1 year later, even after controlling for demographic and developmental risk factors ( Journal of Adolescent Health , Vol. 60, No. 6, 2017 ).

Cyberbullying is another source of worry, both for young people and their caregivers. Indeed, research shows that online bullying and harassment can be harmful for a young person’s psychological well-being. APA’s health advisory cited several studies that found online bullying and harassment can be more severe than offline bullying. The research showed it can increase the risk of mental health problems in adolescents—with risks for both perpetrators and victims of cyberhate.

Ingrained racism

Search engines and social media algorithms can expose adolescents to other types of cyberhate, including racism. In fact, online algorithms often have structural racism and bias baked in, in ways that White users might not even notice. Sometimes, the algorithms themselves churn out biased or racist content. TikTok, for instance, has come under fire for recommending new accounts based on the appearance of the people a user already follows—with the inadvertent effect of segregating the platform. In addition to this form of “algorithmic bias,” people of color are frequently subjected to what some researchers call “filter bias.” In one common example, the beauty filters built into sites like Instagram or Snapchat might apply paler skin or more typically White facial features to a user’s selfies.

Like microaggressions in offline life, online racism in the form of algorithmic and filter bias can take a toll on mental health, said Brendesha Tynes, PhD, a professor of education and psychology at the University of Southern California, and a member of the APA advisory panel. In an ongoing daily diary study with adolescents, she is finding evidence that people who are exposed to algorithmic and filter bias are at increased risk of next-day depression and anxiety symptoms.

“I’m an adult who studies these issues and who has a lot of strategies to protect myself, and it can still be really hard” to cope with online racism, she said. Impressionable teens who haven’t learned such strategies are likely to experience even greater psychological impacts from the racism they encounter every day on social media. “We’re just beginning to understand the profound negative impacts of online racism,” Tynes said. “We need all hands on deck in supporting kids of color and helping them cope with these experiences.”

Despite the drawbacks of technology, there is a silver lining. Tynes has found Black youth receive valuable social support from other Black people on social media. Those interactions can help them learn to think critically about the racism they encounter. That’s important, since her research also shows that youth who are able to critique racism experience less psychological distress when they witness race-related traumatic events online ( Journal of Adolescent Health , Vol. 43, No. 6, 2008 ).

Tynes said more research is needed to understand how online racism affects youth and how best to protect them from its harms.

“Different groups have vastly different experiences online,” she said. “We need more detailed recommendations for specific groups.”

A role for psychology

How to protect kids from online racism is just one of a long list of questions on researchers’ wish lists. Digital technologies evolve so quickly that kids are off to a new platform before scientists can finish collecting data about yesterday’s favorite sites. “There’s so much we still don’t know about this topic. That’s understandably frustrating for people because social media is impacting people’s lives as we speak,” Nesi said.

It’s likely some groups, and some individuals, are more susceptible than others to the negative effects of social media, she added. “We need more information about who is more vulnerable and who is more resilient, and what it is they’re doing online that’s healthy versus harmful.”

While there is a lot of work to be done, Nesi said, “we’re getting closer.” As APA’s recommendations make clear, there is ample evidence some types of content and online behaviors can harm youth. Adult role models can work together with teens to understand the pitfalls of technology and establish boundaries to protect them from dangerous content and excessive screen time.

Psychological research shows children from a young age should be taught digital literacy skills such as identifying misinformation, protecting privacy, understanding how people can misrepresent themselves online, and how to critically evaluate race-related materials online. One way to promote those skills may be to lean into teens’ inherent skepticism of grown-ups. “You can teach kids that a lot of people want something from them,” Alvord said—whether it’s a stranger trying to message them on Instagram, or TikTok earning money by collecting their data or showing them branded content.

That’s not to say it’s easy to help kids develop a healthy relationship with social media. “By necessity, adolescents disagree more with their parents—and they are formidable when they insist on having something, like phones or social media, that all their friends have,” McCabe said. “But parents are eager for guidance. There is an appetite for this information now,” she added—and psychological scientists can help provide it.

That scientific research can inform broader efforts to keep children safe on social media as well. “Parents can’t do this alone,” Nesi said. “We need larger-scale changes to these platforms to protect kids.”

There are efforts to make such changes. The Kids Online Safety Act, a bipartisan bill introduced in April, establishes a duty of care for social media companies to protect minors from mental health harms, sex trafficking, narcotics, and other dangers. Additionally, the bill requires social media companies to go through independent, external audits, allows researcher access to platform data assets, and creates substantial youth and parental controls to create a safer digital environment. Even as legislators and tech companies consider those and other policies, researchers can continue their efforts to determine which actions might be most protective, said Nesi, who is currently leading a study to understand which features of social media are helpful versus harmful for kids at high risk of suicide. “For some kids, being able to connect with others and find support is really important. For others, social media may create more challenges than it solves,” Nesi said. “The key is making sure we don’t accidentally do any harm” by enacting restrictions and legislation that are not backed by science.

While researchers forge ahead, clinical psychologists, too, can add valuable insight for teens and their families. “Screens are a central part of adolescents’ lives, and that needs to be integrated into assessment and treatment,” Nesi said. “Clinicians can help families and teens take a step back and look at their social media use to figure out what’s working for them and what isn’t.”

Someday, McCabe said, digital literacy may be taught in schools the same way that youth learn about sexual health and substance use. “I hope we’ll come to a point where teaching about the healthy use of social media is an everyday occurrence,” she said. “Because of this dialogue that we’re having now among families and policymakers, we may see a new generation of kids whose entry into the digital world is very different, where we can use social media for connection and education but minimize the harms,” she added. “I hope this is the beginning of a new day.”

Social media recommendations

APA’s Health Advisory on Social Media Use in Adolescence makes these recommendations based on the scientific evidence to date:

Youth using social media should be encouraged to use functions that create opportunities for social support, online companionship, and emotional intimacy that can promote healthy socialization.
Social media use, functionality, and permissions/consenting should be tailored to youths’ developmental capabilities; designs created for adults may not be appropriate for children.
In early adolescence (i.e., typically 10–14 years), adult monitoring (i.e., ongoing review, discussion, and coaching around social media content) is advised for most youths’ social media use; autonomy may increase gradually as kids age and if they gain digital literacy skills. However, monitoring should be balanced with youths’ appropriate needs for privacy.
To reduce the risks of psychological harm, adolescents’ exposure to content on social media that depicts illegal or psychologically maladaptive behavior, including content that instructs or encourages youth to engage in health-risk behaviors, such as self-harm (e.g., cutting, suicide), harm to others, or those that encourage eating-disordered behavior (e.g., restrictive eating, purging, excessive exercise) should be minimized, reported, and removed; moreover, technology should not drive users to this content.
To minimize psychological harm, adolescents’ exposure to “cyberhate” including online discrimination, prejudice, hate, or cyberbullying especially directed toward a marginalized group (e.g., racial, ethnic, gender, sexual, religious, ability status), or toward an individual because of their identity or allyship with a marginalized group should be minimized.
Adolescents should be routinely screened for signs of “problematic social media use” that can impair their ability to engage in daily roles and routines, and may present risk for more serious psychological harms over time.
The use of social media should be limited so as to not interfere with adolescents’ sleep and physical activity.
Adolescents should limit use of social media for social comparison, particularly around beauty- or appearance-related content.
Adolescents’ social media use should be preceded by training in social media literacy to ensure that users have developed psychologically-informed competencies and skills that will maximize the chances for balanced, safe, and meaningful social media use.
Substantial resources should be provided for continued scientific examination of the positive and negative effects of social media on adolescent development.

Read the full recommendations and see the science behind them .

Six Things Psychologists are Talking About

The APA Monitor on Psychology ® sister e-newsletter offers fresh articles on psychology trends, new research, and more.

Welcome! Thank you for subscribing.

Speaking of Psychology

Subscribe to APA’s audio podcast series highlighting some of the most important and relevant psychological research being conducted today.

Subscribe to Speaking of Psychology and download via:

Contact APA

Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice

Published: 20 April 2020
Volume 5 , pages 245–257, ( 2020 )

Cite this article

John A. Naslund 1 ,
Ameya Bondre 2 ,
John Torous 3 &
Kelly A. Aschbrenner 4

409k Accesses

223 Citations

186 Altmetric

19 Mentions

Explore all metrics

Avoid common mistakes on your manuscript.

Introduction

Social media has become a prominent fixture in the lives of many individuals facing the challenges of mental illness. Social media refers broadly to web and mobile platforms that allow individuals to connect with others within a virtual network (such as Facebook, Twitter, Instagram, Snapchat, or LinkedIn), where they can share, co-create, or exchange various forms of digital content, including information, messages, photos, or videos (Ahmed et al. 2019 ). Studies have reported that individuals living with a range of mental disorders, including depression, psychotic disorders, or other severe mental illnesses, use social media platforms at comparable rates as the general population, with use ranging from about 70% among middle-age and older individuals to upwards of 97% among younger individuals (Aschbrenner et al. 2018b ; Birnbaum et al. 2017b ; Brunette et al. 2019 ; Naslund et al. 2016 ). Other exploratory studies have found that many of these individuals with mental illness appear to turn to social media to share their personal experiences, seek information about their mental health and treatment options, and give and receive support from others facing similar mental health challenges (Bucci et al. 2019 ; Naslund et al. 2016b ).

Across the USA and globally, very few people living with mental illness have access to adequate mental health services (Patel et al. 2018 ). The wide reach and near ubiquitous use of social media platforms may afford novel opportunities to address these shortfalls in existing mental health care, by enhancing the quality, availability, and reach of services. Recent studies have explored patterns of social media use, impact of social media use on mental health and wellbeing, and the potential to leverage the popularity and interactive features of social media to enhance the delivery of interventions. However, there remains uncertainty regarding the risks and potential harms of social media for mental health (Orben and Przybylski 2019 ) and how best to weigh these concerns against potential benefits.

In this commentary, we summarized current research on the use of social media among individuals with mental illness, with consideration of the impact of social media on mental wellbeing, as well as early efforts using social media for delivery of evidence-based programs for addressing mental health problems. We searched for recent peer reviewed publications in Medline and Google Scholar using the search terms “mental health” or “mental illness” and “social media,” and searched the reference lists of recent reviews and other relevant studies. We reviewed the risks, potential harms, and necessary safety precautions with using social media for mental health. Overall, our goal was to consider the role of social media as a potentially viable intervention platform for offering support to persons with mental disorders, promoting engagement and retention in care, and enhancing existing mental health services, while balancing the need for safety. Given this broad objective, we did not perform a systematic search of the literature and we did not apply specific inclusion criteria based on study design or type of mental disorder.

Social Media Use and Mental Health

In 2020, there are an estimated 3.8 billion social media users worldwide, representing half the global population (We Are Social 2020 ). Recent studies have shown that individuals with mental disorders are increasingly gaining access to and using mobile devices, such as smartphones (Firth et al. 2015 ; Glick et al. 2016 ; Torous et al. 2014a , b ). Similarly, there is mounting evidence showing high rates of social media use among individuals with mental disorders, including studies looking at engagement with these popular platforms across diverse settings and disorder types. Initial studies from 2015 found that nearly half of a sample of psychiatric patients were social media users, with greater use among younger individuals (Trefflich et al. 2015 ), while 47% of inpatients and outpatients with schizophrenia reported using social media, of which 79% reported at least once-a-week usage of social media websites (Miller et al. 2015 ). Rates of social media use among psychiatric populations have increased in recent years, as reflected in a study with data from 2017 showing comparable rates of social media use (approximately 70%) among individuals with serious mental illness in treatment as compared with low-income groups from the general population (Brunette et al. 2019 ).

Similarly, among individuals with serious mental illness receiving community-based mental health services, a recent study found equivalent rates of social media use as the general population, even exceeding 70% of participants (Naslund et al. 2016 ). Comparable findings were demonstrated among middle-age and older individuals with mental illness accessing services at peer support agencies, where 72% of respondents reported using social media (Aschbrenner et al. 2018b ). Similar results, with 68% of those with first episode psychosis using social media daily were reported in another study (Abdel-Baki et al. 2017 ).

Individuals who self-identified as having a schizophrenia spectrum disorder responded to a survey shared through the National Alliance of Mental Illness (NAMI) and reported that visiting social media sites was one of their most common activities when using digital devices, taking up roughly 2 h each day (Gay et al. 2016 ). For adolescents and young adults ages 12 to 21 with psychotic disorders and mood disorders, over 97% reported using social media, with average use exceeding 2.5 h per day (Birnbaum et al. 2017b ). Similarly, in a sample of adolescents ages 13–18 recruited from community mental health centers, 98% reported using social media, with YouTube as the most popular platform, followed by Instagram and Snapchat (Aschbrenner et al. 2019 ).

Research has also explored the motivations for using social media as well as the perceived benefits of interacting on these platforms among individuals with mental illness. In the sections that follow (see Table 1 for a summary), we consider three potentially unique features of interacting and connecting with others on social media that may offer benefits for individuals living with mental illness. These include: (1) Facilitate social interaction; (2) Access to a peer support network; and (3) Promote engagement and retention in services.

Facilitate Social Interaction

Social media platforms offer near continuous opportunities to connect and interact with others, regardless of time of day or geographic location. This on demand ease of communication may be especially important for facilitating social interaction among individuals with mental disorders experiencing difficulties interacting in face-to-face settings. For example, impaired social functioning is a common deficit in schizophrenia spectrum disorders, and social media may facilitate communication and interacting with others for these individuals (Torous and Keshavan 2016 ). This was suggested in one study where participants with schizophrenia indicated that social media helped them to interact and socialize more easily (Miller et al. 2015 ). Like other online communication, the ability to connect with others anonymously may be an important feature of social media, especially for individuals living with highly stigmatizing health conditions (Berger et al. 2005 ), such as serious mental disorders (Highton-Williamson et al. 2015 ).

Studies have found that individuals with serious mental disorders (Spinzy et al. 2012 ) as well as young adults with mental illness (Gowen et al. 2012 ) appear to form online relationships and connect with others on social media as often as social media users from the general population. This is an important observation because individuals living with serious mental disorders typically have few social contacts in the offline world and also experience high rates of loneliness (Badcock et al. 2015 ; Giacco et al. 2016 ). Among individuals receiving publicly funded mental health services who use social media, nearly half (47%) reported using these platforms at least weekly to feel less alone (Brusilovskiy et al. 2016 ). In another study of young adults with serious mental illness, most indicated that they used social media to help feel less isolated (Gowen et al. 2012 ). Interestingly, more frequent use of social media among a sample of individuals with serious mental illness was associated with greater community participation, measured as participation in shopping, work, religious activities, or visiting friends and family, as well as greater civic engagement, reflected as voting in local elections (Brusilovskiy et al. 2016 ).

Emerging research also shows that young people with moderate to severe depressive symptoms appear to prefer communicating on social media rather than in-person (Rideout and Fox 2018 ), while other studies have found that some individuals may prefer to seek help for mental health concerns online rather than through in-person encounters (Batterham and Calear 2017 ). In a qualitative study, participants with schizophrenia described greater anonymity, the ability to discover that other people have experienced similar health challenges and reducing fears through greater access to information as important motivations for using the Internet to seek mental health information (Schrank et al. 2010 ). Because social media does not require the immediate responses necessary in face-to-face communication, it may overcome deficits with social interaction due to psychotic symptoms that typically adversely affect face-to-face conversations (Docherty et al. 1996 ). Online social interactions may not require the use of non-verbal cues, particularly in the initial stages of interaction (Kiesler et al. 1984 ), with interactions being more fluid and within the control of users, thereby overcoming possible social anxieties linked to in-person interaction (Indian and Grieve 2014 ). Furthermore, many individuals with serious mental disorders can experience symptoms including passive social withdrawal, blunted affect, and attentional impairment, as well as active social avoidance due to hallucinations or other concerns (Hansen et al. 2009 ), thus potentially reinforcing the relative advantage, as perceived by users, of using social media over in person conversations.

Access to a Peer Support Network

There is growing recognition about the role that social media channels could play in enabling peer support (Bucci et al. 2019 ; Naslund et al. 2016b ), referred to as a system of mutual giving and receiving where individuals who have endured the difficulties of mental illness can offer hope, friendship, and support to others facing similar challenges (Davidson et al. 2006 ; Mead et al. 2001 ). Initial studies exploring use of online self-help forums among individuals with serious mental illnesses have found that individuals with schizophrenia appeared to use these forums for self-disclosure and sharing personal experiences, in addition to providing or requesting information, describing symptoms, or discussing medication (Haker et al. 2005 ), while users with bipolar disorder reported using these forums to ask for help from others about their illness (Vayreda and Antaki 2009 ). More recently, in a review of online social networking in people with psychosis, Highton-Williamson et al. ( 2015 ) highlight that an important purpose of such online connections was to establish new friendships, pursue romantic relationships, maintain existing relationships or reconnect with people, and seek online peer support from others with lived experience (Highton-Williamson et al. 2015 ).

Online peer support among individuals with mental illness has been further elaborated in various studies. In a content analysis of comments posted to YouTube by individuals who self-identified as having a serious mental illness, there appeared to be opportunities to feel less alone, provide hope, find support and learn through mutual reciprocity, and share coping strategies for day-to-day challenges of living with a mental illness (Naslund et al. 2014 ). In another study, Chang ( 2009 ) delineated various communication patterns in an online psychosis peer-support group (Chang 2009 ). Specifically, different forms of support emerged, including “informational support” about medication use or contacting mental health providers, “esteem support” involving positive comments for encouragement, “network support” for sharing similar experiences, and “emotional support” to express understanding of a peer’s situation and offer hope or confidence (Chang 2009 ). Bauer et al. ( 2013 ) reported that the main interest in online self-help forums for patients with bipolar disorder was to share emotions with others, allow exchange of information, and benefit by being part of an online social group (Bauer et al. 2013 ).

For individuals who openly discuss mental health problems on Twitter, a study by Berry et al. ( 2017 ) found that this served as an important opportunity to seek support and to hear about the experiences of others (Berry et al. 2017 ). In a survey of social media users with mental illness, respondents reported that sharing personal experiences about living with mental illness and opportunities to learn about strategies for coping with mental illness from others were important reasons for using social media (Naslund et al. 2017 ). A computational study of mental health awareness campaigns on Twitter provides further support with inspirational posts and tips being the most shared (Saha et al. 2019 ). Taken together, these studies offer insights about the potential for social media to facilitate access to an informal peer support network, though more research is necessary to examine how these online interactions may impact intentions to seek care, illness self-management, and clinically meaningful outcomes in offline contexts.

Promote Engagement and Retention in Services

Many individuals living with mental disorders have expressed interest in using social media platforms for seeking mental health information (Lal et al. 2018 ), connecting with mental health providers (Birnbaum et al. 2017b ), and accessing evidence-based mental health services delivered over social media specifically for coping with mental health symptoms or for promoting overall health and wellbeing (Naslund et al. 2017 ). With the widespread use of social media among individuals living with mental illness combined with the potential to facilitate social interaction and connect with supportive peers, as summarized above, it may be possible to leverage the popular features of social media to enhance existing mental health programs and services. A recent review by Biagianti et al. ( 2018 ) found that peer-to-peer support appeared to offer feasible and acceptable ways to augment digital mental health interventions for individuals with psychotic disorders by specifically improving engagement, compliance, and adherence to the interventions and may also improve perceived social support (Biagianti et al. 2018 ).

Among digital programs that have incorporated peer-to-peer social networking consistent with popular features on social media platforms, a pilot study of the HORYZONS online psychosocial intervention demonstrated significant reductions in depression among patients with first episode psychosis (Alvarez-Jimenez et al. 2013 ). Importantly, the majority of participants (95%) in this study engaged with the peer-to-peer networking feature of the program, with many reporting increases in perceived social connectedness and empowerment in their recovery process (Alvarez-Jimenez et al. 2013 ). This moderated online social therapy program is now being evaluated as part of a large randomized controlled trial for maintaining treatment effects from first episode psychosis services (Alvarez-Jimenez et al. 2019 ).

Other early efforts have demonstrated that use of digital environments with the interactive peer-to-peer features of social media can enhance social functioning and wellbeing in young people at high risk of psychosis (Alvarez-Jimenez et al. 2018 ). There has also been a recent emergence of several mobile apps to support symptom monitoring and relapse prevention in psychotic disorders. Among these apps, the development of PRIME (Personalized Real-time Intervention for Motivational Enhancement) has involved working closely with young people with schizophrenia to ensure that the design of the app has the look and feel of mainstream social media platforms, as opposed to existing clinical tools (Schlosser et al. 2016 ). This unique approach to the design of the app is aimed at promoting engagement and ensuring that the app can effectively improve motivation and functioning through goal setting and promoting better quality of life of users with schizophrenia (Schlosser et al. 2018 ).

Social media platforms could also be used to promote engagement and participation in in-person services delivered through community mental health settings. For example, the peer-based lifestyle intervention called PeerFIT targets weight loss and improved fitness among individuals living with serious mental illness through a combination of in-person lifestyle classes, exercise groups, and use of digital technologies (Aschbrenner et al. 2016b , c ). The intervention holds tremendous promise as lack of support is one of the largest barriers towards exercise in patients with serious mental illness (Firth et al. 2016 ), and it is now possible to use social media to counter such. Specifically, in PeerFIT, a private Facebook group is closely integrated into the program to offer a closed platform where participants can connect with the lifestyle coaches, access intervention content, and support or encourage each other as they work towards their lifestyle goals (Aschbrenner et al. 2016a ; Naslund et al. 2016a ). To date, this program has demonstrated preliminary effectiveness for meaningfully reducing cardiovascular risk factors that contribute to early mortality in this patient group (Aschbrenner, Naslund, Shevenell, Kinney, et al., 2016), while the Facebook component appears to have increased engagement in the program, while allowing participants who were unable to attend in-person sessions due to other health concerns or competing demands to remain connected with the program (Naslund et al. 2018 ). This lifestyle intervention is currently being evaluated in a randomized controlled trial enrolling young adults with serious mental illness from real world community mental health services settings (Aschbrenner et al. 2018a ).

These examples highlight the promise of incorporating the features of popular social media into existing programs, which may offer opportunities to safely promote engagement and program retention, while achieving improved clinical outcomes. This is an emerging area of research, as evidenced by several important effectiveness trials underway (Alvarez-Jimenez et al. 2019 ; Aschbrenner et al. 2018a ), including efforts to leverage online social networking to support family caregivers of individuals receiving first episode psychosis services (Gleeson et al. 2017 ).

Challenges with Social Media for Mental Health

The science on the role of social media for engaging persons with mental disorders needs a cautionary note on the effects of social media usage on mental health and wellbeing, particularly in adolescents and young adults. While the risks and harms of social media are frequently covered in the popular press and mainstream news reports, careful consideration of the research in this area is necessary. In a review of 43 studies in young people, many benefits of social media were cited, including increased self-esteem and opportunities for self-disclosure (Best et al. 2014 ). Yet, reported negative effects were an increased exposure to harm, social isolation, depressive symptoms, and bullying (Best et al. 2014 ). In the sections that follow (see Table 1 for a summary), we consider three major categories of risk related to use of social media and mental health. These include: (1) Impact on symptoms; (2) Facing hostile interactions; and (3) Consequences for daily life.

Impact on Symptoms

Studies consistently highlight that use of social media, especially heavy use and prolonged time spent on social media platforms, appears to contribute to increased risk for a variety of mental health symptoms and poor wellbeing, especially among young people (Andreassen et al. 2016 ; Kross et al. 2013 ; Woods and Scott 2016 ). This may partly be driven by the detrimental effects of screen time on mental health, including increased severity of anxiety and depressive symptoms, which have been well documented (Stiglic and Viner 2019 ). Recent studies have reported negative effects of social media use on mental health of young people, including social comparison pressure with others and greater feeling of social isolation after being rejected by others on social media (Rideout and Fox 2018 ). In a study of young adults, it was found that negative comparisons with others on Facebook contributed to risk of rumination and subsequent increases in depression symptoms (Feinstein et al. 2013 ). Still, the cross-sectional nature of many screen time and mental health studies makes it challenging to reach causal inferences (Orben and Przybylski 2019 ).

Quantity of social media use is also an important factor, as highlighted in a survey of young adults ages 19 to 32, where more frequent visits to social media platforms each week were correlated with greater depressive symptoms (Lin et al. 2016 ). More time spent using social media is also associated with greater symptoms of anxiety (Vannucci et al. 2017 ). The actual number of platforms accessed also appears to contribute to risk as reflected in another national survey of young adults where use of a large number of social media platforms was associated with negative impact on mental health (Primack et al. 2017 ). Among survey respondents using between 7 and 11 different social media platforms compared with respondents using only 2 or fewer platforms, there were 3 times greater odds of having high levels of depressive symptoms and a 3.2 times greater odds of having high levels of anxiety symptoms (Primack et al. 2017 ).

Many researchers have postulated that worsening mental health attributed to social media use may be because social media replaces face-to-face interactions for young people (Twenge and Campbell 2018 ) and may contribute to greater loneliness (Bucci et al. 2019 ) and negative effects on other aspects of health and wellbeing (Woods and Scott 2016 ). One nationally representative survey of US adolescents found that among respondents who reported more time accessing media such as social media platforms or smartphone devices, there were significantly greater depressive symptoms and increased risk of suicide when compared with adolescents who reported spending more time on non-screen activities, such as in-person social interaction or sports and recreation activities (Twenge et al. 2018 ). For individuals living with more severe mental illnesses, the effects of social media on psychiatric symptoms have received less attention. One study found that participation in chat rooms may contribute to worsening symptoms in young people with psychotic disorders (Mittal et al. 2007 ), while another study of patients with psychosis found that social media use appeared to predict low mood (Berry et al. 2018 ). These studies highlight a clear relationship between social media use and mental health that may not be present in general population studies (Orben and Przybylski 2019 ) and emphasize the need to explore how social media may contribute to symptom severity and whether protective factors may be identified to mitigate these risks.

Facing Hostile Interactions

Popular social media platforms can create potential situations where individuals may be victimized by negative comments or posts. Cyberbullying represents a form of online aggression directed towards specific individuals, such as peers or acquaintances, which is perceived to be most harmful when compared with random hostile comments posted online (Hamm et al. 2015 ). Importantly, cyberbullying on social media consistently shows harmful impact on mental health in the form of increased depressive symptoms as well as worsening of anxiety symptoms, as evidenced in a review of 36 studies among children and young people (Hamm et al. 2015 ). Furthermore, cyberbullying disproportionately impacts females as reflected in a national survey of adolescents in the USA, where females were twice as likely to be victims of cyberbullying compared with males (Alhajji et al. 2019 ). Most studies report cross-sectional associations between cyberbullying and symptoms of depression or anxiety (Hamm et al. 2015 ), though one longitudinal study in Switzerland found that cyberbullying contributed to significantly greater depression over time (Machmutow et al. 2012 ).

For youth ages 10 to 17 who reported major depressive symptomatology, there were over 3 times greater odds of facing online harassment in the last year compared with youth who reported mild or no depressive symptoms (Ybarra 2004 ). Similarly, in a 2018 national survey of young people, respondents ages 14 to 22 with moderate to severe depressive symptoms were more likely to have had negative experiences when using social media and, in particular, were more likely to report having faced hostile comments or being “trolled” from others when compared with respondents without depressive symptoms (31% vs. 14%) (Rideout and Fox 2018 ). As these studies depict risks for victimization on social media and the correlation with poor mental health, it is possible that individuals living with mental illness may also experience greater hostility online compared to individuals without mental illness. This would be consistent with research showing greater risk of hostility, including increased violence and discrimination, directed towards individuals living with mental illness in in-person contexts, especially targeted at those with severe mental illnesses (Goodman et al. 1999 ).

A computational study of mental health awareness campaigns on Twitter reported that while stigmatizing content was rare, it was actually the most spread (re-tweeted) demonstrating that harmful content can travel quickly on social media (Saha et al. 2019 ). Another study was able to map the spread of social media posts about the Blue Whale Challenge, an alleged game promoting suicide, over Twitter, YouTube, Reddit, Tumblr, and other forums across 127 countries (Sumner et al. 2019 ). These findings show that it is critical to monitor the actual content of social media posts, such as determining whether content is hostile or promotes harm to self or others. This is pertinent because existing research looking at duration of exposure cannot account for the impact of specific types of content on mental health and is insufficient to fully understand the effects of using these platforms on mental health.

Consequences for Daily Life

The ways in which individuals use social media can also impact their offline relationships and everyday activities. To date, reports have described risks of social media use pertaining to privacy, confidentiality, and unintended consequences of disclosing personal health information online (Torous and Keshavan 2016 ). Additionally, concerns have been raised about poor quality or misleading health information shared on social media and that social media users may not be aware of misleading information or conflicts of interest especially when the platforms promote popular content regardless of whether it is from a trustworthy source (Moorhead et al. 2013 ; Ventola 2014 ). For persons living with mental illness, there may be additional risks from using social media. A recent study that specifically explored the perspectives of social media users with serious mental illnesses, including participants with schizophrenia spectrum disorders, bipolar disorder, or major depression, found that over one third of participants expressed concerns about privacy when using social media (Naslund and Aschbrenner 2019 ). The reported risks of social media use were directly related to many aspects of everyday life, including concerns about threats to employment, fear of stigma and being judged, impact on personal relationships, and facing hostility or being hurt (Naslund and Aschbrenner 2019 ). While few studies have specifically explored the dangers of social media use from the perspectives of individuals living with mental illness, it is important to recognize that use of these platforms may contribute to risks that extend beyond worsening symptoms and that can affect different aspects of daily life.

In this commentary, we considered ways in which social media may yield benefits for individuals living with mental illness, while contrasting these with the possible harms. Studies reporting on the threats of social media for individuals with mental illness are mostly cross-sectional, making it difficult to draw conclusions about direction of causation. However, the risks are potentially serious. These risks should be carefully considered in discussions pertaining to use of social media and the broader use of digital mental health technologies, as avenues for mental health promotion or for supporting access to evidence-based programs or mental health services. At this point, it would be premature to view the benefits of social media as outweighing the possible harms, when it is clear from the studies summarized here that social media use can have negative effects on mental health symptoms, can potentially expose individuals to hurtful content and hostile interactions, and can result in serious consequences for daily life, including threats to employment and personal relationships. Despite these risks, it is also necessary to recognize that individuals with mental illness will continue to use social media given the ease of accessing these platforms and the immense popularity of online social networking. With this in mind, it may be ideal to raise awareness about these possible risks so that individuals can implement necessary safeguards, while highlighting that there could also be benefits. Being aware of the risks is an essential first step, before then recognizing that use of these popular platforms could contribute to some benefits like finding meaningful interactions with others, engaging with peer support networks, and accessing information and services.

To capitalize on the widespread use of social media and to achieve the promise that these platforms may hold for supporting the delivery of targeted mental health interventions, there is need for continued research to better understand how individuals living with mental illness use social media. Such efforts could inform safety measures and also encourage use of social media in ways that maximize potential benefits while minimizing risk of harm. It will be important to recognize how gender and race contribute to differences in use of social media for seeking mental health information or accessing interventions, as well as differences in how social media might impact mental wellbeing. For example, a national survey of 14- to 22-year olds in the USA found that female respondents were more likely to search online for information about depression or anxiety and to try to connect with other people online who share similar mental health concerns when compared with male respondents (Rideout and Fox 2018 ). In the same survey, there did not appear to be any differences between racial or ethnic groups in social media use for seeking mental health information (Rideout and Fox 2018 ). Social media use also appears to have a differential impact on mental health and emotional wellbeing between females and males (Booker et al. 2018 ), highlighting the need to explore unique experiences between gender groups to inform tailored programs and services. Research shows that lesbian, gay, bisexual, or transgender individuals frequently use social media for searching for health information and may be more likely compared with heterosexual individuals to share their own personal health experiences with others online (Rideout and Fox 2018 ). Less is known about use of social media for seeking support for mental health concerns among gender minorities, though this is an important area for further investigation as these individuals are more likely to experience mental health problems and online victimization when compared with heterosexual individuals (Mereish et al. 2019 ).

Similarly, efforts are needed to explore the relationship between social media use and mental health among ethnic and racial minorities. A recent study found that exposure to traumatic online content on social media showing violence or hateful posts directed at racial minorities contributed to increases in psychological distress, PTSD symptoms, and depression among African American and Latinx adolescents in the USA (Tynes et al. 2019 ). These concerns are contrasted by growing interest in the potential for new technologies including social media to expand the reach of services to underrepresented minority groups (Schueller et al. 2019 ). Therefore, greater attention is needed to understanding the perspectives of ethnic and racial minorities to inform effective and safe use of social media for mental health promotion efforts.

Research has found that individuals living with mental illness have expressed interest in accessing mental health services through social media platforms. A survey of social media users with mental illness found that most respondents were interested in accessing programs for mental health on social media targeting symptom management, health promotion, and support for communicating with health care providers and interacting with the health system (Naslund et al. 2017 ). Importantly, individuals with serious mental illness have also emphasized that any mental health intervention on social media would need to be moderated by someone with adequate training and credentials, would need to have ground rules and ways to promote safety and minimize risks, and importantly, would need to be free and easy to access.

An important strength with this commentary is that it combines a range of studies broadly covering the topic of social media and mental health. We have provided a summary of recent evidence in a rapidly advancing field with the goal of presenting unique ways that social media could offer benefits for individuals with mental illness, while also acknowledging the potentially serious risks and the need for further investigation. There are also several limitations with this commentary that warrant consideration. Importantly, as we aimed to address this broad objective, we did not conduct a systematic review of the literature. Therefore, the studies reported here are not exhaustive, and there may be additional relevant studies that were not included. Additionally, we only summarized published studies, and as a result, any reports from the private sector or websites from different organizations using social media or other apps containing social media–like features would have been omitted. Although, it is difficult to rigorously summarize work from the private sector, sometimes referred to as “gray literature,” because many of these projects are unpublished and are likely selective in their reporting of findings given the target audience may be shareholders or consumers.

Another notable limitation is that we did not assess risk of bias in the studies summarized in this commentary. We found many studies that highlighted risks associated with social media use for individuals living with mental illness; however, few studies of programs or interventions reported negative findings, suggesting the possibility that negative findings may go unpublished. This concern highlights the need for a future more rigorous review of the literature with careful consideration of bias and an accompanying quality assessment. Most of the studies that we described were from the USA, as well as from other higher income settings such as Australia or the UK. Despite the global reach of social media platforms, there is a dearth of research on the impact of these platforms on the mental health of individuals in diverse settings, as well as the ways in which social media could support mental health services in lower income countries where there is virtually no access to mental health providers. Future research is necessary to explore the opportunities and risks for social media to support mental health promotion in low-income and middle-income countries, especially as these countries face a disproportionate share of the global burden of mental disorders, yet account for the majority of social media users worldwide (Naslund et al. 2019 ).

Future Directions for Social Media and Mental Health

As we consider future research directions, the near ubiquitous social media use also yields new opportunities to study the onset and manifestation of mental health symptoms and illness severity earlier than traditional clinical assessments. There is an emerging field of research referred to as “digital phenotyping” aimed at capturing how individuals interact with their digital devices, including social media platforms, in order to study patterns of illness and identify optimal time points for intervention (Jain et al. 2015 ; Onnela and Rauch 2016 ). Given that most people access social media via mobile devices, digital phenotyping and social media are closely related (Torous et al. 2019 ). To date, the emergence of machine learning, a powerful computational method involving statistical and mathematical algorithms (Shatte et al. 2019 ), has made it possible to study large quantities of data captured from popular social media platforms such as Twitter or Instagram to illuminate various features of mental health (Manikonda and De Choudhury 2017 ; Reece et al. 2017 ). Specifically, conversations on Twitter have been analyzed to characterize the onset of depression (De Choudhury et al. 2013 ) as well as detecting users’ mood and affective states (De Choudhury et al. 2012 ), while photos posted to Instagram can yield insights for predicting depression (Reece and Danforth 2017 ). The intersection of social media and digital phenotyping will likely add new levels of context to social media use in the near future.

Several studies have also demonstrated that when compared with a control group, Twitter users with a self-disclosed diagnosis of schizophrenia show unique online communication patterns (Birnbaum et al. 2017a ), including more frequent discussion of tobacco use (Hswen et al. 2017 ), symptoms of depression and anxiety (Hswen et al. 2018b ), and suicide (Hswen et al. 2018a ). Another study found that online disclosures about mental illness appeared beneficial as reflected by fewer posts about symptoms following self-disclosure (Ernala et al. 2017 ). Each of these examples offers early insights into the potential to leverage widely available online data for better understanding the onset and course of mental illness. It is possible that social media data could be used to supplement additional digital data, such as continuous monitoring using smartphone apps or smart watches, to generate a more comprehensive “digital phenotype” to predict relapse and identify high-risk health behaviors among individuals living with mental illness (Torous et al. 2019 ).

With research increasingly showing the valuable insights that social media data can yield about mental health states, greater attention to the ethical concerns with using individual data in this way is necessary (Chancellor et al. 2019 ). For instance, data is typically captured from social media platforms without the consent or awareness of users (Bidargaddi et al. 2017 ), which is especially crucial when the data relates to a socially stigmatizing health condition such as mental illness (Guntuku et al. 2017 ). Precautions are needed to ensure that data is not made identifiable in ways that were not originally intended by the user who posted the content as this could place an individual at risk of harm or divulge sensitive health information (Webb et al. 2017 ; Williams et al. 2017 ). Promising approaches for minimizing these risks include supporting the participation of individuals with expertise in privacy, clinicians, and the target individuals with mental illness throughout the collection of data, development of predictive algorithms, and interpretation of findings (Chancellor et al. 2019 ).

In recognizing that many individuals living with mental illness use social media to search for information about their mental health, it is possible that they may also want to ask their clinicians about what they find online to check if the information is reliable and trustworthy. Alternatively, many individuals may feel embarrassed or reluctant to talk to their clinicians about using social media to find mental health information out of concerns of being judged or dismissed. Therefore, mental health clinicians may be ideally positioned to talk with their patients about using social media and offer recommendations to promote safe use of these sites while also respecting their patients’ autonomy and personal motivations for using these popular platforms. Given the gap in clinical knowledge about the impact of social media on mental health, clinicians should be aware of the many potential risks so that they can inform their patients while remaining open to the possibility that their patients may also experience benefits through use of these platforms. As awareness of these risks grows, it may be possible that new protections will be put in place by industry or through new policies that will make the social media environment safer. It is hard to estimate a number needed to treat or harm today given the nascent state of research, which means the patient and clinician need to weigh the choice on a personal level. Thus, offering education and information is an important first step in that process. As patients increasingly show interest in accessing mental health information or services through social media, it will be necessary for health systems to recognize social media as a potential avenue for reaching or offering support to patients. This aligns with growing emphasis on the need for greater integration of digital psychiatry, including apps, smartphones, or wearable devices, into patient care and clinical services through institution-wide initiatives and training clinical providers (Hilty et al. 2019 ). Within a learning healthcare environment where research and care are tightly intertwined and feedback between both is rapid, the integration of digital technologies into services may create new opportunities for advancing use of social media for mental health.

As highlighted in this commentary, social media has become an important part of the lives of many individuals living with mental disorders. Many of these individuals use social media to share their lived experiences with mental illness, to seek support from others, and to search for information about treatment recommendations, accessing mental health services and coping with symptoms (Bucci et al. 2019 ; Highton-Williamson et al. 2015 ; Naslund et al. 2016b ). As the field of digital mental health advances, the wide reach, ease of access, and popularity of social media platforms could be used to allow individuals in need of mental health services or facing challenges of mental illness to access evidence-based treatment and support. To achieve this end and to explore whether social media platforms can advance efforts to close the gap in available mental health services in the USA and globally, it will be essential for researchers to work closely with clinicians and with those affected by mental illness to ensure that possible benefits of using social media are carefully weighed against anticipated risks.

Abdel-Baki, A., Lal, S., Charron, D.-C., Stip, E., & Kara, N. (2017). Understanding access and use of technology among youth with first-episode psychosis to inform the development of technology-enabled therapeutic interventions. Early Intervention in Psychiatry, 11 (1), 72–76.

PubMed Google Scholar

Ahmed, Y. A., Ahmad, M. N., Ahmad, N., & Zakaria, N. H. (2019). Social media for knowledge-sharing: a systematic literature review. Telematics and Informatics, 37 , 72–112.

Google Scholar

Alhajji, M., Bass, S., & Dai, T. (2019). Cyberbullying, mental health, and violence in adolescents and associations with sex and race: data from the 2015 youth risk behavior survey. Global Pediatric Health, 6 , 2333794X19868887.

PubMed PubMed Central Google Scholar

Alvarez-Jimenez, M., Bendall, S., Lederman, R., Wadley, G., Chinnery, G., Vargas, S., Larkin, M., Killackey, E., McGorry, P., & Gleeson, J. F. (2013). On the HORYZON: moderated online social therapy for long-term recovery in first episode psychosis. Schizophrenia Research, 143 (1), 143–149.

Alvarez-Jimenez, M., Gleeson, J., Bendall, S., Penn, D., Yung, A., Ryan, R., et al. (2018). Enhancing social functioning in young people at ultra high risk (UHR) for psychosis: a pilot study of a novel strengths and mindfulness-based online social therapy. Schizophrenia Research, 202 , 369–377.

Alvarez-Jimenez, M., Bendall, S., Koval, P., Rice, S., Cagliarini, D., Valentine, L., et al. (2019). HORYZONS trial: protocol for a randomised controlled trial of a moderated online social therapy to maintain treatment effects from first-episode psychosis services. BMJ Open, 9 (2), e024104.

Andreassen, C. S., Billieux, J., Griffiths, M. D., Kuss, D. J., Demetrovics, Z., Mazzoni, E., & Pallesen, S. (2016). The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: a large-scale cross-sectional study. Psychology of Addictive Behaviors, 30 (2), 252.

Aschbrenner, K. A., Naslund, J. A., & Bartels, S. J. (2016a). A mixed methods study of peer-to-peer support in a group-based lifestyle intervention for adults with serious mental illness. Psychiatric Rehabilitation Journal, 39 (4), 328–334.

Aschbrenner, K. A., Naslund, J. A., Shevenell, M., Kinney, E., & Bartels, S. J. (2016b). A pilot study of a peer-group lifestyle intervention enhanced with mHealth technology and social media for adults with serious mental illness. The Journal of Nervous and Mental Disease, 204 (6), 483–486.

Aschbrenner, K. A., Naslund, J. A., Shevenell, M., Mueser, K. T., & Bartels, S. J. (2016c). Feasibility of behavioral weight loss treatment enhanced with peer support and mobile health technology for individuals with serious mental illness. Psychiatric Quarterly, 87 (3), 401–415.

Aschbrenner, K. A., Naslund, J. A., Gorin, A. A., Mueser, K. T., Scherer, E. A., Viron, M., et al. (2018a). Peer support and mobile health technology targeting obesity-related cardiovascular risk in young adults with serious mental illness: protocol for a randomized controlled trial. Contemporary Clinical Trials, 74 , 97–106.

Aschbrenner, K. A., Naslund, J. A., Grinley, T., Bienvenida, J. C. M., Bartels, S. J., & Brunette, M. (2018b). A survey of online and mobile technology use at peer support agencies. Psychiatric Quarterly , 1–10.

Aschbrenner, K. A., Naslund, J. A., Tomlinson, E. F., Kinney, A., Pratt, S. I., & Brunette, M. F. (2019). Adolescents’ use of digital technologies and preferences for mobile health coaching in mental health settings. Frontiers in Public Health. 7 , 178.

Badcock, J. C., Shah, S., Mackinnon, A., Stain, H. J., Galletly, C., Jablensky, A., & Morgan, V. A. (2015). Loneliness in psychotic disorders and its association with cognitive function and symptom profile. Schizophrenia Research, 169 (1–3), 268–273.

Batterham, P. J., & Calear, A. J. (2017). Preferences for internet-based mental health interventions in an adult online sample: findings from ann online community survey. JMIR Mental Health, 4 (2), e26.

Bauer, R., Bauer, M., Spiessl, H., & Kagerbauer, T. (2013). Cyber-support: an analysis of online self-help forums (online self-help forums in bipolar disorder). Nordic Journal of Psychiatry, 67 (3), 185–190.

Berger, M., Wagner, T. H., & Baker, L. C. (2005). Internet use and stigmatized illness. Social Science & Medicine, 61 (8), 1821–1827.

Berry, N., Lobban, F., Belousov, M., Emsley, R., Nenadic, G., & Bucci, S. (2017). # WhyWeTweetMH: understanding why people use Twitter to discuss mental health problems. Journal of Medical Internet Research, 19 (4), e107.

Berry, N., Emsley, R., Lobban, F., & Bucci, S. (2018). Social media and its relationship with mood, self-esteem and paranoia in psychosis. Acta Psychiatrica Scandinavica, 138 , 558–570.

Best, P., Manktelow, R., & Taylor, B. (2014). Online communication, social media and adolescent wellbeing: a systematic narrative review. Children and Youth Services Review, 41 , 27–36.

Biagianti, B., Quraishi, S. H., & Schlosser, D. A. (2018). Potential benefits of incorporating peer-to-peer interactions into digital interventions for psychotic disorders: a systematic review. Psychiatric Services, 69 (4), 377–388.

Bidargaddi, N., Musiat, P., Makinen, V.-P., Ermes, M., Schrader, G., & Licinio, J. (2017). Digital footprints: facilitating large-scale environmental psychiatric research in naturalistic settings through data from everyday technologies. Molecular Psychiatry, 22 (2), 164.

Birnbaum, M. L., Ernala, S. K., Rizvi, A. F., De Choudhury, M., & Kane, J. M. (2017a). A collaborative approach to identifying social media markers of schizophrenia by employing machine learning and clinical appraisals. Journal of Medical Internet Research, 19 (8), e289.

Birnbaum, M. L., Rizvi, A. F., Correll, C. U., Kane, J. M., & Confino, J. (2017b). Role of social media and the Internet in pathways to care for adolescents and young adults with psychotic disorders and non-psychotic mood disorders. Early Intervention in Psychiatry, 11 (4), 290–295.

Booker, C. L., Kelly, Y. J., & Sacker, A. (2018). Gender differences in the associations between age trends of social media interaction and well-being among 10-15 year olds in the UK. BMC Public Health, 18 (1), 321.

Brunette, M., Achtyes, E., Pratt, S., Stilwell, K., Opperman, M., Guarino, S., & Kay-Lambkin, F. (2019). Use of smartphones, computers and social media among people with SMI: opportunity for intervention. Community Mental Health Journal , 1–6.

Brusilovskiy, E., Townley, G., Snethen, G., & Salzer, M. S. (2016). Social media use, community participation and psychological well-being among individuals with serious mental illnesses. Computers in Human Behavior, 65 , 232–240.

Bucci, S., Schwannauer, M., & Berry, N. (2019). The digital revolution and its impact on mental health care. Psychology and Psychotherapy: Theory, Research and Practice, 92 (2), 277–297.

Chancellor, S., Birnbaum, M. L., Caine, E. D., Silenzio, V. M., & De Choudhury, M. (2019). A taxonomy of ethical tensions in inferring mental health states from social media. In Proceedings of the Conference on Fairness, Accountability, and Transparency, 79–88.

Chang, H. J. (2009). Online supportive interactions: using a network approach to examine communication patterns within a psychosis social support group in Taiwan. Journal of the American Society for Information Science and Technology, 60 (7), 1504–1517.

Davidson, L., Chinman, M., Sells, D., & Rowe, M. (2006). Peer support among adults with serious mental illness: a report from the field. Schizophrenia Bulletin, 32 (3), 443–450.

De Choudhury, M., Gamon, M., & Counts, S. (2012). Happy, nervous or surprised? classification of human affective states in social media. Paper presented at the sixth international Association for Advancement of Artificial Intelligence (AAAI) Conference on Weblogs and Social Meedia, 435–438.

De Choudhury, M., Gamon, M., Counts, S., & Horvitz, E. (2013). Predicting depression via social media. Paper presented at the seventh international Association for Advancement of Artificial Intelligence (AAAI) Conference on Weblogs and Social Media, 128–137.

Docherty, N. M., Hawkins, K. A., Hoffman, R. E., Quinlan, D. M., Rakfeldt, J., & Sledge, W. H. (1996). Working memory, attention, and communication disturbances in schizophrenia. Journal of Abnormal Psychology, 105 (2), 212–219.

Ernala, S. K., Rizvi, A. F., Birnbaum, M. L., Kane, J. M., & De Choudhury, M. (2017). Linguistic markers indicating therapeutic outcomes of social media disclosures of schizophrenia. Proceedings of the ACM on Human-Computer Interaction, 1 (1), 43.

Feinstein, B. A., Hershenberg, R., Bhatia, V., Latack, J. A., Meuwly, N., & Davila, J. (2013). Negative social comparison on Facebook and depressive symptoms: rumination as a mechanism. Psychology of Popular Media Culture, 2 (3), 161.

Firth, J., Cotter, J., Torous, J., Bucci, S., Firth, J. A., & Yung, A. R. (2015). Mobile phone ownership and endorsement of “mHealth” among people with psychosis: a meta-analysis of cross-sectional studies. Schizophrenia Bulletin, 42 (2), 448–455.

Firth, J., Rosenbaum, S., Stubbs, B., Gorczynski, P., Yung, A. R., & Vancampfort, D. (2016). Motivating factors and barriers towards exercise in severe mental illness: a systematic review and meta-analysis. Psychological Medicine, 46 (14), 2869–2881.

Gay, K., Torous, J., Joseph, A., Pandya, A., & Duckworth, K. (2016). Digital technology use among individuals with schizophrenia: results of an online survey. JMIR Mental Health, 3 (2), e15.

Giacco, D., Palumbo, C., Strappelli, N., Catapano, F., & Priebe, S. (2016). Social contacts and loneliness in people with psychotic and mood disorders. Comprehensive Psychiatry, 66 , 59–66.

Gleeson, J., Lederman, R., Herrman, H., Koval, P., Eleftheriadis, D., Bendall, S., Cotton, S. M., & Alvarez-Jimenez, M. (2017). Moderated online social therapy for carers of young people recovering from first-episode psychosis: study protocol for a randomised controlled trial. Trials, 18 (1), 27.

Glick, G., Druss, B., Pina, J., Lally, C., & Conde, M. (2016). Use of mobile technology in a community mental health setting. Journal of Telemedicine and Telecare, 22 (7), 430–435.

Goodman, L. A., Thompson, K. M., Weinfurt, K., Corl, S., Acker, P., Mueser, K. T., & Rosenberg, S. D. (1999). Reliability of reports of violent victimization and posttraumatic stress disorder among men and women with serious mental illness. Journal of Traumatic Stress: Official Publication of the International Society for Traumatic Stress Studies, 12 (4), 587–599.

Gowen, K., Deschaine, M., Gruttadara, D., & Markey, D. (2012). Young adults with mental health conditions and social networking websites: seeking tools to build community. Psychiatric Rehabilitation Journal, 35 (3), 245–250.

Guntuku, S. C., Yaden, D. B., Kern, M. L., Ungar, L. H., & Eichstaedt, J. C. (2017). Detecting depression and mental illness on social media: an integrative review. Current Opinion in Behavioral Sciences, 18 , 43–49.

Haker, H., Lauber, C., & Rössler, W. (2005). Internet forums: a self-help approach for individuals with schizophrenia? Acta Psychiatrica Scandinavica, 112 (6), 474–477.

Hamm, M. P., Newton, A. S., Chisholm, A., Shulhan, J., Milne, A., Sundar, P., Ennis, H., Scott, S. D., & Hartling, L. (2015). Prevalence and effect of cyberbullying on children and young people: a scoping review of social media studies. JAMA Pediatrics, 169 (8), 770–777.

Hansen, C. F., Torgalsbøen, A.-K., Melle, I., & Bell, M. D. (2009). Passive/apathetic social withdrawal and active social avoidance in schizophrenia: difference in underlying psychological processes. The Journal of Nervous and Mental Disease, 197 (4), 274–277.

Highton-Williamson, E., Priebe, S., & Giacco, D. (2015). Online social networking in people with psychosis: a systematic review. International Journal of Social Psychiatry, 61 (1), 92–101.

Hilty, D. M., Chan, S., Torous, J., Luo, J., & Boland, R. J. (2019). Mobile health, smartphone/device, and apps for psychiatry and medicine: competencies, training, and faculty development issues. Psychiatric Clinics, 42 (3), 513–534.

Hswen, Y., Naslund, J. A., Chandrashekar, P., Siegel, R., Brownstein, J. S., & Hawkins, J. B. (2017). Exploring online communication about cigarette smoking among Twitter users who self-identify as having schizophrenia. Psychiatry Research, 257 , 479–484.

Hswen, Y., Naslund, J. A., Brownstein, J. S., & Hawkins, J. B. (2018a). Monitoring online discussions about suicide among Twitter users with schizophrenia: exploratory study. JMIR Mental Health, 5 (4), e11483.

Hswen, Y., Naslund, J. A., Brownstein, J. S., & Hawkins, J. B. (2018b). Online communication about depression and anxiety among twitter users with schizophrenia: preliminary findings to inform a digital phenotype using social media. Psychiatric Quarterly, 89 (3), 569–580.

Indian, M., & Grieve, R. (2014). When Facebook is easier than face-to-face: social support derived from Facebook in socially anxious individuals. Personality and Individual Differences, 59 , 102–106.

Jain, S. H., Powers, B. W., Hawkins, J. B., & Brownstein, J. S. (2015). The digital phenotype. Nature Biotechnology, 33 (5), 462–463.

Kiesler, S., Siegel, J., & McGuire, T. W. (1984). Social psychological aspects of computer-mediated communication. American Psychologist, 39 , 1123–1134.

Kross, E., Verduyn, P., Demiralp, E., Park, J., Lee, D. S., Lin, N., Shablack, H., Jonides, J., & Ybarra, O. (2013). Facebook use predicts declines in subjective well-being in young adults. PLoS One, 8 (8), e69841.

Lal, S., Nguyen, V., & Theriault, J. (2018). Seeking mental health information and support online: experiences and perspectives of young people receiving treatment for first-episode psychosis. Early Intervention in Psychiatry, 12 (3), 324–330.

Lin, L. Y., Sidani, J. E., Shensa, A., Radovic, A., Miller, E., Colditz, J. B., Hoffman, B. L., Giles, L. M., & Primack, B. A. (2016). Association between social media use and depression among US young adults. Depression and Anxiety, 33 (4), 323–331.

Machmutow, K., Perren, S., Sticca, F., & Alsaker, F. D. (2012). Peer victimisation and depressive symptoms: can specific coping strategies buffer the negative impact of cybervictimisation? Emotional and Behavioural Difficulties, 17 (3–4), 403–420.

Manikonda, L., & De Choudhury, M. (2017). Modeling and understanding visual attributes of mental health disclosures in social media. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 170–181.

Mead, S., Hilton, D., & Curtis, L. (2001). Peer support: a theoretical perspective. Psychiatric Rehabilitation Journal, 25 (2), 134–141.

Mereish, E. H., Sheskier, M., Hawthorne, D. J., & Goldbach, J. T. (2019). Sexual orientation disparities in mental health and substance use among Black American young people in the USA: effects of cyber and bias-based victimisation. Culture, Health & Sexuality, 21 (9), 985–998.

Miller, B. J., Stewart, A., Schrimsher, J., Peeples, D., & Buckley, P. F. (2015). How connected are people with schizophrenia? Cell phone, computer, email, and social media use. Psychiatry Research, 225 (3), 458–463.

Mittal, V. A., Tessner, K. D., & Walker, E. F. (2007). Elevated social Internet use and schizotypal personality disorder in adolescents. Schizophrenia Research, 94 (1–3), 50–57.

Moorhead, S. A., Hazlett, D. E., Harrison, L., Carroll, J. K., Irwin, A., & Hoving, C. (2013). A new dimension of health care: systematic review of the uses, benefits, and limitations of social media for health communication. Journal of Medical Internet Research, 15 (4), e85.

Naslund, J. A., & Aschbrenner, K. A. (2019). Risks to privacy with use of social media: understanding the views of social media users with serious mental illness. Psychiatric Services, 70 (7), 561–568.

Naslund, J. A., Grande, S. W., Aschbrenner, K. A., & Elwyn, G. (2014). Naturally occurring peer support through social media: the experiences of individuals with severe mental illness using YouTube. PLoS One, 9 (10), e110171.

Naslund, J. A., Aschbrenner, K. A., & Bartels, S. J. (2016). How people living with serious mental illness use smartphones, mobile apps, and social media. Psychiatric Rehabilitation Journal, 39 (4), 364–367.

Naslund, J. A., Aschbrenner, K. A., Marsch, L. A., & Bartels, S. J. (2016a). Feasibility and acceptability of Facebook for health promotion among people with serious mental illness. Digital Health, 2 , 2055207616654822.

PubMed Central Google Scholar

Naslund, J. A., Aschbrenner, K. A., Marsch, L. A., & Bartels, S. J. (2016b). The future of mental health care: peer-to-peer support and social media. Epidemiology and Psychiatric Sciences, 25 (2), 113–122.

Naslund, J. A., Aschbrenner, K. A., McHugo, G. J., Unützer, J., Marsch, L. A., & Bartels, S. J. (2019). Exploring opportunities to support mental health care using social media: A survey of social media users with mental illness. Early Intervention in Psychiatry, 13 (3), 405–413.

Naslund, J. A., Aschbrenner, K. A., Marsch, L. A., McHugo, G. J., & Bartels, S. J. (2018). Facebook for supporting a lifestyle intervention for people with major depressive disorder, bipolar disorder, and schizophrenia: an exploratory study. Psychiatric Quarterly, 89 (1), 81–94.

Naslund, J. A., Gonsalves, P. P., Gruebner, O., Pendse, S. R., Smith, S. L., Sharma, A., & Raviola, G. (2019). Digital innovations for global mental health: opportunities for data science, task sharing, and early intervention. Current Treatment Options in Psychiatry , 1–15.

Onnela, J.-P., & Rauch, S. L. (2016). Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health. Neuropsychopharmacology, 41 (7), 1691–1696.

Orben, A., & Przybylski, A. K. (2019). The association between adolescent well-being and digital technology use. Nature Human Behaviour, 3 (2), 173–182.

Patel, V., Saxena, S., Lund, C., Thornicroft, G., Baingana, F., Bolton, P., et al. (2018). The Lancet Commission on global mental health and sustainable development. The Lancet, 392 (10157), 1553–1598.

Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: a nationally-representative study among US young adults. Computers in Human Behavior, 69 , 1–9.

Reece, A. G., & Danforth, C. M. (2017). Instagram photos reveal predictive markers of depression. EPJ Data Science, 6 (1), 15.

Reece, A. G., Reagan, A. J., Lix, K. L., Dodds, P. S., Danforth, C. M., & Langer, E. J. (2017). Forecasting the onset and course of mental illness with Twitter data. Scientific Reports, 7 (1), 13006.

Rideout, V., & Fox, S. (2018). Digital health practices, social media use, and mental well-being among teens and young adults in the U.S. Retrieved from San Francisco, CA: https://www.hopelab.org/reports/pdf/a-national-survey-by-hopelab-and-well-being-trust-2018.pdf . Accessed 10 Jan 2020.

Saha, K., Torous, J., Ernala, S. K., Rizuto, C., Stafford, A., & De Choudhury, M. (2019). A computational study of mental health awareness campaigns on social media. Translational behavioral medicine, 9 (6), 1197–1207.

Schlosser, D. A., Campellone, T., Kim, D., Truong, B., Vergani, S., Ward, C., & Vinogradov, S. (2016). Feasibility of PRIME: a cognitive neuroscience-informed mobile app intervention to enhance motivated behavior and improve quality of life in recent onset schizophrenia. JMIR Research Protocols, 5 (2).

Schlosser, D. A., Campellone, T. R., Truong, B., Etter, K., Vergani, S., Komaiko, K., & Vinogradov, S. (2018). Efficacy of PRIME, a mobile app intervention designed to improve motivation in young people with schizophrenia. Schizophrenia Bulletin, 44 (5), 1010–1020.

Schrank, B., Sibitz, I., Unger, A., & Amering, M. (2010). How patients with schizophrenia use the internet: qualitative study. Journal of Medical Internet Research, 12 (5), e70.

Schueller, S. M., Hunter, J. F., Figueroa, C., & Aguilera, A. (2019). Use of digital mental health for marginalized and underserved populations. Current Treatment Options in Psychiatry, 6 (3), 243–255.

Shatte, A. B., Hutchinson, D. M., & Teague, S. J. (2019). Machine learning in mental health: a scoping review of methods and applications. Psychological Medicine, 49 (9), 1426–1448.

Spinzy, Y., Nitzan, U., Becker, G., Bloch, Y., & Fennig, S. (2012). Does the Internet offer social opportunities for individuals with schizophrenia? A cross-sectional pilot study. Psychiatry Research, 198 (2), 319–320.

Stiglic, N., & Viner, R. M. (2019). Effects of screentime on the health and well-being of children and adolescents: a systematic review of reviews. BMJ Open, 9 (1), e023191.

Sumner, S. A., Galik, S., Mathieu, J., Ward, M., Kiley, T., Bartholow, B., et al. (2019). Temporal and geographic patterns of social media posts about an emerging suicide game. Journal of Adolescent Health, 65 (1), 94–100.

Torous, J., & Keshavan, M. (2016). The role of social media in schizophrenia: evaluating risks, benefits, and potential. Current Opinion in Psychiatry, 29 (3), 190–195.

Torous, J., Chan, S. R., Tan, S. Y.-M., Behrens, J., Mathew, I., Conrad, E. J., et al. (2014a). Patient smartphone ownership and interest in mobile apps to monitor symptoms of mental health conditions: a survey in four geographically distinct psychiatric clinics. JMIR Mental Health, 1 (1), e5.

Torous, J., Friedman, R., & Keshavan, M. (2014b). Smartphone ownership and interest in mobile applications to monitor symptoms of mental health conditions. JMIR mHealth and uHealth, 2 (1), e2.

Torous, J., Wisniewski, H., Bird, B., Carpenter, E., David, G., Elejalde, E., et al. (2019). Creating a digital health smartphone app and digital phenotyping platform for mental health and diverse healthcare needs: an interdisciplinary and collaborative approach. Journal of Technology in Behavioral Science, 4 (2), 73–85.

Trefflich, F., Kalckreuth, S., Mergl, R., & Rummel-Kluge, C. (2015). Psychiatric patients' internet use corresponds to the internet use of the general public. Psychiatry Research, 226 , 136–141.

Twenge, J. M., & Campbell, W. K. (2018). Associations between screen time and lower psychological well-being among children and adolescents: evidence from a population-based study. Preventive Medicine Reports, 12 , 271–283.

Twenge, J. M., Joiner, T. E., Rogers, M. L., & Martin, G. N. (2018). Increases in depressive symptoms, suicide-related outcomes, and suicide rates among US adolescents after 2010 and links to increased new media screen time. Clinical Psychological Science, 6 (1), 3–17.

Tynes, B. M., Willis, H. A., Stewart, A. M., & Hamilton, M. W. (2019). Race-related traumatic events online and mental health among adolescents of color. Journal of Adolescent Health, 65 (3), 371–377.

Vannucci, A., Flannery, K. M., & Ohannessian, C. M. (2017). Social media use and anxiety in emerging adults. Journal of Affective Disorders, 207 , 163–166.

Vayreda, A., & Antaki, C. (2009). Social support and unsolicited advice in a bipolar disorder online forum. Qualitative Health Research, 19 (7), 931–942.

Ventola, C. L. (2014). Social media and health care professionals: benefits, risks, and best practices. Pharmacy and Therapeutics, 39 (7), 491–520.

We Are Social. (2020). Digital in 2020. Retrieved from https://wearesocial.com/global-digital-report-2019 . Accessed 10 Jan 2020.

Webb, H., Jirotka, M., Stahl, B. C., Housley, W., Edwards, A., Williams, M., ... & Burnap, P. (2017). The ethical challenges of publishing Twitter data for research dissemination . Paper presented at the proceedings of the 2017 ACM on Web Science Conference, 339–348.

Williams, M. L., Burnap, P., & Sloan, L. (2017). Towards an ethical framework for publishing twitter data in social research: taking into account users’ views, online context and algorithmic estimation. Sociology, 51 (6), 1149–1168.

Woods, H. C., & Scott, H. (2016). # Sleepyteens: social media use in adolescence is associated with poor sleep quality, anxiety, depression and low self-esteem. Journal of Adolescence, 51 , 41–49.

Ybarra, M. L. (2004). Linkages between depressive symptomatology and internet harassment among young regular Internet users. Cyberpsychology & Behavior, 7 (2), 247–257.

Download references

Dr. Naslund is supported by a grant from the National Institute of Mental Health (U19MH113211). Dr. Aschbrenner is supported by a grant from the National Institute of Mental Health (1R01MH110965-01).

Author information

Authors and affiliations.

Department of Global Health and Social Medicine, Harvard Medical School, 641 Huntington Avenue, Boston, MA, 02115, USA

John A. Naslund

Digital Mental Health Research Consultant, Mumbai, India

Ameya Bondre

Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

John Torous

Department of Psychiatry, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA

Kelly A. Aschbrenner

You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to John A. Naslund .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Naslund, J.A., Bondre, A., Torous, J. et al. Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice. J. technol. behav. sci. 5 , 245–257 (2020). https://doi.org/10.1007/s41347-020-00134-x

Download citation

Received : 19 October 2019

Revised : 24 February 2020

Accepted : 17 March 2020

Published : 20 April 2020

Issue Date : September 2020

DOI : https://doi.org/10.1007/s41347-020-00134-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Find a journal
Publish with us
Track your research

How Social Media Affects Your Teen’s Mental Health: A Parent’s Guide

BY KATHY KATELLA June 17, 2024

two teenage girls in their room on their phones, representing how social media can affect teens' mental health

[Originally published: Jan. 8, 2024. Updated: June 17, 2024.]

Mental health issues among teens have been rising for more than a decade, and some experts wonder how much social media use is to blame. If you’re a parent questioning if—and how—you should monitor the way your teenager uses social media, you’re not alone.

In the spring of 2023, United States Surgeon General Vivek Murthy, MD, MBA, released an advisory called Social Media and Youth Mental Health , in which he says there is growing evidence that social media is causing harm to young people’s mental health. Soon after, the American Psychological Association (APA) issued its own health advisory . A year later, in June 2024, Dr. Murthy called for a surgeon general’s warning label on social media platforms, which would require an act of Congress to implement.

The issue is complicated, however. While there are indicators that it can have a profound risk of harm to teens (more on that below), social media use aimed at making healthy connections with others may actually be beneficial to some people. Dr. Murthy’s report indicates that more research is needed to fully understand the impact of social media. For parents, this means there are no easy answers.

“The issues we face now with social media are similar to those we faced when television came out,” says Linda Mayes, MD , chair of the Yale Child Study Center (YCSC). She explains that, as with TV watching, there are pros and cons to social media for young people. “So, how do we help parents filter out the parts that may be detrimental?”

Below, Dr. Mayes and YCSC’s Yann Poncin, MD , a child psychiatrist, offer advice for parents trying to help their teenagers use social media in a positive way. But first, some background.

Social media use and teens: Background, benefits, and harms

As a parent, you might ask yourself, “What, specifically, about social media use can have a negative impact on my teen?”

Dr. Murthy’s advisory was based on what it describes as a “substantial review of the available evidence.” It raises a variety of concerns, including the amount of time adolescents spend on platforms, the type of content they consume (or are exposed to), and the degree to which their online interactions disrupt activities essential for health, such as sleep and exercise. It points out that social media can also affect young users in different ways, depending on their strengths and vulnerabilities as individuals, as well as their cultural, historical, and socio-economic backgrounds.

The report stresses that the brain is going through a highly sensitive period between the ages of 10 and 19, when identities and feelings of self-worth are forming. Frequent social media use may be associated with distinct changes in the developing brain, potentially affecting such functions as emotional learning and behavior, impulse control, and emotional regulation.

What are the potential benefits of social media use by teens?

Some teenagers experience a benefit when they use social media to foster positive connections with others who share common interests or identities (if they are seeking a connection with others who are, say, members of a particular racial identity), creating a space for self-expression. Relationships formed in communities like these can create opportunities for positive interactions with more diverse peer groups than are available to them offline, according to Dr. Murthy’s report.

The advisory points to a 2022 survey of American teenagers and their parents by the Pew Research Center, which showed that a majority of respondents felt that social media helps teenagers feel more accepted (58%), like they have people who can support them through tough times (67%), that they have a place to show their creative side (71%), and that they are more connected to what’s going on in their friends’ lives (80%).

“Posting to let your friends know how you’ve been spending your time can be a positive or healthy way to connect and hear about each other’s day,” says Dr. Poncin. “It’s no different than 30 years ago when adolescents would be on the phone for three hours connecting with their friends—only now you're online with your friends, saying, ‘Meet you after school,’ or ‘Did you hear about this?’”

What are the potential harms of social media use by teens?

Over the last decade, increasing evidence has identified the potential negative impact of social media on adolescents. According to a research study of American teens ages 12-15, those who used social media over three hours each day faced twice the risk of having negative mental health outcomes, including depression and anxiety symptoms.

The advisory states that other studies "point to a higher relative concern of harm in adolescent girls and those already experiencing poor mental health, as well as for particular health outcomes, such as cyberbullying-related depression, body image and disordered eating behaviors, and poor sleep quality linked to social media use."

“What’s more, the social media algorithms are built to promote whatever you seem interested in,” says Dr. Mayes. “If a teen searches for any kind of mental health condition, such as depression or suicide, it's going to feed them information about those things, so soon they may begin to think that everyone around them is depressed or thinking about suicide, which is not necessarily good for mental health.”

When does the kind of content teens see become an issue?

Teens can easily access extreme, inappropriate, and harmful content. In certain cases, deaths have been linked to suicide- and self-harm-related content, such as “cutting,” partial asphyxiation, and risk-taking challenges on social media platforms, according to Dr. Murthy’s report. Studies also found that discussing or showing this content can normalize these behaviors.

Eating disorders are yet another concern. A review of 50 studies across 17 countries between 2016 and 2021 published in PLOS Global Public Health suggested that relentless online exposure to largely unattainable physical ideals may trigger a distorted sense of self and eating disorders. This is considered to be a particular problem among girls.

In addition, people who target adolescents—for instance, adults seeking to sexually exploit teens or financially extort them through the threat or actual distribution of intimate images—may use social media platforms for these types of predatory behaviors, according to the Surgeon General's advisory.

Why is excessive use of social media a problem?

The excessive use of social media can harm teens by disrupting important healthy behaviors. Some researchers think that exposure to social media can overstimulate the brain's reward center and, when the stimulation becomes excessive, can trigger pathways comparable to addiction.

Excessive use has also been linked to sleep problems, attention problems, and feelings of exclusion in adolescents. And sleep is essential for the healthy development of teens, according to Dr. Murthy’s advisory.

Social media use in teens: A guide for parents

After reading the background, as a parent, you might ask yourself, “Sure, but do kids really use social media that much?”

Social media use among young people is nearly universal now, based on surveys from the Pew Research Center. In 2022, up to 95% of teenagers surveyed (ages 13 to 17) reported using social media, and more than a third of them use it “almost constantly.”

Pew has also tracked which social media platforms (or “apps”) teenagers are using. In 2023, it found the majority of teens—9 out of 10 for those ages 13 to 17—use YouTube, followed by TikTok, Snapchat, and Instagram. (Their use of Facebook dropped dramatically; there was also a decrease in the use of Twitter, now called X, although that was not as steep.) With that in mind, YCSC experts provide a guide for parents concerned about their teens’ social media use.

1. Determine the age your child will have access to social media.

Experts are still exploring whether there is a “right age” for a child to access social media. The APA explains that adolescents mature at different rates, which makes establishing a universal age recommendation difficult.

Although the minimum age most commonly required by social media platforms in the U.S. is 13, nearly 40% of children ages 8–12 use social media, according to Dr. Murthy’s report. That signals how difficult it can be to enforce these rules without parental supervision.

One strategy is to make a social media plan for your family long before the teenage years, Dr. Poncin says. “In my opinion, elementary school-age children should not have full-on internet access using a device with all the social media apps.”

In terms of phones, they can start with a “dumbphone,” a cell phone that doesn’t have email, an internet browser, and other features found on smartphones, he adds.

For middle-schoolers who show maturity and responsibility—who can get themselves to sleep and do their homework, for example—additional access may be fine, notes Dr. Poncin. But he suggests delaying full access to smartphones for as long as possible, opting for a device allowing you to add more apps as your child matures.

Establishing a family social media plan might also be useful—the American Academy of Pediatrics offers a tool that can help. In addition to setting the age at which you plan to start giving your kids phones or internet access, this plan can be used to establish rules and educate children and teens about being careful about privacy settings, avoiding strangers online, not giving out personal information, and knowing how to report cyberbullying, Dr. Mayes says.

2. Keep devices out of the bedroom.

Research shows a relationship between social media use and poor sleep quality, reduced sleep duration, and sleep difficulties in young people, according to Dr. Murthy’s advisory. For teens, poor sleep is linked to emotional health issues and a higher risk for suicide.

According to Dr. Murthy’s report, on a typical weekday, nearly one-third of adolescents report using screen media until midnight or later. (While screen media use includes various digital activities, social media apps are the most commonly used applications by teens.)

“Knowing that, try to create a culture at home where all phones are turned off by a certain time, and make sure it's at least one hour before going to bed,” Dr. Poncin says.

However, you may find that bedtime rules don’t work as well as your kids get older. It may be necessary to ask your teen to put their phone outside the bedroom before going to bed. “But, if the response is, ‘I do my homework late and have a group chat about math, so I'm going to need the phone to group chat,’ and you suspect your teen isn’t being honest, that will be a different conversation,” Dr. Poncin says. “But having these open conversations is critical.”

3. Keep the lines of communication open, and let your teen make mistakes.

It will be easier to talk to your teens about social media if you have comfortable conversations with them about other issues, the doctors say.

“I don’t believe you should monitor the content of your teen’s phone, because a teenager should have privacy,” Dr. Poncin says. “An important part of the teenage years is figuring out who you are in the world. So, it's important for them to explore and even make mistakes without you hovering around them.” The goal is to keep lines of communication open and establish some trust with your child, so they'll come to you if there are issues, he adds.

This, too, is similar to the advice given when parents were concerned about the impact of television on children, adds Dr. Mayes. “Research showed that watching TV in and of itself wasn’t bad, but it began to have potentially negative effects on kids’ behavior when it was used as a babysitter,” she says. “The message to parents was to sit beside their kids and watch TV with them, and then talk about what they’re watching.”

Neither you nor your teen will be happy if you sit beside them and scroll down your social media feeds together, but you can take the time to ask how social media is working for them, Dr. Mayes suggests. “So, you're not saying, ‘Oh no! You're on social media!’ Rather, you’re normalizing it and making it clear that you're willing to talk about what they're experiencing or learning. This sets it up for them to talk to you if they run into a problem, instead of going to their peers or looking for solutions online,” she says.

4. Be mindful of your approach when talking to your teen.

Although keeping the line of communication open matters, how you have those conversations is equally important. If you are concerned about your teen’s social media use and feel the need to intervene, you might say something like, "It seems like you're on the phone so much that I don't see you just doing homework like you used to do, so I'm just worried how healthy this is for you in terms of getting your stuff done. What do you think about that?" Dr. Poncin says.

You might even need to be more assertive, for instance, saying, "I've noticed that you're on your phone until 1 a.m. When I go to the bathroom, your light is on, and you're on your phone. That’s not healthy. So, can we come up with a plan that you're most comfortable with?"

5. Follow the rules yourself.

As a parent, you are a role model and that means following all the same rules you are setting for your children—if you ask your teenager to limit their screen time, you should do so as well, says Dr. Mayes, noting that it’s not uncommon to see parents looking at their phones when they are out with their kids.

You might have a hard time resisting your social media feeds, texts, and emails. Sometimes, it helps to admit to your teenagers that you find it difficult to put down your devices, too. “This a global issue, where parents want their kids to do things differently and better than they do,” Dr. Poncin says. “So, once again, having an honest conversation is important.”

Note: Cyberbullying and online abuse or exploitation can be reported to the school or the police, or on websites such as Take It Down and CyberTipline , according to the Surgeon General report.

More news from Yale Medicine

Illustration of woman, possibly depressed, anxious, or stressed at a desk slumped over raising a white flag with an unhappy face on it.

Skip to main content
Keyboard shortcuts for audio player

Your Health
Treatments & Tests
Health Inc.
Public Health

Living Better

The truth about teens, social media and the mental health crisis.

Michaeleen Doucleff

For years, the research picture on how social media affects teen mental health has been murky. That is changing as scientists find new tools to answer the question. Olivier Douliery/AFP via Getty Images hide caption

For years, the research picture on how social media affects teen mental health has been murky. That is changing as scientists find new tools to answer the question.

Back in 2017, psychologist Jean Twenge set off a firestorm in the field of psychology.

Twenge studies generational trends at San Diego State University. When she looked at mental health metrics for teenagers around 2012, what she saw shocked her. "In all my analyses of generational data — some reaching back to the 1930s — I had never seen anything like it," Twenge wrote in the Atlantic in 2017.

Twenge warned of a mental health crisis on the horizon. Rates of depression, anxiety and loneliness were rising. And she had a hypothesis for the cause: smartphones and all the social media that comes along with them. "Smartphones were used by the majority of Americans around 2012, and that's the same time loneliness increases. That's very suspicious," Twenge told NPR in 2017.

But many of her colleagues were skeptical. Some even accused her of inciting a panic with too little — and too weak — data to back her claims.

Now, six years later, Twenge is back. She has a new book out this week, called Generations , with much more data backing her hypothesis. At the same time, several high-quality studies have begun to answer critical questions, such as does social media cause teens to become depressed and is it a key contributor to a rise in depression?

In particular, studies from three different types of experiments, altogether, point in the same direction. "Indeed, I think the picture is getting more and more consistent," says economist Alexey Makarin , at the Massachusetts Institute of Technology.

Shots - Health News

How to help young people limit screen time — and feel better about how they look, a seismic change in how teens spend their time.

In Generations , Twenge analyzes mental health trends for five age groups, from the Silent Generation, who were born between 1925 and 1945, to Gen Z, who were born between 1995 and 2012. She shows definitively that "the way teens spend their time outside of school fundamentally changed in 2012," as Twenge writes in the book.

Take for instance, hanging out with friends, in person. Since 1976, the number of times per week teens go out with friends — and without their parents — held basically steady for nearly 30 years. In 2004, it slid a bit. Then in 2010, it nosedived.

"It was just like a Black Diamond ski slope straight down," Twenge tells NPR. "So these really big changes occur."

At the same time, around 2012, time on social media began to soar. In 2009, only about half of teens used social media every day, Twenge reports. In 2017, 85% used it daily. By 2022, 95% of teens said they use some social media, and about a third say they use it constantly, a poll from Pew Research Center found .

"Now, in the most recent data, 22% of 10th grade girls spend seven or more hours a day on social media," Twenge says, which means many teenage girls are doing little else than sleeping, going to school and engaging with social media.

Not surprisingly, all this screen time has cut into many kids' sleep time. Between 2010 and 2021, the percentage of 10th and 12th graders who slept seven or fewer hours each night rose from a third to nearly one-half. "That's a big jump," Twenge says. "Kids in that age group are supposed to sleep nine hours a night. So less than seven hours is a really serious problem."

Teen girls and LGBTQ+ youth plagued by violence and trauma, survey says

On its own, sleep deprivation can cause mental health issues. "Sleep is absolutely crucial for physical health and for mental health. Not getting enough sleep is a major risk factor for anxiety and depression and self-harm," she explains. Unfortunately, all of those mental health problems have continued to rise since Twenge first sounded the alarm six years ago.

"Nuclear bomb" on teen social life

"Every indicator of mental health and psychological well-being has become more negative among teens and young adults since 2012," Twenge writes in Generations . "The trends are stunning in their consistency, breadth and size."

Across the board, since 2010, anxiety, depression and loneliness have all increased . "And it's not just symptoms that rose, but also behaviors," she says, "including emergency room visits for self-harm, for suicide attempts and completed suicides." The data goes up through 2019, so it doesn't include changes due to COVID-19.

All these rapid changes coincide with what, Twenge says, may be the most rapid uptake in a new technology in human history: the incorporation of smartphones into our lives, which has allowed nearly nonstop engagement with social media apps. Apple introduced the first iPhones in 2007, and by 2012, about 50% of American adults owned a smartphone, the Pew Research Center found .

The timing is hard to ignore, says data scientist Chris Said , who has a Ph.D. in psychology from Princeton University and has worked at Facebook and Twitter. "Social media was like a nuclear bomb on teen social life," he says. "I don't think there's anything in recent memory, or even distant history, that has changed the way teens socialize as much as social media."

Murky picture becomes clearer on causes of teen depression

But the timing doesn't tell you whether social media actually causes depression in teens.

In the past decade, scientists have published a whole slew of studies trying to answer this question, and those studies sparked intense debate among scientists and in the media. But, Said says, what many people don't realize is scientists weren't using — or didn't even have — the proper tools to answer the question. "This is a very hard problem to study," he says. "The data they were analyzing couldn't really solve the problem."

Mental Health

The mental health of teen girls and lgbtq+ teens has worsened since 2011.

So the findings have been all over the place. They've been murky, noisy, inconclusive and confusing. "When you use tools that can't fully answer the question, you're going to get weak answers," he says. "So I think that's one reason why really strong evidence didn't show up in the data, at least early on."

On top of it, psychology has a bad track record in this field, Said points out. For nearly a century, psychologists have repeatedly blamed new technologies for mental and physical health problems of children, even when they've had little — or shady — data to back up their claims.

For example, in the 1940s, psychologists worried that children were becoming addicted to radio crime dramas, psychologist Amy Orben at the University of Cambridge explains in her doctoral thesis. After that, they raised concerns about comic books, television and — eventually — video games. Thus, many researchers worried that social media may simply be the newest scapegoat for children's mental health issues.

A handful of scientists, including MIT's Alexey Makarin, noticed this problem with the data, the tools and the field's past failures, and so they took the matter into their own hands. They went out and found better tools.

Hundreds of thousands of more college students depressed

Over the past few years, several high-quality studies have come that can directly test whether social media causes depression. Instead of being murky and mixed, they support each other and show clear effects of social media. "The body of literature seems to suggest that indeed, social media has negative effects on mental health, especially on young adults' mental health," says Makarin, who led what many scientists say is the best study on the topic to date.

In that study, Makarin and his team took advantage of a once-in-a-lifetime opportunity: the staggered introduction of Facebook across U.S. colleges from 2004 to 2006. Facebook rolled out into society first on college campuses, but not all campuses introduced Facebook at the same time.

For Makarin and his colleagues, this staggered rollout is experimental gold.

"It allowed us to compare students' mental health between colleges where Facebook just arrived to colleges where Facebook had not yet arrived," he says. They could also measure how students' mental health shifted on a particular campus when people started to spend a bunch of their time on social media.

Luckily, his team could track mental health at the time because college administrators were also conducting a national survey that asked students an array of questions about their mental health, including diagnoses, therapies and medications for depression, anxiety and eating disorders. "These are not just people's feelings," Makarin says. "These are actual conditions that people have to report."

They had data on a large number of students. "The data comes from more than 350,000 student responses across more than 300 colleges," Makarin says.

This type of study is called a quasi-experiment, and it allows scientists to estimate how much social media actually changes teens' mental health, or as Makarin says, "We can get causal estimates of the impact of Facebook on mental health."

So what happened? "Almost immediately after Facebook arrives on campus, we see an uptick in mental health issues that students report," Makarin says. "We especially find an impact on depression rates, anxiety disorders and other questions associated with depression in general."

And the effect isn't small, he says. Across the population, the rollout of Facebook caused about 2% of college students to become clinically depressed. That may sound modest, but with more than 17 million college students in the U.S. at the time, that means Facebook caused more than 300,000 young adults to suffer from depression.

For an individual, on average, engaging with Facebook decreases their mental health by roughly 22% of the effect of losing one's job, as reported by a previous meta-analysis, Makarin and his team found.

Facebook's rollout had a larger effect on women's mental health than on men's mental health, the study showed. But the difference was small, Makarin says.

He and his colleagues published their findings last November in the American Economic Review . "I love that paper," says economist Matthew Gentzkow at Stanford University, who was not involved in the research. "It's probably the most convincing study I've seen. I think it shows a clear effect, and it's really credible. They did a good job of isolating the effect of Facebook, which isn't easy."

Of course, the study has limitations, Gentzkow says. First off, it's Facebook, which teens are using less and less. And the version of Facebook is barebones. In 2006, the platform didn't have a "like" button" or a "newsfeed." This older version probably wasn't as "potent" as social media now, says data scientist Chris Said. Furthermore, students used the platform only on a computer because smartphones weren't available yet. And the study only examined mental health impacts over a six-month period.

Nevertheless, the findings in this study bolster other recent studies, including one that Gentzkow led.

Social media is "like the ocean" for kids

Back in 2018, Gentzkow and his team recruited about 2,700 Facebook users ages 18 or over. They paid about half of them to deactivate their Facebook accounts for four weeks. Then Gentzkow and his team looked to see how a Facebook break shifted their mental health. They reported their findings in March 2020 in the American Economic Review.

This type of study is called a randomized experiment, and it's thought of as the best way to estimate whether a variable in life causes a particular problem. But with social media, these randomized experiments have big limitations. For one, the experiments are short-term — here only four weeks. Also, people use social media in clusters, not as individuals. So having individuals quit Facebook won't capture the effect of having an entire social group quit together. Both of these limitations could underestimate the impact of social media on an individual and community.

Nevertheless, Gentzkow could see how deactivating Facebook made people, on average, feel better. "Being off Facebook was positive across well-being outcomes," he says. "You see higher happiness, life satisfaction, and also lower depression, lower anxiety, and maybe a little bit lower loneliness."

Gentzkow and his team measured participants' well-being by giving them a survey at the end of the experiment but also asking questions, via text message, through the experiment. "For example, we sent people text messages that say, 'Right now, would you say you're feeling happy or not happy,'" he explains.

Again, as with Makarin's experiment, the effect was moderate. Gentzkow and his colleagues estimate that temporarily quitting Facebook improves a person's mental health by about 30% of the positive effect seen by going to therapy. "You could view that meaning these effects are pretty big," he explains, "or you could also see that as meaning that the effects of therapy are somewhat small. And I think both of those things are true to an extent."

Scientists still don't know to what extent social media is behind the rising mental health issues among teenagers and whether it is the primary cause. "It seems to be the case — like it's a big factor," says MIT's Alexey Makarin, "but that's still up for debate."

Still, though, other specifics are beginning to crystallize. Scientists are narrowing in on what aspects of social media are most problematic. And they can see that social media won't hurt every teen — or hurt them by the same amount. The data suggests that the more hours a child devotes to social media, the higher their risk for mental health problems.

Finally, some adolescents are likely more vulnerable to social media, and children may be more vulnerable at particular ages. A study published in February 2022 looked to see how time spent on social media varies with life satisfaction during different times in a child's life (see the graphic).

The researchers also looked to see if a child's present use of social media predicted a decrease of life satisfaction one year later. That data suggests two windows of time when children are most sensitive to detrimental effects of social media, especially heavy use of it. For girls, one window occurs at ages 11 through 13. And for boys, one window occurs at ages 14 and 15. For both genders, there's a window of sensitivity around age 19 — or near the time teenagers enter college. Amy Orben and her team at the University of Cambridge reported the findings in Nature Communications .

This type of evidence is known as a correlative. "It's hard to draw conclusions from these studies," Gentzkow says, because many factors contribute to life satisfaction, such as environmental factors and family backgrounds. Plus, people may use social media because they're depressed (and so depression could be the cause, not the outcome of social media use).

"Nevertheless, these correlative studies, together with the evidence from the causal experiments, paint a picture that suggests we should take social media seriously and be concerned," Gentzkow adds.

Psychologist Orben once heard a metaphor that may help parents understand how to approach this new technology. Social media for children is a bit like the ocean, she says, noting that it can be an extremely dangerous place for children. Before parents let children swim in any open water, they make sure the child is well-prepared and equipped to handle problems that arise. They provide safety vests, swimming lessons, often in less dangerous waters, and even then parents provide a huge amount of supervision.

Alyson Hurt created the graphic. Jane Greenhalgh and Diane Webber edited the story.

mental health
smartphones
social media

Introduction
Conclusions
Article Information

The dashed lines represent potential confounding. The solid line represents the main association of interest. BMI indicates body mass index.

Error bars indicate 95% CIs.

eFigure. Participant selection from the complete PATH sample into the analytic sample.

eTable 1. Items from the GAIN-SS scale used to assess internalizing and externalizing problems.

eTable 2. Unadjusted and adjusted relative risk ratios for each category of social media use in relation to internalizing and externalizing problems among U.S. youth in the PATH Study, 2013-2016, after multiple imputation with chained equations (n=7,234).

eMethods. Calculating population attributable fractions from adjusted models.

Social Media and the Youth Mental Health Crisis JAMA Medical News & Perspectives July 3, 2023 This Medical News article discusses potential harms and benefits of social media use and what primary care physicians can do to protect children and adolescents and support families. Jennifer Abbasi
US Surgeon General Calls for Social Media Warning Labels JAMA Medical News & Perspectives September 3, 2024 This Medical News article is an interview with US Surgeon General Vivek Murthy, MD, MBA, and JAMA Editor in Chief Kirsten Bibbins-Domingo, PhD, MD, MAS, about his call for a warning label on social media platforms. Jennifer Abbasi; Yulin Hswen, ScD, MPH
Is There an Association Between Social Media Use and Mental Health? The Timing of Confounding Measurement Matters JAMA Psychiatry Comment & Response April 1, 2020 Katherine M. Keyes, PhD; Noah Kreski, MPH
Is There an Association Between Social Media Use and Mental Health? The Timing of Confounding Measurement Matters—Reply JAMA Psychiatry Comment & Response April 1, 2020 Kenneth A. Feder, PhD; Kira E. Riehm, MSc; Ramin Mojtabai, MD

See More About

Select your interests.

Customize your JAMA Network experience by selecting one or more topics from the list below.

Academic Medicine
Acid Base, Electrolytes, Fluids
Allergy and Clinical Immunology
American Indian or Alaska Natives
Anesthesiology
Anticoagulation
Art and Images in Psychiatry
Artificial Intelligence
Assisted Reproduction
Bleeding and Transfusion
Caring for the Critically Ill Patient
Challenges in Clinical Electrocardiography
Climate and Health
Climate Change
Clinical Challenge
Clinical Decision Support
Clinical Implications of Basic Neuroscience
Clinical Pharmacy and Pharmacology
Complementary and Alternative Medicine
Consensus Statements
Coronavirus (COVID-19)
Critical Care Medicine
Cultural Competency
Dental Medicine
Dermatology
Diabetes and Endocrinology
Diagnostic Test Interpretation
Drug Development
Electronic Health Records
Emergency Medicine
End of Life, Hospice, Palliative Care
Environmental Health
Equity, Diversity, and Inclusion
Facial Plastic Surgery
Gastroenterology and Hepatology
Genetics and Genomics
Genomics and Precision Health
Global Health
Guide to Statistics and Methods
Hair Disorders
Health Care Delivery Models
Health Care Economics, Insurance, Payment
Health Care Quality
Health Care Reform
Health Care Safety
Health Care Workforce
Health Disparities
Health Inequities
Health Policy
Health Systems Science
History of Medicine
Hypertension
Images in Neurology
Implementation Science
Infectious Diseases
Innovations in Health Care Delivery
JAMA Infographic
Law and Medicine
Leading Change
Less is More
LGBTQIA Medicine
Lifestyle Behaviors
Medical Coding
Medical Devices and Equipment
Medical Education
Medical Education and Training
Medical Journals and Publishing
Mobile Health and Telemedicine
Narrative Medicine
Neuroscience and Psychiatry
Notable Notes
Nutrition, Obesity, Exercise
Obstetrics and Gynecology
Occupational Health
Ophthalmology
Orthopedics
Otolaryngology
Pain Medicine
Palliative Care
Pathology and Laboratory Medicine
Patient Care
Patient Information
Performance Improvement
Performance Measures
Perioperative Care and Consultation
Pharmacoeconomics
Pharmacoepidemiology
Pharmacogenetics
Pharmacy and Clinical Pharmacology
Physical Medicine and Rehabilitation
Physical Therapy
Physician Leadership
Population Health
Primary Care
Professional Well-being
Professionalism
Psychiatry and Behavioral Health
Public Health
Pulmonary Medicine
Regulatory Agencies
Reproductive Health
Research, Methods, Statistics
Resuscitation
Rheumatology
Risk Management
Scientific Discovery and the Future of Medicine
Shared Decision Making and Communication
Sleep Medicine
Sports Medicine
Stem Cell Transplantation
Substance Use and Addiction Medicine
Surgical Innovation
Surgical Pearls
Teachable Moment
Technology and Finance
The Art of JAMA
The Arts and Medicine
The Rational Clinical Examination
Tobacco and e-Cigarettes
Translational Medicine
Trauma and Injury
Treatment Adherence
Ultrasonography
Users' Guide to the Medical Literature
Vaccination
Venous Thromboembolism
Veterans Health
Women's Health
Workflow and Process
Wound Care, Infection, Healing

Others Also Liked

Download PDF
X Facebook More LinkedIn

Riehm KE , Feder KA , Tormohlen KN, et al. Associations Between Time Spent Using Social Media and Internalizing and Externalizing Problems Among US Youth. JAMA Psychiatry. 2019;76(12):1266–1273. doi:10.1001/jamapsychiatry.2019.2325

Manage citations:

Permissions

Associations Between Time Spent Using Social Media and Internalizing and Externalizing Problems Among US Youth

1 Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
2 Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
3 Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore, Maryland
4 Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, Maryland
5 Division of Child and Adolescent Psychiatry, School of Medicine, Johns Hopkins University, Baltimore, Maryland
6 Department of Behavioral and Community Health, University of Maryland, College Park, College Park
7 Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina
8 Washington State Department of Health, Olympia
Medical News & Perspectives Social Media and the Youth Mental Health Crisis Jennifer Abbasi JAMA
Medical News & Perspectives US Surgeon General Calls for Social Media Warning Labels Jennifer Abbasi; Yulin Hswen, ScD, MPH JAMA
Comment & Response Is There an Association Between Social Media Use and Mental Health? The Timing of Confounding Measurement Matters Katherine M. Keyes, PhD; Noah Kreski, MPH JAMA Psychiatry
Comment & Response Is There an Association Between Social Media Use and Mental Health? The Timing of Confounding Measurement Matters—Reply Kenneth A. Feder, PhD; Kira E. Riehm, MSc; Ramin Mojtabai, MD JAMA Psychiatry

Question Is time spent using social media associated with mental health problems among adolescents?

Findings In this cohort study of 6595 US adolescents, increased time spent using social media per day was prospectively associated with increased odds of reporting high levels of internalizing and comorbid internalizing and externalizing problems, even after adjusting for history of mental health problems.

Meaning Adolescents who spend more than 3 hours per day on social media may be at heightened risk for mental health problems, particularly internalizing problems.

Importance Social media use may be a risk factor for mental health problems in adolescents. However, few longitudinal studies have investigated this association, and none have quantified the proportion of mental health problems among adolescents attributable to social media use.

Objective To assess whether time spent using social media per day is prospectively associated with internalizing and externalizing problems among adolescents.

Design, Setting, and Participants This longitudinal cohort study of 6595 participants from waves 1 (September 12, 2013, to December 14, 2014), 2 (October 23, 2014, to October 30, 2015), and 3 (October 18, 2015, to October 23, 2016) of the Population Assessment of Tobacco and Health study, a nationally representative cohort study of US adolescents, assessed US adolescents via household interviews using audio computer-assisted self-interviewing. Data analysis was performed from January 14, 2019, to May 22, 2019.

Exposures Self-reported time spent on social media during a typical day (none, ≤30 minutes, >30 minutes to ≤3 hours, >3 hours to ≤6 hours, and >6 hours) during wave 2.

Main Outcomes and Measure Self-reported past-year internalizing problems alone, externalizing problems alone, and comorbid internalizing and externalizing problems during wave 3 using the Global Appraisal of Individual Needs–Short Screener.

Results A total of 6595 adolescents (aged 12-15 years during wave 1; 3400 [51.3%] male) were studied. In unadjusted analyses, spending more than 30 minutes of time on social media, compared with no use, was associated with increased risk of internalizing problems alone (≤30 minutes: relative risk ratio [RRR], 1.30; 95% CI, 0.94-1.78; >30 minutes to ≤3 hours: RRR, 1.89; 95% CI, 1.36-2.64; >3 to ≤6 hours: RRR, 2.47; 95% CI, 1.74-3.49; >6 hours: RRR, 2.83; 95% CI, 1.88-4.26) and comorbid internalizing and externalizing problems (≤30 minutes: RRR, 1.39; 95% CI, 1.06-1.82; >30 minutes to ≤3 hours: RRR, 2.34; 95% CI, 1.83-3.00; >3 to ≤6 hours: RRR, 3.15; 95% CI, 2.43-4.09; >6 hours: RRR, 4.29; 95% CI, 3.22-5.73); associations with externalizing problems were inconsistent. In adjusted analyses, use of social media for more than 3 hours per day compared with no use remained significantly associated with internalizing problems alone (>3 to ≤6 hours: RRR, 1.60; 95% CI, 1.11-2.31; >6 hours: RRR, 1.78; 95% CI, 1.15-2.77) and comorbid internalizing and externalizing problems (>3 to ≤6 hours: RRR, 2.01; 95% CI, 1.51-2.66; >6 hours: RRR, 2.44; 95% CI, 1.73-3.43) but not externalizing problems alone.

Conclusions and Relevance Adolescents who spend more than 3 hours per day using social media may be at heightened risk for mental health problems, particularly internalizing problems. Future research should determine whether setting limits on daily social media use, increasing media literacy, and redesigning social media platforms are effective means of reducing the burden of mental health problems in this population.

For adolescents in the United States, social media use is ubiquitous. A 2018 Pew Research Center poll found that 97% of adolescents report using at least 1 of the 7 most popular social media platforms (YouTube, Instagram, Snapchat, Facebook, Twitter, Tumblr, and Reddit). Moreover, digital media use by adolescents is common: 95% report owning or having access to a smartphone, and almost 90% report they are online at least several times a day. 1

Social media offers numerous potential benefits to users, including exposure to current events, interpersonal connection, and enhancement of social support networks. 2 However, concerns are increasingly raised about potential harms of social media use. 2 One-quarter of adolescents think social media has a mostly negative influence on people their age, pointing to reasons like rumor spreading, lack of in-person contact, unrealistic views of others’ lives, peer pressure, and mental health issues. 1

An increasing body of literature suggests that social media use is associated with mental health problems in adolescence. Numerous cross-sectional studies and a limited number of longitudinal studies suggest that high levels of social media use are associated with internalizing problems, including depressive and anxiety symptoms, 3 - 6 although results are not entirely consistent. 7 Some studies also suggest an association between social media use and externalizing problems, such as bullying and attention problems. 8 , 9 Furthermore, a previous study 4 produced mixed results regarding the possible moderating effect of sex.

The prevalence of major depressive disorder and depressive symptoms has increased among adolescents in the United States, 10 , 11 and adolescent suicide death and attempt rates have increased sharply during the past 2 decades. 12 , 13 Some authors 14 have postulated that increases in depression may be attributable to rapid increases in social media use. However, evidence of this association in nationally representative samples is scarce, and little is known about whether reducing time spent on social media might influence the prevalence of mental health problems at a national level.

In this article, we build on existing literature by examining the prospective association of time spent on social media with internalizing and externalizing problems in a representative sample of US adolescents. We used data from the Population Assessment of Tobacco and Health (PATH) study, which is a nationally representative, longitudinal cohort of adolescents. 15 Unlike a prior study, 16 we adjusted for mental health problems measured before the exposure, which is critical for reducing the influence of reverse causality. We hypothesized that greater time spent on social media would prospectively be associated with internalizing and externalizing problems alone, as well as comorbid problems at 1-year follow-up. On the basis of past research, 5 we also examined whether these associations differed between males and females.

In this longitudinal cohort study, participants were drawn from the public-use data files of waves 1 (September 12, 2013, to December 14, 2014), 2 (October 23, 2014, to October 30, 2015), and 3 (October 18, 2015, to October 23, 2016) of the PATH study. 15 The methods of the PATH study have been previously described. 15 In brief, the target population for this survey was the civilian household population in the United States. Data were collected in 1-year intervals, starting with wave 1 from September 12, 2013, to December 14, 2014. Multistage-stratified sampling was used to obtain a sample of households from which up to 2 individuals aged 12 to 17 years were randomly selected to be interviewed. Data analysis was performed from January 14, 2019, to May 22, 2019. After oral parent permission and adolescent assent were obtained, adolescents were interviewed using audio computer-assisted self-interviewing. The current analyses were considered exempt from human subjects research according to Johns Hopkins institutional review board policy because the data were publicly available and deidentified.

The weighted response rate for adolescents during wave 1 was 78.4%, and the weighted retention rate during wave 3 was 83.3%. 17 A total of 7595 adolescents (aged 12-15 years during wave 1, aged 13-16 years during wave 2, and aged 14-17 years during wave 3) completed all 3 PATH survey waves. Of these, 1000 adolescents (13.2%) were excluded because they were missing data on at least 1 variable required for this analysis; the remaining 6595 adolescents comprised the analytic sample (eFigure in the Supplement ).

Past-year mental health problems, the outcome of interest, were assessed during wave 3 using the Global Appraisal of Individual Needs–Short Screener (GAIN-SS). 18 The GAIN-SS is a screening measure intended to identify a probable mental health disorder and assess symptom severity; it has been validated in adolescents 19 and includes internalizing and externalizing subscales (eTable 1 in the Supplement ). Each item measures 1 symptom; for this study, symptoms were considered to be present if the respondent selected in the past month or 2 to 12 months from the response options that indicated the last time they had experienced that symptom. Symptom counts were generated for each subscale. Adolescents were classified as reporting low to moderate (0-3 symptoms) or high (≥4 symptoms) internalizing and externalizing problems. These cut points have been validated for use when making treatment decisions 18 and have previously been used with the PATH sample. 20 , 21 We combined these subscales to create a single outcome variable with 4 mutually exclusive categories: no or low internalizing and externalizing problems, internalizing problems alone, externalizing problems alone, and comorbid internalizing and externalizing problems. Comorbid problems were defined as having all 4 internalizing and 4 or more externalizing symptoms.

The exposure of interest was time spent using social media per day during wave 2. Adolescents who reported that they ever went online were asked, “Sometimes people use the internet to connect with other people online through social networks like Facebook, Google Plus, YouTube, MySpace, Linkedin, Twitter, Tumblr, Instagram, Pinterest, or Snapchat. This is often called ‘social media.’ Do you have a social media account?” Adolescents who reported that they had a social media account that they visited were asked, “On a typical day, about how much total time do you spend on social media sites?” The response options were up to 30 minutes; more than 30 minutes, up to 3 hours; more than 3 hours, up to 6 hours; and more than 6 hours. We retained these categories for our exposure variable, with an additional category of none for adolescents who reported not going online, not having a social media account, or never visiting their social media account.

Potential confounders, including demographic characteristics (ie, sex, age, race, and parental educational level), body mass index (based on parent-reported weight and height), self-reported lifetime marijuana use and alcohol use, and scale scores for lifetime internalizing and externalizing problems, were adjusted for in the analyses. To ensure that we did not improperly adjust for mediating variables, 22 we used covariates measured at wave 1 instead of wave 2. The full study design is displayed in Figure 1 .

Multinomial logistic regression was used to estimate the associations between time spent on social media per day with internalizing problems alone, externalizing problems alone, and comorbid internalizing and externalizing problems (reference group: no or low internalizing and externalizing problems). Both unadjusted and adjusted analyses were conducted. Regression coefficients were exponentiated for interpretation as relative risk ratios (RRRs). In addition, we used the adjusted model to generate and plot predicted probabilities of high internalizing and externalizing problems for each level of social media use for an otherwise average study participant.

We tested for the presence of a linear trend in the coefficients for social media use in their relation to each category of mental health problems by converting the social media use variable to an ordinal variable and reestimating the adjusted model (ie, a Mantel test for trend 23 ). A linear trend would suggest that more time spent on social media is associated with a proportionally greater likelihood of reporting mental health problems.

We tested whether any observed association of social media use with mental health problems differed between males and females by testing an interaction term between social media use and sex in our adjusted model.

In addition, we estimated the respective proportions of high internalizing and high externalizing problem cases that would be potentially prevented if adolescents spent less time using social media (ie, the population-attributable fraction [PAF] for social media use). We did this for 4 counterfactual scenarios that represented increasingly greater population reductions in social media use. In scenario 1, adolescents who actually used social media more than 6 hours per day would instead use social media more than 3 hours to 6 hours or less per day; in scenario 2, adolescents who actually used social media more than 3 hours per day would instead use social media more than 30 minutes to 3 hours or less per day; in scenario 3, adolescents who actually used social media more than 30 minutes per day would instead use social media 30 minutes or less per day; and in scenario 4, adolescents who actually spent any amount of time on social media per day would instead not spend any time on social media.

We estimated each scenario by generating a counterfactual population from our adjusted model using the approach to calculate PAFs described by Greenland and Drescher 24 and Rückinger et al. 25 See the eMethods in the Supplement for a detailed description.

To test whether our results were sensitive to missing data, we repeated analyses using multiply imputed data. We performed multiple imputation using chained equations and recomputed the unadjusted, adjusted, and sex-interaction models. We stratified by sex and generated 10 imputed data sets to account for the hypothesized interaction between sex and social media use. 26

Data for analyses were weighted to be representative of 12- to 15-year-old adolescents living in the United States in 2013 to 2014. Standard errors were estimated using the wave 3 all-waves replicate weights constructed using balanced repeated replication (the Fay method) provided in the PATH data set. Statistical significance was assessed at a 2-sided P < .05 level. All analyses were conducted using Stata, version 14 (StataCorp).

A total of 6595 adolescents (aged 12-15 years during wave 1; 3400 [51.3%] male) were included in the analysis. During wave 3, of the sample of 6595 adolescents, 611 (9.1%) reported internalizing problems alone, 885 (14.0%) reported externalizing problems alone, 1169 (17.7%) reported comorbid internalizing and externalizing problems, and the remaining 3930 (59.3%) reported no or low problems. During wave 2, a total of 1125 adolescents (16.8%) reported no social media use, 2082 (31.8%) reported 30 minutes or less, 2000 (30.7%) reported more than 30 minutes to 3 hours or more, 817 (12.3%) reported more than 3 hours to 6 hours or less, and 571 (8.4%) reported more than 6 hours of use per day. Sample characteristics are given in Table 1 .

Compared with adolescents who did not use social media, the use of social media for more than 30 minutes per day was associated with greater risk of internalizing problems alone (≤30 minutes: RRR, 1.30; 95% CI, 0.94-1.78; >30 minutes to ≤3 hours: RRR, 1.89; 95% CI, 1.36-2.64; >3 to ≤6 hours: RRR, 2.47; 95% CI, 1.74-3.49; >6 hours: RRR, 2.83; 95% CI, 1.88-4.26) and comorbid internalizing and externalizing problems (≤30 minutes: RRR, 1.39; 95% CI, 1.06-1.82; >30 minutes to ≤3 hours: RRR, 2.34; 95% CI, 1.83-3.00; >3 to ≤6 hours: RRR, 3.15; 95% CI, 2.43-4.09; >6 hours: RRR, 4.29; 95% CI, 3.22-5.73) ( Table 2 ). In the adjusted model, the associations for the 2 highest categories of social media use persisted for internalizing problems alone (>3 to ≤6 hours: RRR, 1.60; 95% CI, 1.11-2.31; >6 hours: RRR, 1.78; 95% CI, 1.15-2.77), and the associations for the 3 highest categories of social media use persisted for comorbid internalizing and externalizing problems (>30 minutes to ≤3 hours: RRR, 1.59; 95% CI, 1.23-2.05; >3 to ≤6 hours: RRR, 2.01; 95% CI, 1.51-2.66; >6 hours: RRR, 2.44; 95% CI, 1.73-3.43). In contrast, in unadjusted analyses, the association of social media use with externalizing problems was inconsistent (≤30 minutes: RRR, 1.28; 95% CI, 0.98-1.67; >30 minutes to ≤3 hours: RRR, 1.60; 95% CI, 1.16-2.21; >3 to ≤6 hours: RRR, 1.36; 95% CI, 0.97-1.90; >6 hours: RRR, 1.59; 95% CI, 1.07-2.37) and not significant in the adjusted analysis (≤30 minutes: RRR, 1.18; 95% CI, 0.89-1.56; >30 minutes to ≤3 hours: RRR, 1.37; 95% CI, 0.98-1.92; >3 to ≤6 hours: RRR, 1.22; 95% CI, 0.86-1.72; >6 hours: RRR, 1.40; 95% CI, 0.90-2.19) ( Table 2 ). The predicted probabilities of high internalizing, externalizing, and comorbid problems for each level of social media use, with all other covariates set to their mean, are displayed in Figure 2 .

We observed a significant linear trend in the coefficients for both internalizing ( F 1,99 = 8.86, P = .004) and comorbid problems ( F 1,99 = 35.16, P < .001); as time on social media increased, the odds of these outcomes increased proportionately. In contrast, we observed no association for externalizing problems ( F 1,99 = 2.25, P = .14).

We observed no statistically significant interaction between social media use and sex for internalizing ( F 4,96 = 0.84, P = .50), externalizing ( F 4,96 = 0.32, P = .86), or comorbid problems ( F 4,96 = 0.73, P = .57).

All PAF estimates are given in Table 3 . On the basis of our adjusted model assuming no confounding, 0.8% to 18.9% of internalizing problems and 0.8% to 15.3% of externalizing problems could be prevented if participants had instead used less social media.

Results of analyses using multiple imputation methods did not differ appreciably from the main analyses (eTable 2 in the Supplement ).

Consistent with a prior study, 4 we found that adolescent social media use was prospectively associated with increased risk of comorbid internalizing and externalizing problems as well as internalizing problems alone. This association remained significant after adjusting for demographics, past alcohol and marijuana use, and, most importantly, a history of mental health problems, which mitigates the possibility that reverse causality explains these findings. In contrast, we did not find an association of social media use with externalizing problems alone. This finding suggests that the association of social media use with comorbid problems occurs primarily because of the association of social media with internalizing problems and the high comorbidity of internalizing and externalizing problems. Unlike a prior study, 4 we found no evidence of moderation by sex, perhaps because of the simplicity of our social media use variable, which could not capture the nature of interactions on social media that may differ by sex.

Numerous mechanisms could account for the association between social media use and internalizing problems. Adolescents who engage in high levels of social media use may experience poorer quality sleep, which may be a mediator on the pathway to internalizing problems. 27 Time spent on social media may increase the risk of experiencing cyberbullying, which has a strong association with depressive symptoms. 28 Social media may also expose adolescents to idealized self-presentations that negatively influence body image and encourage social comparisons. 4 Poor emotion regulation and lack of social interaction may also be associated with social media use and contribute to symptoms of anxiety and depression. 29

These mechanisms are potentially consistent with the notion that spending less time on social media may contribute to mental health. In fact, the PAFs obtained in our study suggest that if adolescents using social media for more than 30 minutes per day had instead used it for 30 minutes or less, there would have been 9.4% fewer high internalizing problem cases and 7.3% fewer high externalizing problem cases. Of importance, this is not meant to imply that reductions in mental health problems would definitively happen if social media use were reduced or that all social media use is harmful. Instead, these PAFs suggest the potential influence of our findings on the population at a national level assuming a causal effect of social media use and no confounding—both strong assumptions. Future research could improve on our PAF estimates by using data from randomized clinical trials (RCTs).

Our findings must be balanced with the potential benefits of social media use, which include exposure to current events, communication over geographic barriers, and social inclusion for those who may be otherwise excluded in their day-to-day lives (eg, lesbian, bisexual, transgender, queer, and questioning youth). 2 A limitation of our study is that we measured overall time spent on social media; prior studies 30 - 32 have found that social media use may be positively or negatively associated with mental health depending on which platforms are used and how. Nevertheless, a number of interventions could lead to a reduction in time spent on social media by adolescents, while still allowing for the benefits of such use. The American Academy of Pediatrics has developed a Family Media Use Plan, which can be tailored to specific developmental phases and help parents set reasonable rules for digital media use. 2 Pediatricians and teachers are essential for promoting these plans, as well as helping parents identify problematic social media use in their children. 33 There is also evidence that interventions that promote media literacy, defined as “specific knowledge and skills that can help critical understanding and usage of the media,” 34 (p 455) counteract the harmful association of media use with behavioral health. 34 Also, there is an increasing movement to improve the design of social media platforms; a notable recent example is not displaying the number of “likes” that an Instagram post receives. 35 We believe that technology companies and regulators responsible for social media platforms should consider how these platforms can be designed to minimize risk of mental health problems.

Some researchers have raised concerns that studies on technology use and well-being are limited by publication bias. 36 We believe that this is a legitimate concern given that many studies on this topic, including the present study, are secondary analyses of data not collected for the purpose of studying social media. 36 There appears to be an urgent need for experimental research, specifically a priori registered RCTs that examine interventions designed to reduce social media use. Our study findings suggest a population-level association between social media use and mental health problems, and evidence from RCTs could build on this by examining changes in mental health as a result of changes in social media use. The existing observational study findings and at least 1 RCT in college students 37 appear to be sufficient to justify investment in these trials. In addition, RCTs may be valuable for developing clinical guidelines and informing regulatory policy for social media design.

Some limitations of this study should be noted. First, adolescents self-reported the exposure and outcome, which may inflate the observed associations. Second, we measured mental health problems with a self-report questionnaire rather than a diagnostic interview. Third, the validity of self-reported time spent on social media in the PATH study is unknown. Some research suggests that self-reported time on social media may exceed actual use 38 ; future studies should consider the use of digital trace data to capture actual time spent using social media. 39 Fourth, social media use continues to change rapidly over time; although our data were collected relatively recently, they may not reflect current trends. Fifth, although our study design mitigates the possibility of reverse causality, some residual confounding from imprecise measurement of prior mental health problems may have been present. Sixth, it remains possible that mental health problems are prospectively associated with social media use, but we could not examine this in the present study because of data limitations. Seventh, it is possible that the observed associations were an artifact of unmeasured confounding. Although we controlled for a number of potential confounders, there may be others, such as physical activity, that we were unable to include because of data limitations.

This study suggests that increased time spent on social media may be a risk factor for internalizing problems in adolescents. Future research should determine whether setting limits on daily social media use, increasing media literacy, and redesigning social media platforms are effective means of reducing the burden of mental health problems in this population.

Accepted for Publication: June 14, 2019.

Corresponding Author: Kira E. Riehm, MS, Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, 624 N Broadway, Baltimore, MD 21205 ( [email protected] ).

Published Online: September 11, 2019. doi:10.1001/jamapsychiatry.2019.2325

Author Contributions: Ms Riehm had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Riehm, Feder, Crum, Green, La Flair, Mojtabai.

Acquisition, analysis, or interpretation of data: Riehm, Feder, Tormohlen, Young, Green, Pacek, La Flair.

Drafting of the manuscript: Riehm, Feder, Pacek.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Riehm, Feder, Green, Pacek.

Administrative, technical, or material support: Green.

Supervision: Crum, Green, Mojtabai.

Conflict of Interest Disclosures: Dr Young reported receiving grants from the National Institute on Drug Abuse and the Brain and Behavior Research Foundation during the conduct of the study, receiving grants from Supernus Pharmaceuticals and Psychnostics LLC outside the submitted work, and receiving personal fees from University of Montana's American Indian/Alaska Native Clinical Translational Program. Dr Pacek reported receiving grants from the National Institute on Drug Abuse during the conduct of the study. No other disclosures were reported.

Funding/Support: Ms Riehm was supported by grant 5T32MH014592-39 from the National Institute of Mental Health Psychiatric Epidemiology Training Program (Peter Zandi, principal investigator) and by a doctoral foreign study award from the Canadian Institutes of Health Research. Dr Feder was supported by National Research and Service Award F31DA044699 from the National Institute on Drug Abuse. Ms Tormohlen was supported by grant T32DA007292 (Renee M. Johnson, principal investigator), Dr Young was supported by grant K23DA044288, and Dr Pacek was supported by grant K01DA043413 from the National Institute on Drug Abuse.

Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Register for email alerts with links to free full-text articles
Access PDFs of free articles
Manage your interests
Save searches and receive search alerts

Social Media Smarts: Navigating Sleep and Mental Health for K-12 Teachers and Parents

Presenter/s.

Event Details

Social media has received significant attention as a culprit for the ongoing youth mental health crisis, both on its own and indirectly by disrupting sleep and circadian rhythms. There are several emerging critical developments including US Surgeon General’s advisories on (1) the negative consequences of social media use and (2) the declining mental health and wellbeing of parents, as well as emerging policies in school districts banning or restricting smart phone use at school. In this webinar we will discuss emerging research on both negative and protective effects of social media use, ways in which screens interfere with sleep and mental health, and discuss how educators and parents can navigate the Digital Age.

Learning Goals:

Interpret ways in which social media use may both confer risk and be protective against mental illness and risky behavior in youth
Evaluate the evidence for the critical role of sleep in cognition and emotional processing
Critically evaluate the evidence that social media use and screen time reduces and disrupts sleep, and that insufficient sleep has been associated with depression, anxiety, behavioral problems and aggression

All Articles

Iowa State Researchers Find Cutting back on social media reduces anxiety, depression, loneliness

Posted Jun 15, 2023

Last month, the American Psychological Association and the U.S. Surgeon General both issued health advisories. Their concerns and recommendations for teens, parents and policymakers addressed a mounting body of research that shows two trends are intertwined.

Young people are using social media more, and their mental health is suffering.

Researchers at Iowa State University found a simple intervention could help. During a two-week experiment with 230 college students, half were asked to limit their social media usage to 30 minutes a day and received automated, daily reminders. They scored significantly lower for anxiety, depression, loneliness and fear of missing out at the end of the experiment compared to the control group.

They also scored higher for “positive affect,” which the researchers describe as “the tendency to experience positive emotions described with words such as ‘excited’ and ‘proud.’” Essentially, they had a brighter outlook on life.

“It surprised me to find that participants’ well-being did not only improve in one dimension but in all of them. I was excited to learn that such a simple intervention of sending a daily reminder can motivate people to change their behavior and improve their social media habits.” says Ella Faulhaber , a Ph.D. student in human-computer interaction and lead author of the paper .

The researchers found the psychological benefits from cutting back on social media extended to participants who sometimes exceeded the 30-minute time limit.

“The lesson here is, it’s not about being perfect but putting in effort, which makes a difference. I think self-limiting and paying attention are the secret ingredients, more so than the 30-minute benchmark,” Faulhaber states.

Douglas A. Gentile , co-author and distinguished professor of psychology , says their results fit with other research that’s grown out of kinesiology and health fields.

“Knowing how much time we spend on activities each day and making something countable makes it easier for people to change their behaviors,” he says, giving Fitbits and daily steps as an example.

Many of the participants in the ISU study commented that the first few days of cutting back were challenging. But after the initial push, one student felt more productive and in tune with life. Others shared that they were getting better sleep or spending more time with people in person.

Self-limiting May be More Practical

Gentile and Faulhaber point out other studies have investigated the effects of limiting or abstaining from social media. But many of the interventions require heavy supervision and deleting apps or using a special application to block or limit social media. Like rehab for someone who’s addicted to drugs, external accountability can help some users. But it also carries a higher risk of backfiring.

“When a perceived freedom is taken away, we start resisting,” says Gentile. He adds that eliminating social media also means losing some of the benefits it can bring, like connecting with friends and family.

Faulhaber says their study extends the current research on social media and provides a practical way for people to limit their use. For anyone looking to cut back, she recommends:

Create awareness. Set a timer or use a built-in wellness app to see how much time you spend on social media.
Give yourself grace. Recognize that it’s not easy to stick to a time limit. Social media apps are designed to keep you engaged.
Don’t give up. Limiting social media use over time has real benefits for your daily life.

The researchers say it’s also important to be mindful of how and when we use these platforms. Future research could further explore this, along with the long-term effects from limiting social media and what people do with the time they gain.

“We live in an age of anxiety. Lots of indicators show that anxiety, depression, loneliness are all getting worse, and that can make us feel helpless. But there are things we can do to manage our mental health and well-being,” says Gentile.

Paying more attention to how much time we spend on social media and setting measurable goals can help.

Jeong Eun Lee, assistant professor of human development and family studies, contributed to the paper.

Don’t Just Blame Social Media for Kids’ Poor Mental Health—Blame a Lack of Sleep

Schools have a powerful strategy at their disposal to help improve students’ mental health, one that doesn’t necessarily require banning cellphones : Help kids—especially adolescents—get more sleep.

There is a rich body of research showing that poor sleep leads to poor mental health, said Andrew Fuligni, a psychology professor and director of the Adolescent Development Lab at UCLA. And it’s a link that is getting overlooked in the current frenzy over cellphones and social media, he said.

“The evidence for sleep and mental health is much stronger than the evidence for social media and mental health,” he said. “Added to the mix, adolescents in the United States are getting less and less sleep over the last 15 years or so. I want to highlight that because this is not discussed as much in the national conversation about mental health as it should be.”

Fuligni made these remarks during a webinar about adolescent mental health hosted by the Frameworks Institute, a nonprofit organization that studies strategic communications around social issues.

That’s not to say that social media doesn’t affect students’ mental health, but there’s less research into the connection, and of the research that exists, the findings are mixed .

Meanwhile, a lack of sleep does more than hurt kids’ mental health—adolescents who don’t get enough sleep are more likely to have behavior and attention problems as well as higher risks of obesity, diabetes, and injury.

The vast majority of high schoolers are not getting the 8 to 10 hours of sleep a day recommended by the American Academy of Sleep Medicine.

Also, inequalities in which groups of students are getting better sleep quality exacerbate some of the educational inequalities that schools are already grappling with, Fuligni said.

Students from low-income households are more likely to have parents who work irregular schedules which can throw off sleep routines, he said, while students living in urban areas are more likely to have their sleep disrupted by noise and light pollution.

Sleep quality and consistency—such as whether a student’s sleep is getting interrupted throughout the night or whether a student is going to bed at the same time every night—is also important to adolescent mental health and brain development.

Districts consider changing school start times

To address the inadequate sleep today’s adolescents are getting, there has been a growing movement among some districts and states to push back start times for middle and high school to better align with natural shifts in adolescents’ sleep needs and patterns.

Adolescents are hard-wired to go to bed and sleep in later, which is why the American Academy of Pediatrics recommends that schools shouldn’t start before 8:30 a.m. for adolescents. An increasing amount of research shows that pushing back school start times can improve teens’ learning and well-being, according to the AAP.

Silhouette of a woman hanging from the hour arm of a clock set at 9.

But changing school start times is no easy task , said Kent Pekel, the superintendent of Rochester Public Schools in Minnesota. Three years ago, his district pushed back start times for middle and high school. Adjusting start times for a district that covers more than 200 square miles is challenging when taking into account the fact that some students have to get on the bus an hour and a half before school starts, he said.

The district worked with an independent sleep researcher to measure the effects of the schedule changes, Pekel said during the webinar.

“We found that we got benefits in the quality of sleep and the amount of sleep for the high school kids when we moved to that very nice, after 8 a.m. start time,” he said. “The problem was starting the elementary schools at 9:35 proved to be disastrous because we were missing prime learning time for little kids who wake up ready to learn.”

This year, the 17,500-student district has reshuffled start times again and managed to work out a schedule with elementary and high schools starting at 8 a.m., and middle schools starting at 8:30.

Encouraging healthy sleep habits for students

However, simply giving students more time to sleep in doesn’t mean they will automatically get good sleep. To do that, they need better sleep routines and environments—regular bedtimes, quiet and dark spaces, and no screens close to bedtime. While schools don’t have direct control over kids’ sleeping environments, they can help educate families about creating good sleep environments, said Fuligni.

“Physiologically, we are incredibly sensitive to light, to routine, to noise, to hubbub, to all of the things going on in the home,” he said. “Just telling adolescents to go to sleep earlier is not going to work. We need to educate folks how to set up a sleep-sensitive environment: having an agreement within the family about when is a reasonable time to go to bed, keeping phones outside of the room. Parents need to be thinking about things they’re doing in the home, are they staying up too late?”

Access to cellphones can hurt the quality of kids’ sleep if kids are skipping out on sleep to scroll on social media. The content they see on social media, as well as the light from the screen, also stimulates the brain and makes it harder to fall asleep.

Messaging the importance of sleep in a way adolescents will be receptive to has proven to be somewhat tricky, said Pekel.

A straightforward campaign on the benefits of sleep that his district tried initially appeared to fall flat with students. Pekel hopes that a new approach—discussing the benefits of sleep as part of a broader education initiative about wellness—will do better.

There’s research to suggest that reframing how sleep will benefit students will work, said Nat Kendall-Taylor, the CEO of Frameworks Institute and an expert in strategic communications, during the webinar. Trying too hard to persuade adolescents to do something can backfire, he said.

“But explaining the role that sleep plays in the larger conversation around being and feeling well, our research shows is a much more effective strategy to build understanding and influence behavior,” he said. “This move from persuasion to explanation [is] a really powerful strategy.”

Sign Up for The Savvy Principal

Edweek top school jobs.

Closeup photo of a white adolescent exhaling smoke from an e-cigarette

Sign Up & Sign In

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Publications
Account settings

The PMC website is updating on October 15, 2024. Learn More or Try it out now .

Advanced Search
Journal List
Front Psychol

Social Media Use and Mental Health and Well-Being Among Adolescents – A Scoping Review

Viktor schønning.

1 Department of Health Promotion, Norwegian Institute of Public Health, Bergen, Norway

Gunnhild Johnsen Hjetland

Leif edvard aarø, jens christoffer skogen.

2 Alcohol and Drug Research Western Norway, Stavanger University Hospital, Stavanger, Norway

3 Faculty of Health Sciences, University of Stavanger, Stavanger, Norway

Associated Data

Introduction: Social media has become an integrated part of daily life, with an estimated 3 billion social media users worldwide. Adolescents and young adults are the most active users of social media. Research on social media has grown rapidly, with the potential association of social media use and mental health and well-being becoming a polarized and much-studied subject. The current body of knowledge on this theme is complex and difficult-to-follow. The current paper presents a scoping review of the published literature in the research field of social media use and its association with mental health and well-being among adolescents.

Methods and Analysis: First, relevant databases were searched for eligible studies with a vast range of relevant search terms for social media use and mental health and well-being over the past five years. Identified studies were screened thoroughly and included or excluded based on prior established criteria. Data from the included studies were extracted and summarized according to the previously published study protocol.

Results: Among the 79 studies that met our inclusion criteria, the vast majority (94%) were quantitative, with a cross-sectional design (57%) being the most common study design. Several studies focused on different aspects of mental health, with depression (29%) being the most studied aspect. Almost half of the included studies focused on use of non-specified social network sites (43%). Of specified social media, Facebook (39%) was the most studied social network site. The most used approach to measuring social media use was frequency and duration (56%). Participants of both genders were included in most studies (92%) but seldom examined as an explanatory variable. 77% of the included studies had social media use as the independent variable.

Conclusion: The findings from the current scoping review revealed that about 3/4 of the included studies focused on social media and some aspect of pathology. Focus on the potential association between social media use and positive outcomes seems to be rarer in the current literature. Amongst the included studies, few separated between different forms of (inter)actions on social media, which are likely to be differentially associated with mental health and well-being outcomes.

In just a few decades, the use of social media have permeated most areas of our society. For adolescents, social media play a particularly large part in their lives as indicated by their extensive use of several different social media platforms ( Ofcom, 2018 ). Furthermore, the use of social media and types of platforms offered have increased at such a speed that there is reason to believe that scientific knowledge about social media in relation to adolescents’ health and well-being is scattered and incomplete ( Orben, 2020 ). Nevertheless, research findings indicating the potential negative effects of social media on mental health and well-being are frequently reported in traditional media (newspapers, radio, TV) ( Bell et al., 2015 ). Within the scientific community, however, there are ongoing debates regarding the impact and relevance of social media in relation to mental health and well-being. For instance, Twenge and Campbell (2019) stated that use of digital technology and social media have a negative impact on well-being, while Orben and Przybylski (2019) argued that the association between digital technology use and adolescent well-being is so small that it is more or less inconsequential. Research on social media use is a new focus area, and it is therefore important to get an overview of the studies performed to date, and describe the subject matter studies have investigated in relation to the effect of social media use on adolescents mental health and well-being. Also, research gaps in this emerging research field is important to highlight as it may guide future research in new and meritorious directions. A scoping review is therefore deemed necessary to provide a foundation for further research, which in time will provide a knowledge base for policymaking and service delivery.

This scoping review will help provide an overall understanding of the main foci of research within the field of social media and mental health and well-being among adolescents, as well as the type of data sources and research instruments used so far. Furthermore, we aim to highlight potential gaps in the research literature ( Arksey and O’Malley, 2005 ). Even though a large number of studies on social media use and mental health with different vantage points has been conducted over the last decade, we are not aware of any broad-sweeping scoping review covering this area.

This scoping review aims to give an overview of the main research questions that have been focused on with regard to use of social media among adolescents in relation to mental health and well-being. Both quantitative and qualitative studies are of interest. Three specific secondary research questions will be addressed and together with the main research question serve as a template for organizing the results:

• Which aspects of mental health and well-being have been the focus or foci of research so far?
• Has the research focused on different research aims across gender, ethnicity, socio-economic status, geographic location? What kind of findings are reported across these groups?
• Organize and describe the main sources of evidence related to social media that have been used in the studies identified.

Defining Adolescence and Social Media

In the present review, adolescents are defined as those between 13 and 19 years of age. We chose the mean age of 13 as our lower limit as nearly all social media services require users to be at least 13 years of age to access and use their services ( Childnet International, 2018 ). All pertinent studies which present results relevant for this age range is within the scope of this review. For social media we used the following definition by Kietzmann et al. (2011 , p. 1): “Social media employ mobile and web-based technologies to create highly interactive platforms via which individuals and communities share, co-create, discuss, and modify user-generated content.” We also employed the typology described by Kaplan and Haenlein’s classification scheme across two axes: level of self-presentation and social presence/media richness ( Kaplan and Haenlein, 2010 ). The current scoping review adheres to guidelines and recommendations stated by Tricco et al. (2018) .

See protocol for further details about the definitions used ( Schønning et al., 2020 ).

Data Sources and Search Strategy

A literature search was performed in OVID Medline, OVID Embase, OVID PsycINFO, Sociological Abstracts (proquest), Social Services Abstracts (proquest), ERIC (proquest), and CINAHL. The search strategy combined search terms for adolescents, social media and mental health or wellbeing. The database-controlled vocabulary was used for searching subject headings, and a large spectrum of synonyms with appropriate truncations was used for searching title, abstract, and author keywords. A filter for observational studies was applied to limit the results. The search was also limited to publications from 2014 to current. The search strategy was translated between each database. An example of full strategy for Embase is attached as Supplementary Material .

Study Selection: Exclusion and Inclusion Criteria

The exclusion and inclusion criteria are detailed in the protocol ( Schønning et al., 2020 ). Briefly, we included English language peer-reviewed quantitative- or qualitative papers or systematic reviews published within the last 5 years with an explicit focus on mental health/well-being and social media. Non-empirical studies, intervention studies, clinical studies and publications not peer-reviewed were excluded. Intervention studies and clinical studies were excluded as we sought to not introduce too much heterogeneity in design and our focus was on observational studies. The criteria used for study selection was part of an iterative process which was described in detail in the protocol ( Schønning et al., 2020 ). As per the study protocol ( Schønning et al., 2020 ), and in line with scoping review guidelines ( Peters et al., 2015 , 2017 ; Tricco et al., 2018 ), we did not assess methodological quality or risk of bias of the included studies.

The selection process is illustrated by a flow-chart indicating the stages from unsorted search results to the number of included studies (see Figure 1 ). Study selection was accomplished and organized using the Rayyan QCRI software 1 . The inclusion and exclusion process was performed independently by VS and JCS. The interrater agreement was κ = 0.87, indicating satisfactory agreement.

An external file that holds a picture, illustration, etc.
Object name is fpsyg-11-01949-g001.jpg

Flowchart of exclusion process from unsorted results to included studies.

Data Extraction and Organization

Details of the data extracted is described in the protocol. Three types of information were extracted, bibliographic information, information about study design and subject matter information. Subject matter information included aim of study, how social media and mental health/well-being was measured, and main findings of the study.

Visualization of Words From the Titles of the Included Studies

The most frequently occurring words and bigrams in the titles of the included studies are presented in Figures 2 , ,3. 3 . The following procedure was used to generate Figure 1 : First, a text file containing all titles were imported into R as a data frame ( R Core Team, 2014 ). The data frame was processed using the “tidy text”-package with required additional packages ( Silge and Robinson, 2016 ). Second, numbers and commonly used words with little inherent meaning (so called “stop words,” such as “and,” “of,” and “in”), were removed from the data frame using the three available lexicons in the “tidy-text”-package ( Silge and Robinson, 2016 ). Furthermore, variations of “adolescents” (e.g., “adolescent,” “adolescence,” and “adolescents”) and “social media” (e.g., “social media,” “social networking,” “online social networks”) were removed from the data frame. Third, the resulting data frame was sorted based on frequency of unique words, and words occurring only once were removed. The final data frame is presented as a word cloud in Figure 1 ( N = 113). The same procedure as described above was employed to generate commonly occurring bigrams (two words occurring adjacent to each other), but without removing bigrams occurring only once ( N = 231). The word clouds were generated using the “wordcloud2”-package in R ( Lang and Chien, 2018 ). For Figure 1 , shades of blue indicate word frequencies >2 and green a frequency of 2. For Figure 2 , shades of blue indicate bigram frequencies of >1 and green a frequency of 1.

An external file that holds a picture, illustration, etc.
Object name is fpsyg-11-01949-g002.jpg

Word cloud from the titles of the included studies. Most frequent words, excluding variations of “adolescence” and “social media.” N = 113. Shades of blue indicate word frequencies >2 and green a frequency of 2. The size of each word is indicative of its relative frequency of occurrence.

An external file that holds a picture, illustration, etc.
Object name is fpsyg-11-01949-g003.jpg

Word cloud from the titles of the included studies. Bigrams from the titles of the included studies, excluding variations of “adolescence” and “social media.” N = 231. Shades of blue indicate bigram frequencies of >1 and green a frequency of 1. The size of each bigram is indicative of its relative frequency of occurrence.

Characteristics of the Included Studies

Of 7927 unique studies, 79 (1%) met our inclusion criteria ( Aboujaoude et al., 2015 ; Banjanin et al., 2015 ; Banyai et al., 2017 ; Barry et al., 2017 ; Best et al., 2014 , 2015 ; Booker et al., 2018 ; Bourgeois et al., 2014 ; Boyle et al., 2016 ; Brunborg et al., 2017 ; Burnette et al., 2017 ; Colder Carras et al., 2017 ; Critchlow et al., 2019 ; Cross et al., 2015 ; Curtis et al., 2018 ; de Lenne et al., 2018 ; de Vries et al., 2016 ; Erfani and Abedin, 2018 ; Erreygers et al., 2018 ; Fahy et al., 2016 ; Ferguson et al., 2014 ; Fisher et al., 2016 ; Foerster and Roosli, 2017 ; Foody et al., 2017 ; Fredrick and Demaray, 2018 ; Frison and Eggermont, 2016 , 2017 ; Geusens and Beullens, 2017 , 2018 ; Hamm et al., 2015 ; Hanprathet et al., 2015 ; Harbard et al., 2016 ; Hase et al., 2015 ; Holfeld and Mishna, 2019 ; Houghton et al., 2018 ; Jafarpour et al., 2017 ; John et al., 2018 ; Kim et al., 2019 ; Kim, 2017 ; Koo et al., 2015 ; Lai et al., 2018 ; Larm et al., 2017 , 2019 ; Marchant et al., 2017 ; Marengo et al., 2018 ; Marques et al., 2018 ; Meier and Gray, 2014 ; Memon et al., 2018 ; Merelle et al., 2017 ; Neira and Barber, 2014 ; Nesi et al., 2017a , b ; Niu et al., 2018 ; Nursalam et al., 2018 ; Oberst et al., 2017 ; O’Connor et al., 2014 ; O’Reilly et al., 2018 ; Przybylski and Bowes, 2017 ; Przybylski and Weinstein, 2017 ; Richards et al., 2015 ; Rousseau et al., 2017 ; Salmela-Aro et al., 2017 ; Sampasa-Kanyinga and Chaput, 2016 ; Sampasa-Kanyinga and Lewis, 2015 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Settanni et al., 2018 ; Spears et al., 2015 ; Throuvala et al., 2019 ; Tiggemann and Slater, 2017 ; Tseng and Yang, 2015 ; Twenge and Campbell, 2019 ; Twenge et al., 2018 ; van den Eijnden et al., 2018 ; Wang et al., 2018 ; Wartberg et al., 2018 ; Wolke et al., 2017 ; Woods and Scott, 2016 ; Yan et al., 2017 ). Among the included studies, 74 (94%) are quantitative ( Aboujaoude et al., 2015 ; Banjanin et al., 2015 ; Banyai et al., 2017 ; Barry et al., 2017 ; Best et al., 2014 ; Booker et al., 2018 ; Bourgeois et al., 2014 ; Boyle et al., 2016 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Critchlow et al., 2019 ; Cross et al., 2015 ; Curtis et al., 2018 ; de Lenne et al., 2018 ; de Vries et al., 2016 ; Erfani and Abedin, 2018 ; Erreygers et al., 2018 ; Fahy et al., 2016 ; Ferguson et al., 2014 ; Fisher et al., 2016 ; Foerster and Roosli, 2017 ; Foody et al., 2017 ; Fredrick and Demaray, 2018 ; Frison and Eggermont, 2016 , 2017 ; Geusens and Beullens, 2017 , 2018 ; Hamm et al., 2015 ; Hanprathet et al., 2015 ; Harbard et al., 2016 ; Hase et al., 2015 ; Houghton et al., 2018 ; Jafarpour et al., 2017 ; John et al., 2018 ; Kim et al., 2019 ; Kim, 2017 ; Koo et al., 2015 ; Lai et al., 2018 ; Larm et al., 2017 , 2019 ; Marchant et al., 2017 ; Marengo et al., 2018 ; Marques et al., 2018 ; Meier and Gray, 2014 ; Memon et al., 2018 ; Merelle et al., 2017 ; Neira and Barber, 2014 ; Nesi et al., 2017a , b ; Niu et al., 2018 ; Nursalam et al., 2018 ; Oberst et al., 2017 ; O’Connor et al., 2014 ; Przybylski and Bowes, 2017 ; Przybylski and Weinstein, 2017 ; Richards et al., 2015 ; Rousseau et al., 2017 ; Salmela-Aro et al., 2017 ; Sampasa-Kanyinga and Chaput, 2016 ; Sampasa-Kanyinga and Lewis, 2015 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Settanni et al., 2018 ; Spears et al., 2015 ; Tiggemann and Slater, 2017 ; Tseng and Yang, 2015 ; Twenge and Campbell, 2019 ; Twenge et al., 2018 ; van den Eijnden et al., 2018 ; Wang et al., 2018 ; Wartberg et al., 2018 ; Wolke et al., 2017 ; Woods and Scott, 2016 ; Yan et al., 2017 ), three are qualitative ( O’Reilly et al., 2018 ; Burnette et al., 2017 ; Throuvala et al., 2019 ), and two use mixed methods ( Best et al., 2015 ; Holfeld and Mishna, 2019 ) (see Supplementary Tables 1 , 2 in the Supplementary Material for additional details extracted from all included studies). In relation to study design, 45 (57%) used a cross-sectional design ( Bourgeois et al., 2014 ; Ferguson et al., 2014 ; Meier and Gray, 2014 ; Neira and Barber, 2014 ; O’Connor et al., 2014 ; Banjanin et al., 2015 ; Hanprathet et al., 2015 ; Hase et al., 2015 ; Koo et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Spears et al., 2015 ; Tseng and Yang, 2015 ; Frison and Eggermont, 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Woods and Scott, 2016 ; Banyai et al., 2017 ; Barry et al., 2017 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Larm et al., 2017 , 2019 ; Merelle et al., 2017 ; Oberst et al., 2017 ; Przybylski and Bowes, 2017 ; Przybylski and Weinstein, 2017 ; Tiggemann and Slater, 2017 ; Wolke et al., 2017 ; Yan et al., 2017 ; de Lenne et al., 2018 ; Erreygers et al., 2018 ; Fredrick and Demaray, 2018 ; Geusens and Beullens, 2018 ; Lai et al., 2018 ; Marengo et al., 2018 ; Marques et al., 2018 ; Niu et al., 2018 ; Nursalam et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Settanni et al., 2018 ; Wang et al., 2018 ; Wartberg et al., 2018 ; Critchlow et al., 2019 ; Kim et al., 2019 ; Twenge and Campbell, 2019 ), 17 used a longitudinal design ( Cross et al., 2015 ; Boyle et al., 2016 ; de Vries et al., 2016 ; Fahy et al., 2016 ; Frison and Eggermont, 2016 ; Harbard et al., 2016 ; Foerster and Roosli, 2017 ; Geusens and Beullens, 2017 ; Kim, 2017 ; Nesi et al., 2017a , b ; Rousseau et al., 2017 ; Salmela-Aro et al., 2017 ; Booker et al., 2018 ; Houghton et al., 2018 ; van den Eijnden et al., 2018 ; Holfeld and Mishna, 2019 ), seven were systematic reviews ( Aboujaoude et al., 2015 ; Best et al., 2015 ; Fisher et al., 2016 ; Marchant et al., 2017 ; Erfani and Abedin, 2018 ; John et al., 2018 ; Memon et al., 2018 ), two were meta-analyses ( Foody et al., 2017 : Curtis et al., 2018 ), one was a causal-comparative study ( Jafarpour et al., 2017 ), one was a review article ( Richards et al., 2015 ), one used a time-lag design ( Twenge et al., 2018 ), one was a scoping review ( Hamm et al., 2015 ), three used a focus-group interview design ( Burnette et al., 2017 ; O’Reilly et al., 2018 ; Throuvala et al., 2019 ), and one study used a combined survey and focus-group design ( Best et al., 2014 ).

The most common study settings were schools [ N = 42 (54%)] ( Best et al., 2014 ; Bourgeois et al., 2014 ; Meier and Gray, 2014 ; Neira and Barber, 2014 ; O’Connor et al., 2014 ; Banjanin et al., 2015 ; Hanprathet et al., 2015 ; Hase et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Frison and Eggermont, 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Woods and Scott, 2016 ; Banyai et al., 2017 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Foerster and Roosli, 2017 ; Geusens and Beullens, 2017 , 2018 ; Kim, 2017 ; Larm et al., 2017 , 2019 ; Merelle et al., 2017 ; Nesi et al., 2017a , b ; Przybylski and Bowes, 2017 ; Rousseau et al., 2017 ; Salmela-Aro et al., 2017 ; Tiggemann and Slater, 2017 ; de Lenne et al., 2018 ; Fredrick and Demaray, 2018 ; Houghton et al., 2018 ; Lai et al., 2018 ; Marengo et al., 2018 ; Niu et al., 2018 ; Nursalam et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Settanni et al., 2018 ; van den Eijnden et al., 2018 ; Wang et al., 2018 ; Holfeld and Mishna, 2019 ; Kim et al., 2019 ). Fourteen of the included studies were based on data from a home setting ( Cross et al., 2015 ; Koo et al., 2015 ; Spears et al., 2015 ; Boyle et al., 2016 ; de Vries et al., 2016 ; Harbard et al., 2016 ; Barry et al., 2017 ; Frison and Eggermont, 2017 ; Oberst et al., 2017 ; Yan et al., 2017 ; Booker et al., 2018 ; Marques et al., 2018 ; Wartberg et al., 2018 ; Critchlow et al., 2019 ). Eleven publications were reviews or meta-analyses and included primary studies from different settings ( Aboujaoude et al., 2015 ; Best et al., 2015 ; Hamm et al., 2015 ; Richards et al., 2015 ; Fisher et al., 2016 ; Foody et al., 2017 ; Marchant et al., 2017 ; Curtis et al., 2018 ; Erfani and Abedin, 2018 ; John et al., 2018 ; Memon et al., 2018 ). One study used both a home and school setting ( Erreygers et al., 2018 ), and 11 (14%) of the included studies did not mention the study setting for data collection ( Ferguson et al., 2014 ; Tseng and Yang, 2015 ; Fahy et al., 2016 ; Burnette et al., 2017 ; Jafarpour et al., 2017 ; Przybylski and Weinstein, 2017 ; Wolke et al., 2017 ; O’Reilly et al., 2018 ; Twenge et al., 2018 ; Throuvala et al., 2019 ; Twenge and Campbell, 2019 ).

Mental Health Foci of Included Studies

For a visual overview of the mental health foci of the included studies see Figures 2 , ,3. 3 . Most studies had a focus on different negative aspects of mental health, as evident from the frequently used terms in Figures 2 , ,3. 3 . The most studied aspect was depression, with 23 (29%) studies examining the relationship between social media use and depressive symptoms ( Ferguson et al., 2014 ; Neira and Barber, 2014 ; O’Connor et al., 2014 ; Banjanin et al., 2015 ; Richards et al., 2015 ; Spears et al., 2015 ; Tseng and Yang, 2015 ; Fahy et al., 2016 ; Frison and Eggermont, 2016 , 2017 ; Woods and Scott, 2016 ; Banyai et al., 2017 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Larm et al., 2017 ; Nesi et al., 2017a ; Salmela-Aro et al., 2017 ; Fredrick and Demaray, 2018 ; Houghton et al., 2018 ; Niu et al., 2018 ; Twenge et al., 2018 ; Wang et al., 2018 ; Wartberg et al., 2018 ). Twenty of the included studies focused on different aspects of good mental health, such as well-being, happiness, or quality of life ( Best et al., 2014 , 2015 ; Bourgeois et al., 2014 ; Ferguson et al., 2014 ; Cross et al., 2015 ; Koo et al., 2015 ; Richards et al., 2015 ; Spears et al., 2015 ; Fahy et al., 2016 ; Foerster and Roosli, 2017 ; Przybylski and Bowes, 2017 ; Przybylski and Weinstein, 2017 ; Yan et al., 2017 ; Booker et al., 2018 ; de Lenne et al., 2018 ; Erfani and Abedin, 2018 ; Erreygers et al., 2018 ; Lai et al., 2018 ; van den Eijnden et al., 2018 ; Twenge and Campbell, 2019 ). Nineteen studies had a more broad-stroke approach, and covered general mental health or psychiatric problems ( Aboujaoude et al., 2015 ; Hanprathet et al., 2015 ; Hase et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Spears et al., 2015 ; Fisher et al., 2016 ; Barry et al., 2017 ; Jafarpour et al., 2017 ; Kim, 2017 ; Merelle et al., 2017 ; Oberst et al., 2017 ; Wolke et al., 2017 ; Marengo et al., 2018 ; Marques et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Holfeld and Mishna, 2019 ; Kim et al., 2019 ; Larm et al., 2019 ). Eight studies examined the link between social media use and body dissatisfaction and eating disorder symptoms ( Ferguson et al., 2014 ; Meier and Gray, 2014 ; de Vries et al., 2016 ; Burnette et al., 2017 ; Rousseau et al., 2017 ; Tiggemann and Slater, 2017 ; Marengo et al., 2018 ; Wartberg et al., 2018 ). Anxiety was the focus of seven studies ( O’Connor et al., 2014 ; Koo et al., 2015 ; Spears et al., 2015 ; Fahy et al., 2016 ; Woods and Scott, 2016 ; Colder Carras et al., 2017 ; Yan et al., 2017 ), and 13 studies included a focus on the relationship between alcohol use and social media use ( O’Connor et al., 2014 ; Boyle et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Brunborg et al., 2017 ; Geusens and Beullens, 2017 , 2018 ; Larm et al., 2017 ; Merelle et al., 2017 ; Nesi et al., 2017b ; Curtis et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Critchlow et al., 2019 ; Kim et al., 2019 ). Seven studies examined the effect of social media use on sleep ( Harbard et al., 2016 ; Woods and Scott, 2016 ; Yan et al., 2017 ; Nursalam et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Larm et al., 2019 ). Five studies saw how drug use and social media use affected each other ( O’Connor et al., 2014 ; Merelle et al., 2017 ; Sampasa-Kanyinga et al., 2018 ; Kim et al., 2019 ; Larm et al., 2019 ). Self-harm and suicidal behavior was the focus of eleven studies ( O’Connor et al., 2014 ; Sampasa-Kanyinga and Lewis, 2015 ; Tseng and Yang, 2015 ; Kim, 2017 ; Marchant et al., 2017 ; Merelle et al., 2017 ; Fredrick and Demaray, 2018 ; John et al., 2018 ; Memon et al., 2018 ; Twenge et al., 2018 ; Kim et al., 2019 ). Other areas of focus other than the aforementioned are loneliness, self-esteem, fear of missing out and other non-pathological measures ( Neira and Barber, 2014 ; Banyai et al., 2017 ; Barry et al., 2017 ; Colder Carras et al., 2017 ).

Social Media Metrics of Included Studies

The studies included in the current scoping review often focus on specific, widely used, social media and social networking services, such as 31 (39%) studies focusing on Facebook ( Bourgeois et al., 2014 ; Meier and Gray, 2014 ; Banjanin et al., 2015 ; Cross et al., 2015 ; Hanprathet et al., 2015 ; Richards et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Spears et al., 2015 ; Boyle et al., 2016 ; de Vries et al., 2016 ; Frison and Eggermont, 2016 ; Harbard et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Banyai et al., 2017 ; Barry et al., 2017 ; Brunborg et al., 2017 ; Larm et al., 2017 ; Merelle et al., 2017 ; Nesi et al., 2017a , b ; Rousseau et al., 2017 ; Tiggemann and Slater, 2017 ; Booker et al., 2018 ; de Lenne et al., 2018 ; Lai et al., 2018 ; Marengo et al., 2018 ; Marques et al., 2018 ; Memon et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Settanni et al., 2018 ; Twenge et al., 2018 ), 11 on Instagram ( Sampasa-Kanyinga and Lewis, 2015 ; Boyle et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Barry et al., 2017 ; Brunborg et al., 2017 ; Frison and Eggermont, 2017 ; Nesi et al., 2017a ; Marengo et al., 2018 ; Memon et al., 2018 ; Sampasa-Kanyinga et al., 2018 ), 11 including Twitter ( Richards et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Spears et al., 2015 ; Harbard et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Barry et al., 2017 ; Brunborg et al., 2017 ; Merelle et al., 2017 ; Nesi et al., 2017a ; Memon et al., 2018 ; Sampasa-Kanyinga et al., 2018 ), and five studies asking about Snapchat ( Boyle et al., 2016 ; Barry et al., 2017 ; Brunborg et al., 2017 ; Nesi et al., 2017a ; Marengo et al., 2018 ). Eight studies mentioned Myspace ( Richards et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; de Vries et al., 2016 ; Harbard et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Larm et al., 2017 ; Booker et al., 2018 ; Sampasa-Kanyinga et al., 2018 ) and two asked about Tumblr ( Barry et al., 2017 ; Nesi et al., 2017a ). Other media such as Skype ( Merelle et al., 2017 ), Youtube ( Richards et al., 2015 ), WhatsApp ( Brunborg et al., 2017 ), Ping ( Merelle et al., 2017 ), Bebo ( Booker et al., 2018 ), Hyves ( de Vries et al., 2016 ), Kik ( Brunborg et al., 2017 ), Ask ( Brunborg et al., 2017 ), and Qzone ( Niu et al., 2018 ) were only included in one study each.

Almost half ( n = 34, 43%) of the included studies focus on use of social network sites or online communication in general, without specifying particular social media sites, leaving this up to the study participants to decide ( Best et al., 2014 , 2015 ; Ferguson et al., 2014 ; Neira and Barber, 2014 ; O’Connor et al., 2014 ; Koo et al., 2015 ; Tseng and Yang, 2015 ; Fahy et al., 2016 ; Woods and Scott, 2016 ; Burnette et al., 2017 ; Colder Carras et al., 2017 ; Foerster and Roosli, 2017 ; Foody et al., 2017 ; Geusens and Beullens, 2017 , 2018 ; Jafarpour et al., 2017 ; Kim, 2017 ; Marchant et al., 2017 ; Oberst et al., 2017 ; Przybylski and Weinstein, 2017 ; Salmela-Aro et al., 2017 ; Yan et al., 2017 ; Curtis et al., 2018 ; Erfani and Abedin, 2018 ; Erreygers et al., 2018 ; Nursalam et al., 2018 ; Scott and Woods, 2018 ; van den Eijnden et al., 2018 ; Wartberg et al., 2018 ; Critchlow et al., 2019 ; Holfeld and Mishna, 2019 ; Larm et al., 2019 ; Throuvala et al., 2019 ; Twenge and Campbell, 2019 ). Seven of the included studies examined the relationship between virtual game worlds or socially oriented video games and mental health ( Ferguson et al., 2014 ; Best et al., 2015 ; Spears et al., 2015 ; Yan et al., 2017 ; van den Eijnden et al., 2018 ; Larm et al., 2019 ; Twenge and Campbell, 2019 ).

In the 79 studies included in this scoping review, several approaches to measuring social media use are utilized. The combination of frequency and duration of social media use is by far the most used measurement of social media use, and 44 (56%) of the included studies collected data on these parameters ( Ferguson et al., 2014 ; Meier and Gray, 2014 ; Neira and Barber, 2014 ; Banjanin et al., 2015 ; Best et al., 2015 ; Hanprathet et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Tseng and Yang, 2015 ; Boyle et al., 2016 ; de Vries et al., 2016 ; Frison and Eggermont, 2016 , 2017 ; Harbard et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Woods and Scott, 2016 ; Banyai et al., 2017 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Foerster and Roosli, 2017 ; Jafarpour et al., 2017 ; Kim, 2017 ; Larm et al., 2017 , 2019 ; Merelle et al., 2017 ; Nesi et al., 2017b ; Oberst et al., 2017 ; Rousseau et al., 2017 ; Tiggemann and Slater, 2017 ; Yan et al., 2017 ; Booker et al., 2018 ; de Lenne et al., 2018 ; Erreygers et al., 2018 ; Houghton et al., 2018 ; Lai et al., 2018 ; Marengo et al., 2018 ; Marques et al., 2018 ; Niu et al., 2018 ; Nursalam et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Settanni et al., 2018 ; Twenge et al., 2018 ; van den Eijnden et al., 2018 ; Twenge and Campbell, 2019 ). Eight studies focused on the relationship between social media addiction or excessive use and mental health ( Banjanin et al., 2015 ; Tseng and Yang, 2015 ; Banyai et al., 2017 ; Merelle et al., 2017 ; Nursalam et al., 2018 ; Settanni et al., 2018 ; Wang et al., 2018 ). Bergen Social Media Addiction Scale is a commonly used questionnaire amongst the included studies ( Hanprathet et al., 2015 ; Banyai et al., 2017 ; Settanni et al., 2018 ). Seven studies asked about various specific actions on social media, such as liking or commenting on photos, posting something or participating in a discussion ( Meier and Gray, 2014 ; Koo et al., 2015 ; Nesi et al., 2017b ; Geusens and Beullens, 2018 ; Marques et al., 2018 ; van den Eijnden et al., 2018 ; Critchlow et al., 2019 ).

Five studies had a specific and sole focus on the link between social media use and alcohol, and examined how various alcohol-related social media use affected alcohol intake ( Boyle et al., 2016 ; Geusens and Beullens, 2017 , 2018 ; Nesi et al., 2017b ; Critchlow et al., 2019 ). Some studies had a more theory-based focus and investigated themes such as peer comparison, social media intrusion or pro-social behavior on social media and its effect on mental health ( Bourgeois et al., 2014 ; Rousseau et al., 2017 ; de Lenne et al., 2018 ). One of the included studies looked into night-time specific social media use ( Scott and Woods, 2018 ) and one looked into pre-bedtime social media behavior ( Harbard et al., 2016 ) to study the link between this use and sleep.

Amongst the 79 included studies, only six (8%) studies had participants of one gender ( Ferguson et al., 2014 ; Meier and Gray, 2014 ; Best et al., 2015 ; Burnette et al., 2017 ; Jafarpour et al., 2017 ; Tiggemann and Slater, 2017 ). Sixteen studies (20%) did not mention the gender distribution of the participants ( Aboujaoude et al., 2015 ; Best et al., 2015 ; Hamm et al., 2015 ; Richards et al., 2015 ; Fisher et al., 2016 ; Woods and Scott, 2016 ; Foody et al., 2017 ; Marchant et al., 2017 ; Przybylski and Weinstein, 2017 ; Curtis et al., 2018 ; Erfani and Abedin, 2018 ; John et al., 2018 ; Memon et al., 2018 ; O’Reilly et al., 2018 ; Twenge et al., 2018 ; Twenge and Campbell, 2019 ). Several of these were meta-analyses or reviews ( Aboujaoude et al., 2015 ; Best et al., 2014 ; Curtis et al., 2018 ; Foody et al., 2017 ; John et al., 2018 ; Erfani and Abedin, 2018 ; Wallaroo, 2020 ). The studies that included both genders as participants generally had a well-balanced gender distribution with no gender below 40% of the participants. Eight of the studies did not report gender-specific results ( Harbard et al., 2016 ; Nesi et al., 2017b ; Curtis et al., 2018 ; de Lenne et al., 2018 ; Niu et al., 2018 ; Nursalam et al., 2018 ; Wang et al., 2018 ; Twenge and Campbell, 2019 ). Of the included studies, gender was seldom examined as an explanatory variable, and other sociodemographic variables (e.g., ethnicity, socioeconomic status) were not included at all.

Implicit Causation Based on Direction of Association

Sixty-one (77%) of the included studies has social media use as the independent variable and some of the mentioned measurements of mental health as the dependent variable ( Aboujaoude et al., 2015 ; Banjanin et al., 2015 ; Banyai et al., 2017 ; Barry et al., 2017 ; Best et al., 2014 ; Booker et al., 2018 ; Bourgeois et al., 2014 ; Boyle et al., 2016 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Critchlow et al., 2019 ; Cross et al., 2015 ; Curtis et al., 2018 ; de Lenne et al., 2018 ; de Vries et al., 2016 ; Erfani and Abedin, 2018 ; Fahy et al., 2016 ; Fisher et al., 2016 ; Foerster and Roosli, 2017 ; Fredrick and Demaray, 2018 ; Frison and Eggermont, 2016 ; Geusens and Beullens, 2018 ; Hamm et al., 2015 ; Hanprathet et al., 2015 ; Harbard et al., 2016 ; Hase et al., 2015 ; Holfeld and Mishna, 2019 ; Jafarpour et al., 2017 ; John et al., 2018 ; Kim et al., 2019 ; Kim, 2017 ; Lai et al., 2018 ; Larm et al., 2017 , 2019 ; Marengo et al., 2018 ; Marques et al., 2018 ; Meier and Gray, 2014 ; Memon et al., 2018 ; Neira and Barber, 2014 ; Nesi et al., 2017b ; Niu et al., 2018 ; Nursalam et al., 2018 ; O’Connor et al., 2014 ; O’Reilly et al., 2018 ; Przybylski and Bowes, 2017 ; Przybylski and Weinstein, 2017 ; Richards et al., 2015 ; Sampasa-Kanyinga and Chaput, 2016 ; Sampasa-Kanyinga and Lewis, 2015 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Spears et al., 2015 ; Tseng and Yang, 2015 ; Twenge and Campbell, 2019 ; Twenge et al., 2018 ; van den Eijnden et al., 2018 ; Wang et al., 2018 ; Wartberg et al., 2018 ; Wolke et al., 2017 ; Woods and Scott, 2016 ; Yan et al., 2017 ). Most of the included studies hypothesize social media use pattern will affect youth mental health in certain ways. The majority of the included studies tend to find a correlation between more frequent social media use and poor well-being and/or mental health (see Supplementary Table 2 ). The strength of this correlation is however heterogeneous as social media use is measured substantially different across studies. Four (5%) of the included studies focus explicitly on how mental health can affect social media use ( Merelle et al., 2017 ; Nesi et al., 2017a ; Erreygers et al., 2018 ; Settanni et al., 2018 ). Fourteen studies included a mediating factor or focus on reciprocal relationships between social media use and mental health ( Ferguson et al., 2014 ; Koo et al., 2015 ; Tseng and Yang, 2015 ; Frison and Eggermont, 2017 ; Geusens and Beullens, 2017 ; Marchant et al., 2017 ; Oberst et al., 2017 ; Rousseau et al., 2017 ; Salmela-Aro et al., 2017 ; Tiggemann and Slater, 2017 ; Houghton et al., 2018 ; Marques et al., 2018 ; Niu et al., 2018 ; Wang et al., 2018 ). An example is a cross-sectional study by Ferguson et al. (2014) suggesting that exposure to social media contribute to later peer competition which was found to be a predictor of negative mental health outcomes such as eating disorder symptoms.

Cyberbullying as a Nexus

Thirteen of the 79 (17%) included studies investigated cyberbullying as the measurement of social media use ( Aboujaoude et al., 2015 ; Cross et al., 2015 ; Hamm et al., 2015 ; Hase et al., 2015 ; Spears et al., 2015 ; Fahy et al., 2016 ; Fisher et al., 2016 ; Foody et al., 2017 ; Przybylski and Bowes, 2017 ; Wolke et al., 2017 ; Fredrick and Demaray, 2018 ; John et al., 2018 ; Holfeld and Mishna, 2019 ). Most of the systematic reviews and meta-analyses included focused on cyberbullying. A cross-sectional study from 2017 suggests that cyberbullying has similar negative effects as direct or relational bullying, and that cyberbullying is “mainly a new tool to harm victims already bullied by traditional means” ( Wolke et al., 2017 ). A meta-analysis from 2016 concludes that “peer cybervictimization is indeed associated with a variety of internalizing and externalizing problems among adolescents” ( Fisher et al., 2016 ). A systematic review from 2018 concludes that both victims and perpetrators of cyberbullying are at greater risk of suicidal behavior compared with non-victims and non-perpetrators ( John et al., 2018 ).

Strengths and Limitations of Present Study

The main strength of this scoping review lies in the effort to give a broad overview of published research related to use of social media, and mental health and well-being among adolescents. Although a range of reviews on screen-based activities in general and mental health and well-being exist ( Dickson et al., 2018 ; Orben, 2020 ), they do not necessarily discern between social media use and other types of technology-based media. Also, some previous reviews tend to be more particular regarding mental health outcome ( Best et al., 2014 ; Seabrook et al., 2016 ; Orben, 2020 ), or do not focus on adolescents per se ( Seabrook et al., 2016 ). The main limitation is that, despite efforts to make the search strategy as comprehensive and inclusive as possible, we probably have not been able to identify all relevant studies – this is perhaps especially true when studies do include relevant information about social media and mental health/well-being, but this information is part of sub-group analyses or otherwise not the main aim of the studies. In a similar manner, related to qualitative studies, we do not know if our search strategy were as efficient in identifying studies of relevance if this was not the main theme or focus of the study. Despite this, we believe that we were able to strike a balance between specificity and sensitivity in our search strategy.

Description of Central Themes and Core Concepts

The findings from the present scoping review on social media use and mental health and well-being among adolescents revealed that the majority (about 3/4) of the included studies focused on social media and pathology. The core concepts identified are social media use and its statistical association with symptoms of depression, general psychiatric symptoms and other symptoms of psychopathology. Similar findings were made by Keles et al. (2020) in a systematic review from 2019. Focus on the potential association between social media use and positive outcomes seems to be rarer in the current literature, even though some studies focused on well-being which also includes positive aspects of mental health. Studies focusing on screen-based media in general and well-being is more prevalent than studies linking social media specifically with well-being ( Orben, 2020 ). The notion that excessive social media use is associated with poor mental health is well established within mainstream media. Our observation that this preconception seems to be the starting point for much research is not conducive to increased knowledge, but also alluded to elsewhere ( Coyne et al., 2020 ).

Why the Focus on Poor Mental Health/Pathology?

The relationship between social media and mental health is likely to be complex, and social media use can be beneficial for maintaining friendships and enriching social life ( Seabrook et al., 2016 ; Birkjær and Kaats, 2019 ; Coyne et al., 2020 ; Orben, 2020 ). This scoping review reveals that the majority of studies focusing on effects of social media use has a clearly stated focus on pathology and detrimental results of social media use. Mainstream media and the public discourse has contributed in creating a culture of fear around social media, with a focus on its negative elements ( Ahn, 2012 ; O’Reilly et al., 2018 ). It is difficult to pin-point why the one-sided focus on the negative effects of social media has been established within the research literature. But likely reasons are elements of “moral panic,” and reports of increases in mental health problems among adolescents in the same period that social media were introduced and became wide-spread ( Birkjær and Kaats, 2019 ). The phenomenon of moral panic typically resurges with the introduction and increasing use of new technologies, as happened with video games, TV, and radio ( Mueller, 2019 ).

The Metrics of Social Media

Social media trends change rapidly, and it is challenging for the research field to keep up. The included studies covered some of the most frequently used social media, but the amount of studies focusing on each social media did not accurately reflect the contemporary distribution of users. Even though sites such as Instagram and Snapchat were covered in some studies, the coverage did not do justice to the amount of users these sites had. Newer social media sites such as TikTok were not mentioned in the included studies even though it has several hundred million daily users ( Mediakix, 2019 ; Wallaroo, 2020 ).

Across the included studies there was some variation in how social media were gauged, but the majority of studies focused on the mere frequency and duration of use. There were little focus on separating between different forms of (inter)actions on social media, as these can vary between being a victim of cyberbullying to participating in healthy community work. Also, few studies differentiated between types of actions (i.e., posting, scrolling, reading), active and passive modes of social media use (i.e., production versus consumption, and level of interactivity), a finding similar to other reports ( Seabrook et al., 2016 ; Verduyn et al., 2017 ; Orben, 2020 ). There is reason to believe that different modes of use on social media platforms are differentially associated with mental health, and a recent narrative review highlight the need to address this in future research ( Orben, 2020 ). One of the included studies found for instance that it is not the total time spent on Facebook or the internet, but the specific amount of time allocated to photo-related activities that is associated with greater symptoms of eating disorders such as thin ideal internalization, self-objectification, weight dissatisfaction, and drive for thinness ( Meier and Gray, 2014 ). This observation can possibly be explained by social comparison mechanisms ( Appel et al., 2016 ) and passive use of social media ( Verduyn et al., 2017 ). The lack of research differentiating social media use and its association with mental health is an important finding of this scoping review and will hopefully contribute to this being included in future studies.

Few studies examined the motivation behind choosing to use social media, or the mental health status of the users when beginning a social media session. It has been reported that young people sometimes choose to enter sites such as Facebook and Twitter as an escape from threats to their mental health such as experiencing overwhelming pressure in daily life ( Boyd, 2014 ). This kind of escapism can be explained through uses and gratifications theory [see for instance ( Coyne et al., 2020 )]. On the other hand, more recent research suggest that additional motivational factors may include the need to control relationships, content, presentation, and impressions ( Throuvala et al., 2019 ), and it is possible that social media use can act as an reinforcement of adolescents’ current moods and motivations ( Birkjær and Kaats, 2019 ). Regardless, it seems obvious that the interplay between online and offline use and underlying motivational mechanisms needs to be better understood.

There has also been some questions about the accuracy when it comes to deciding the amount and frequency of one’s personal social media use. Without measuring duration and frequency of use directly and objectively it is unlikely that subjective self-report of general use is reliable ( Kobayashi and Boase, 2012 ; Scharkow, 2016 , 2019 ; Naab et al., 2019 ). Especially since the potential for social media use is almost omnipresent and the use itself is diverse in nature. Also, due to processes such as social desirability, it is likely that some participants report lower amounts of social media use as excessive use is seen largely undesirable ( Krumpal, 2013 ). Inaccurate reporting of prior social media use could also be a threat to the validity of the reported numbers and thus bias the results reported. Real-time tracking of actual use and modes of use is therefore recommended in future studies to ensure higher accuracy of these aspects of social media use ( Coyne et al., 2020 ; Orben, 2020 ), despite obvious legal and ethical challenges. Another aspect of social media use which does not seem to be addressed is potential spill-over effects, where use of social media leads to potential interest in or thinking about use of – and events or contents on – social media when the individual is offline. When this aspect has been addressed, it seems to be in relation to preoccupations and with a focus on excessive use or addictive behaviors ( Griffiths et al., 2014 ). Conversely, given the ubiquitous and important role of social media, experiences on social media – for better or for worse – are likely to be interconnected with the rest of an individual’s lived experience ( Birkjær and Kaats, 2019 ).

The Studies Seem to Implicitly Think That the Use of Social Media “Causes”/“Affects” Mental Health (Problems)

Most of the included studies establish an implicit causation between social media and mental health. It is assumed that social media use has an impact on mental health. The majority of studies included establish some correlation between more frequent use of social media and poor well-being/mental health, as evident from Supplementary Table 2 . As formerly mentioned, most of the included studies are cross-sectional and cannot shed light into temporality or cause-and-effect. In total, only 16 studies had a longitudinal design, using different types of regression models, latent growth curve models and cross-lagged models. Yet there seems to be an unspoken expectation that the direction of the association is social media use affecting mental health. The reason for this supposition is unclear, but again it is likely that the mainstream media discourse dominated by mostly negative stories and reports of social media use has some impact together with the observed moral panic.

With the increased popularity of social media and internet arrived a reduction of face-to-face contact and supposed increased social isolation ( Kraut et al., 1998 ; Espinoza and Juvonen, 2011 ). This view is described as the displacement hypothesis [see for instance ( Coyne et al., 2020 )]. Having a thriving social life and community with meaningful relations are for many considered vital for well-being and good mental health, and the supposed reduction of sociality were undoubtedly met with skepticism by some. Social media use has increased rapidly among young people over the last two decades along with reports that mental health problems are increasing. Several studies report that there is a rising prevalence of symptom of anxiety and depression among our adolescents ( Bor et al., 2014 ; Olfson et al., 2015 ). The observation that increases in social media use and mental health issues happened in more or less the same time period can have contributed to focus on how use of social media affects mental health problems.

The existence of an implicit causation is supported by the study variables chosen and the lack of positively worded outcomes. Depression, anxiety, alcohol use, psychiatric problems, suicidal behavior and eating disorders are amongst the most studied outcome-variables. On the other side of the spectrum we have well-being, which can oscillate from positive to negative, whilst the measures of pathology only vary from “ill” to “not ill” with positive outcomes not possible.

What Is the Gap in the Literature?

The current literature on social media and mental health among youth is still developing and has several gaps and shortcomings, as evident from this scoping review and other publications ( Seabrook et al., 2016 ; Coyne et al., 2020 ; Keles et al., 2020 ; Orben, 2020 ). Some of the gaps and shortcomings in the field we propose solutions for has been identified in a systematic review from 2019 by Keles et al. (2020) . The majority of the included studies in the current scoping review were cross-sectional, were limited in their inclusion of potential confounders and 3rd variables such as sociodemographics and personality, preventing knowledge about possible cause-and-effect between social media and mental health. There is a lack of longitudinal studies examining the effects of social media over extended periods of time, as well as investigations longitudinally of how mental health impacts social media use. However, since the formal search was ended for this scoping review, some innovative studies have emerged using longitudinal data ( Brunborg and Andreas, 2019 ; Orben et al., 2019 ; Coyne et al., 2020 ). More high quality longitudinal studies of social media use and mental health could help us identify the patterns over time and help us learn about possible cause-and-effect relationships, as well as disentangling between- and within-person associations ( Coyne et al., 2020 ; Orben, 2020 ). Furthermore, both social media use and mental health are complex phenomena in themselves, and future studies need to consider which aspects they want to investigate when trying to understand their relationship. Mechanisms linking social media use and eating disorders are for instance likely to be different than mechanisms linking social media use and symptoms of ADHD.

Our literature search also revealed a paucity of qualitative studies exploring the why’s and how’s of social media use in relation to mental health among adolescents. Few studies examine how youth themselves experience and perceive the relationship between social media and mental health, and the reasons for their continued and frequent use. Qualitatively oriented studies would contribute to a deeper understanding of adolescent’s social media sphere, and their thoughts about the relationship between social media use and mental health [see for instance ( Burnette et al., 2017 )]. For instance, O’Reilly et al. (2018) found that adolescents viewed social media as a threat to mental well-being, and concluded that they buy into the idea that “inherently social media has negative effects on mental wellbeing” and seem to “reify the moral panic that has become endemic to contemporary discourses.” On the other hand, Weinstein found using both quantitative and qualitative data that adolescents’ perceptions of the relationship between social media use and well-being probably is more nuanced, and mostly positive. Another clear gap in the research literature is the lack of focus on potentially positive aspects of social media use. It is obvious that there are some positive sides of the use of social media, and these also need to be investigated further ( Weinstein, 2018 ; Birkjær and Kaats, 2019 ). Gender-specific analyses are also lacking in the research literature, and there is reason to believe that social media use have different characteristics between the genders with different relationships to mental health. In fact, recent findings indicate that not only gender should be considered an important factor when investigating the role of social media in adolescents’ lives, but individual characteristics in general ( Orben et al., 2019 ; Orben, 2020 ). Analyses of socioeconomic status and geographic location are also lacking and it is likely that these factors might play a role the potential association between social media use and mental health. And finally, several studies point to the fact that social media potentially could be a fruitful arena for promoting mental well-being among youth, and developing mental health literacy to better equip our adolescents for the challenges that will surely arise ( O’Reilly et al., 2018 ; Teesson et al., 2020 ).

Research into the association between social media use and mental health and well-being among adolescents is rapidly emerging. The field is characterized by a focus on the association between social media use and negative aspects of mental health and well-being, and where studies focusing on the potentially positive aspects of social media use are lacking. Presently, the majority of studies in the field are quantitatively oriented, with most utilizing a cross-sectional design. An increase in qualitatively oriented studies would add to the field of research by increasing the understanding of adolescents’ social-media life and their own experiences of its association with mental health and well-being. More studies using a longitudinal design would contribute to examining the effects of social media over extended periods of time and help us learn about possible cause-and-effect relationships. Few studies look into individual factors, which may be important for our understanding of the association. Social media use and mental health and well-being are complex phenomena, and future studies could benefit from specifying the type of social media use they focus on when trying to understand its link to mental health. In conclusion, studies including more specific aspects of social media, individual differences and potential intermediate variables, and more studies using a longitudinal design are needed as the research field matures.

Author Contributions

JS conceptualized the review approach and provided general guidance to the research team. VS and JS drafted the first version of this manuscript. JS, GH, and LA developed the draft further based on feedback from the author group. All authors reviewed and approved the final version of the manuscript and have made substantive intellectual contributions to the development of this manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to thank Bergen municipality, Hordaland County Council and Western Norway University of Applied Sciences for their collaboration and help with the review. We would also like to thank Senior Librarian Marita Heinz at the Norwegian Institute for Public Health for vital help conducting the literature search.

Funding. This review was partly funded by Regional Research Funds in Norway, funding #RFF297031. No other specific funding was received for the present project. The present project is associated with a larger innovation-project lead by Bergen municipality in Western Norway related to the use of social media and mental health and well-being. The innovation-project is funded by a program initiated by the Norwegian Directorate of Health, and in Vestland county coordinated by the County Council (County Authority). The project aims to explore social media as platform for health-promotion among adolescents.

1 https://rayyan.qcri.org/welcome

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2020.01949/full#supplementary-material

Aboujaoude E., Savage M. W., Starcevic V., Salame W. O. (2015). Cyberbullying: review of an old problem gone viral. J. Adolesc. Health 57 10–18. 10.1016/j.jadohealth.2015.04.011 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Ahn J. (2012). Teenagers’ experiences with social network sites: relationships to bridging and bonding social capital. Inform. Soc. 28 99–109. 10.1080/01972243.2011.649394 [ CrossRef ] [ Google Scholar ]
Appel H., Gerlach A. L., Crusius J. (2016). The interplay between Facebook use, social comparison, envy, and depression. Curr. Opin. Psychol. 9 44–49. 10.1016/j.copsyc.2015.10.006 [ CrossRef ] [ Google Scholar ]
Arksey H., O’Malley L. (2005). Scoping studies: towards a methodological framework. Int. J. Soc. Res. Methodol. 8 19–32. 10.1080/1364557032000119616 [ CrossRef ] [ Google Scholar ]
Banjanin N., Banjanin N., Dimitrijevic I., Pantic I. (2015). Relationship between internet use and depression: focus on physiological mood oscillations, social networking and online addictive behavior. Comput. Hum. Behav. 43 308–312. 10.1016/j.chb.2014.11.013 [ CrossRef ] [ Google Scholar ]
Banyai F., Zsila A., Kiraly O., Maraz A., Elekes Z., Griffiths M. D., et al. (2017). Problematic social media use: results from a large-scale nationally representative adolescent sample. PLoS One 12 : e0169839 . 10.1371/journal.pone.0169839 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Barry C. T., Sidoti C. L., Briggs S. M., Reiter S. R., Lindsey R. A. (2017). Adolescent social media use and mental health from adolescent and parent perspectives. J. Adolesc. 61 1–11. 10.1016/j.adolescence.2017.08.005 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Bell V., Bishop D. V., Przybylski A. K. (2015). The debate over digital technology and young people. BMJ 351 : h3064 . 10.1136/bmj.h3064 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Best P., Manktelow R., Taylor B. (2014). Online communication, social media and adolescent wellbeing: a systematic narrative review. Child. Youth Serv. Rev. 41 27–36. 10.1016/j.childyouth.2014.03.001 [ CrossRef ] [ Google Scholar ]
Best P., Taylor B., Manktelow R. (2015). I’ve 500 friends, but who are my mates? Investigating the influence of online friend networks on adolescent wellbeing. J. Public Ment. Health 14 135–148. 10.1108/jpmh-05-2014-0022 [ CrossRef ] [ Google Scholar ]
Birkjær M., Kaats M. (2019). in Er sociale Medier Faktisk en Truss for Unges Trivsel? [Does Social Media Really Pose a Threat to Young People’s Well-Being?] , ed. N.M.H.R. Institute (København: Nordic Co-operation; ). [ Google Scholar ]
Booker C. L., Kelly Y. J., Sacker A. (2018). Gender differences in the associations between age trends of social media interaction and well-being among 10-15 year olds in the UK. BMC Public Health 18 : 321 . 10.1186/s12889-018-5220-4 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Bor W., Dean A. J., Najman J., Hayatbakhsh R. (2014). Are child and adolescent mental health problems increasing in the 21st century? A systematic review. Austr. N. Z. J. Psychiatry 48 606–616. 10.1177/0004867414533834 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Bourgeois A., Bower J., Carroll A. (2014). Social networking and the social and emotional wellbeing of adolescents in Australia. J. Psychol. Counsell. Sch. 24 167–182. 10.1017/jgc.2014.14 [ CrossRef ] [ Google Scholar ]
Boyd D. (2014). It’s Complicated: The Social Lives of Networked Teens. New Haven, CT: Yale University Press. [ Google Scholar ]
Boyle S. C., LaBrie J. W., Froidevaux N. M., Witkovic Y. D. (2016). Different digital paths to the keg? How exposure to peers’ alcohol-related social media content influences drinking among male and female first-year college students. Addict. Behav. 57 21–29. 10.1016/j.addbeh.2016.01.011 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Brunborg G. S., Andreas J. B. (2019). Increase in time spent on social media is associated with modest increase in depression, conduct problems, and episodic heavy drinking. J. Adolesc. 74 201–209. 10.1016/j.adolescence.2019.06.013 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Brunborg G. S., Andreas J. B., Kvaavik E. (2017). Social media use and episodic heavy drinking among adolescents. Psychol. Rep. 120 475–490. 10.1177/0033294117697090 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Burnette C. B., Kwitowski M. A., Mazzeo S. E. (2017). “I don’t need people to tell me I’m pretty on social media:” A qualitative study of social media and body image in early adolescent girls. Body Image 23 114–125. 10.1016/j.bodyim.2017.09.001 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Childnet International (2018). Age Restrictions on Social Media Services. Available online at: https://www.childnet.com/blog/age-restrictions-on-social-media-services (accessed September 30, 2019). [ Google Scholar ]
Colder Carras M., Van Rooij A. J., Van de Mheen D., Musci R., Xue Q., Mendelson T. (2017). Video gaming in a hyperconnected world: a cross-sectional study of heavy gaming, problematic gaming symptoms, and online socializing in adolescents. Comput. Hum. Bahav. 68 472–479. 10.1016/j.chb.2016.11.060 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Coyne S. M., Rogers A. A., Zurcher J. D., Stockdale L., Booth M. (2020). Does time spent using social media impact mental health?: an eight year longitudinal study. Comput. Hum. Behav. 104 : 106160 10.1016/j.chb.2019.106160 [ CrossRef ] [ Google Scholar ]
Critchlow N., MacKintosh A. M., Hooper L., Thomas C., Vohra J. (2019). Participation with alcohol marketing and user-created promotion on social media, and the association with higher-risk alcohol consumption and brand identification among adolescents in the UK. Addict. Res. Theory 27 515–526. 10.1080/16066359.2019.1567715 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Cross D., Lester L., Barnes A. (2015). A longitudinal study of the social and emotional predictors and consequences of cyber and traditional bullying victimisation. Int. J. Public Health 60 207–217. 10.1007/s00038-015-0655-1 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Curtis B. L., Lookatch S. J., Ramo D. E., McKay J. R., Feinn R. S., Kranzler H. R. (2018). Meta-analysis of the association of alcohol-related social media use with alcohol consumption and alcohol-related problems in adolescents and young adults. Alcohol. Clin. Exp. Res. 42 978–986. 10.1111/acer.13642 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
de Lenne O., Vandenbosch L., Eggermont S., Karsay K., Trekels J. (2018). Picture-perfect lives on social media: a cross-national study on the role of media ideals in adolescent well-being. Med. Psychol. 23 52–78. 10.1080/15213269.2018.1554494 [ CrossRef ] [ Google Scholar ]
de Vries D. A., Peter J., de Graaf H., Nikken P. (2016). Adolescents’ social network site use, peer appearance-related feedback, and body dissatisfaction: testing a mediation model. J. Youth Adolesc. 45 211–224. 10.1007/s10964-015-0266-4 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Dickson K., Richardson M., Kwan I., MacDowall W., Burchett H., Stansfield C., et al. (2018). Screen-Based Activities and Children and Young People’s Mental Health: A Systematic Map of Reviews. London: University College London. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Erfani S. S., Abedin B. (2018). Impacts of the use of social network sites on users’ psychological well-being: a systematic review. J. Assoc. Inform. Sci. Technol. 69 900–912. 10.1002/asi.24015 [ CrossRef ] [ Google Scholar ]
Erreygers S., Vandebosch H., Vranjes I., Baillien E., De Witte H. (2018). Feel good, do good online? Spillover and crossover effects of happiness on adolescents’ online prosocial behavior. Happiness Stud. 20 1241–1258. 10.1007/s10902-018-0003-2 [ CrossRef ] [ Google Scholar ]
Espinoza G., Juvonen J. (2011). The pervasiveness, connectedness, and intrusiveness of social network site use among young adolescents. Cyberpsychol. Behav. Soc. Netw. 14 705–709. 10.1089/cyber.2010.0492 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Fahy A. E., Stansfield S. A., Smuk M., Smith N. R., Cummins S., Clark C. (2016). Longitudinal associations between cyberbullying involvement and adolescent mental health. J. Adolesc. Health 59 502–509. 10.1016/j.jadohealth.2016.06.006 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Ferguson C. J., Munoz M. E., Garza A., Galindo M. (2014). Concurrent and prospective analyses of peer, television and social media influences on body dissatisfaction, eating disorder symptoms and life satisfaction in adolescent girls. J. Youth Adolesc. 43 1–14. 10.1007/s10964-012-9898-9 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Fisher B. W., Gardella J. H., Teurbe-Tolon A. R. (2016). Peer Cybervictimization among adolescents and the associated internalizing and externalizing problems: a meta-analysis. J. Youth Adolesc. 45 1727–1743. 10.1007/s10964-016-0541-z [ PubMed ] [ CrossRef ] [ Google Scholar ]
Foerster M., Roosli M. (2017). A latent class analysis on adolescents media use and associations with health related quality of life. Comput. Huma. Bahav. 71 266–274. 10.1016/j.chb.2017.02.015 [ CrossRef ] [ Google Scholar ]
Foody M., Samara M., O’Higgins Norman J. (2017). Bullying and cyberbullying studies in the school-aged population on the island of Ireland: a meta-analysis. Br. J. Educ. Psychol. 87 535–557. 10.1111/bjep.12163 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Fredrick S. S., Demaray M. K. (2018). Peer victimization and suicidal ideation: the role of gender and depression in a school. Based sample. J. Sch. Psychol. 67 1–15. 10.1016/j.jsp.2018.02.001 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Frison E., Eggermont S. (2016). Exploring the relationships between different types of Facebook use, perceived online social support, and adolescents’ depressed mood. Soc. Sci. Comput. Rev. 34 153–171. 10.1177/0894439314567449 [ CrossRef ] [ Google Scholar ]
Frison E., Eggermont S. (2017). Browsing, posting, and liking on instagram: the reciprocal relationships between different types of instagram use and adolescents’. Depressed Mood. 20 603–609. 10.1089/cyber.2017.0156 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Geusens F., Beullens K. (2017). The reciprocal associations between sharing alcohol references on social networking sites and binge drinking: a longitudinal study among late adolescents. Comput. Hum. Behav. 73 499–506. 10.1016/j.chb.2017.03.062 [ CrossRef ] [ Google Scholar ]
Geusens F., Beullens K. (2018). The association between social networking sites and alcohol abuse among Belgian adolescents: the role of attitudes and social norms. J. Media Psychol. 30 207–216. 10.1027/1864-1105/a000196 [ CrossRef ] [ Google Scholar ]
Griffiths M. D., Kuss D. J., Demetrovics Z. (2014). “ Chapter 6 - social networking addiction: an overview of preliminary findings ,” in Behavioral Addictions , eds Rosenberg K. P., Feder L. C. (San Diego: Academic Press), 119–141. [ Google Scholar ]
Hamm M. P., Newton A. S., Chisholm A., Shulhan J., Milne A., Sundar P., et al. (2015). Prevalence and effect of cyberbullying on children and young people: a scoping review of social media studies. JAMA Pediatr. 169 770–777. [ PubMed ] [ Google Scholar ]
Hanprathet N., Manwong M., Khumsri J., Yingyeun R., Phanasathit M. (2015). Facebook addiction and its relationship with mental health among thai high school students. J. Med. Assoc. Thailand 98(Suppl. 3) S81–S90. [ PubMed ] [ Google Scholar ]
Harbard E., Allen N. B., Trinder J., Bei B. (2016). What’s keeping teenagers up? prebedtime behaviors and actigraphy-assessed sleep over school and vacation. J. Adolesc. Health 58 426–432. 10.1016/j.jadohealth.2015.12.011 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Hase C. N., Goldberg S. B., Smith D., Stuck A., Campain J. (2015). Impacts of traditional bullying and cyberbullying on the mental health of middle school and high school students. Psychol. Sch. 52 607–617. 10.1002/pits.21841 [ CrossRef ] [ Google Scholar ]
Holfeld B., Mishna F. (2019). , Internalizing symptoms and externalizing problems: risk factors for or consequences of cyber victimization? J. Youth Adolesc. 48 567–580. 10.1007/s10964-018-0974-7 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Houghton S., Lawrence D., Hunter S. C., Rosenberg M., Zadow C., Wood L., et al. (2018). Reciprocal relationships between trajectories of depressive symptoms and screen media use during adolescence. Youth Adolesc. 47 2453–2467. 10.1007/s10964-018-0901-y [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Jafarpour S., Jadidi H., Almadani S. A. H. (2017). Comparing personality traits, mental health and self-esteem in users and non-users of social networks. Razavi Int. J. Med. 5 :e61401. 10.5812/rijm.61401 [ CrossRef ] [ Google Scholar ]
John A., Glendenning A. C., Marchant A., Montgomery P., Stewart A., Wood S., et al. (2018). Self-harm, suicidal behaviours, and cyberbullying in children and young people: systematic review. J. Med. Int. Res. 20 : e129 . 10.2196/jmir.9044 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kaplan A. M., Haenlein M. (2010). Users of the world, unite! The challenges and opportunities of social media. Bus. Horiz. 53 59–68. 10.1016/j.bushor.2009.09.003 [ CrossRef ] [ Google Scholar ]
Keles B., McCrae N., Grealish A. (2020). A systematic review: the influence of social media on depression, anxiety and psychological distress in adolescents. Int. J. Adolesc. Youth 25 79–93. 10.1080/02673843.2019.1590851 [ CrossRef ] [ Google Scholar ]
Kietzmann J. H., Hermkens K., McCarthy I. P., Silvestre B. S. (2011). Social media? Get serious! Understanding the functional building blocks of social media. Bus. Horiz. 54 241–251. 10.1016/j.bushor.2011.01.005 [ CrossRef ] [ Google Scholar ]
Kim H. H.-S. (2017). The impact of online social networking on adolescent psychological well-being (WB): a population-level analysis of Korean school-aged children. Int. J. Adolesc. Youth 22 364–376. 10.1080/02673843.2016.1197135 [ CrossRef ] [ Google Scholar ]
Kim S., Kimber M., Boyle M. H., Georgiades K. (2019). Sex differences in the association between cyberbullying victimization and mental health. Subst. Suicid. Ideation Adolesc. 64 126–135. 10.1177/0706743718777397 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kobayashi T., Boase J. (2012). No Such Effect? The implications of measurement error in self-report measures of mobile communication use. Commun. Methods Meas. 6 126–143. 10.1080/19312458.2012.679243 [ CrossRef ] [ Google Scholar ]
Koo H. J., Woo S., Yang E., Kwon J. H. (2015). The double meaning of online social space: three-way interactions among social anxiety, online social behavior, and offline social behavior. Cyberpsychol. Behav. Soc. Netw. 18 514–520. 10.1089/cyber.2014.0396 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kraut R., Patterson M., Lundmark V., Kiesler S., Mukophadhyay T., Scherlis W. (1998). Internet paradox: a social technology that reduces social involvement and psychological well-being? Am. Psychol. 53 1017 . 10.1037/0003-066x.53.9.1017 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Krumpal I. (2013). Determinants of social desirability bias in sensitive surveys: a literature review. Qua. Quant. 47 2025–2047. 10.1007/s11135-011-9640-9 [ CrossRef ] [ Google Scholar ]
Lai H.-M., Hsieh P.-J., Zhang R.-C. (2018). Understanding adolescent students’ use of facebook and their subjective wellbeing: a gender-based comparison. Behav. Inform. Technol. 38 533–548. 10.1080/0144929x.2018.1543452 [ CrossRef ] [ Google Scholar ]
Lang D., Chien G. (2018). “wordcloud2”: a fast visualization tool for creating wordclouds by using “wordcloud2.js”. R Package Version 0.2.1. Available online at: https://cran.r-project.org/web/packages/wordcloud2/index.html [ Google Scholar ]
Larm P., Aslund C., Nilsson K. W. (2017). The role of online social network chatting for alcohol use in adolescence: testing three peer-related pathways in a Swedish population-based sample. Comput. Hum. Behav. 71 284–290. 10.1016/j.chb.2017.02.012 [ CrossRef ] [ Google Scholar ]
Larm P., Raninen J., Åslund C., Svensson J., Nilsson K. W. (2019). The increased trend of non-drinking alcohol among adolescents: what role do internet activities have? Eur. J. Public Health 29 27–32. 10.1093/eurpub/cky168 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Marchant A., Hawton K., Stewart A., Montgomery P., Singaravelu V., Lloyd K., et al. (2017). A systematic review of the relationship between internet use, self-harm and suicidal behaviour in young people: the good, the bad and the unknown. PLoS One 12 : e0181722 . 10.1371/journal.pone.0181722 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Marengo D., Longobardi C., Fabris M. A., Settanni M. (2018). Highly-visual social media and internalizing symptoms in adolescence: the mediating role of body image concerns. Comput. Hum. Behav. 82 63–69. 10.1016/j.chb.2018.01.003 [ CrossRef ] [ Google Scholar ]
Marques T. P., Marques-Pinto A., Alvarez M. J., Pereira C. R. (2018). Facebook: risks and opportunities in brazilian and portuguese youths with different levels of psychosocial adjustment. Spanish J. Psychol. 21 : E31 . [ PubMed ] [ Google Scholar ]
Mediakix (2019). 20 Tiktok Statistics Marketers Need To Know: Tiktok Demographics & Key Data. 2019. Available online at: https://mediakix.com/blog/top-tik-tok-statistics-demographics/ (accessed February 20, 2020). [ Google Scholar ]
Meier E. P., Gray J. (2014). Facebook photo activity associated with body image disturbance in adolescent girls. Cyberpsychol. Behav. Soc. Netw. 17 199–206. 10.1089/cyber.2013.0305 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Memon A. M., Sharma S. G., Mohite S. S., Jain S. (2018). The role of online social networking on deliberate self-harm and suicidality in adolescents: a systematized review of literature. Indian J. Psychiatry 60 384–392. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Merelle S. Y. M., Kleiboer A., Schotanus M., Cluitmans T. L. M., Waardenburg C. M., Kramer D., et al. (2017). Which health-related problems are associated with problematic video-gaming or social media use in adolescents? A large-scale cross-sectional study. Clin. Neuropsych. 14 11–19. [ Google Scholar ]
Mueller M. (2019). Challenging the Social Media Moral Panic: Preserving Free Expression under Hypertransparency. Washington, DC: Cato Institute Policy Analysis. [ Google Scholar ]
Naab T. K., Karnowski V., Schlütz D. (2019). Reporting mobile social media use: how survey and experience sampling measures differ. Commun. Methods Meas. 13 126–147. 10.1080/19312458.2018.1555799 [ CrossRef ] [ Google Scholar ]
Neira C. J., Barber B. L. (2014). Social networking site use: linked to adolescents’ social self-concept, self-esteem, and depressed mood. Austr. J. Psychol. 66 56–64. 10.1111/ajpy.12034 [ CrossRef ] [ Google Scholar ]
Nesi J., Miller A. B., Prinstein M. J. (2017a). Adolescents’ depressive symptoms and subsequent technology-based interpersonal behaviors: a multi-wave study. J. Appl. Dev. Psychol. 51 12–19. 10.1016/j.appdev.2017.02.002 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Nesi J., Rothenberg W. A., Hussong A. M., Jackson K. M. (2017b). Friends’ alcohol-related social networking site activity predicts escalations in adolescent drinking: mediation by peer norms. J. Adolesc. Health 60 641–647. 10.1016/j.jadohealth.2017.01.009 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Niu G. F., Luo Y. J., Sun X. J., Zhou Z. K., Yu F., Yang S. L., et al. (2018). Qzone use and depression among Chinese adolescents: a moderated mediation model. J. Affect. Disord. 231 58–62. 10.1016/j.jad.2018.01.013 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Nursalam N., Octavia M., Tristiana R. D., Efendi F. (2018). Association between insomnia and social network site use in Indonesian adolescents. Nurs. Forum 54 149–156. 10.1111/nuf.12308 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Oberst U., Wegmann E., Stodt B., Brand M., Chamarro A. (2017). Negative consequences from heavy social networking in adolescents: the mediating role of fear of missing out. J. Adolesc. 55 51–60. 10.1016/j.adolescence.2016.12.008 [ PubMed ] [ CrossRef ] [ Google Scholar ]
O’Connor R. C., Rasmussen S., Hawton K. (2014). Adolescent self-harm: a school-based study in Northern Ireland. J. Affect. Disord. 159 46–52. 10.1016/j.jad.2014.02.015 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Ofcom (2018). Children and Parents: Media Use and Attitudes Report. Warrington: Ofcom. [ Google Scholar ]
Olfson M., Druss B. G., Marcus S. C. (2015). Trends in mental health care among children and adolescents. N. Engl. J. Med. 372 2029–2038. 10.1056/nejmsa1413512 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Orben A. (2020). Teenagers, screens and social media: a narrative review of reviews and key studies. J. Soc. Psychiatry Psychiatr. Epidemiol. 55 407–414. 10.1007/s00127-019-01825-4 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Orben A., Dienlin T., Przybylski A. K. (2019). Social media’s enduring effect on adolescent life satisfaction. Pro. Natl. Acad. Sci. U.S.A. 116 10226–10228. 10.1073/pnas.1902058116 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Orben A., Przybylski A. K. (2019). The association between adolescent well-being and digital technology use. Nat. Hum. Behav. 3 173–182. 10.1038/s41562-018-0506-1 [ PubMed ] [ CrossRef ] [ Google Scholar ]
O’Reilly M., Dogra N., Whiteman N., Hughes J., Eruyar S., Reilly P. (2018). Is social media bad for mental health and wellbeing? Exploring the perspectives of adolescents. Clin. Chld Psychol. Psychiatry 23 601–613. 10.1177/1359104518775154 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Peters M., Godfrey C., McInerney P. (2017). “ Chapter 11: scoping reviews ,” in Joanna Briggs Institute Reviewer’s Manual , eds Aromataris E., Munn Z. (Adelaide: The Joanna Briggs Institute; ). [ Google Scholar ]
Peters M. D., Godfrey C., Khalil H., McInerney P., Parker D., Soares C. B. (2015). Guidance for conducting systematic scoping reviews. Int. J. Evi. -Based Healthc. 13 141–146. 10.1097/xeb.0000000000000050 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Przybylski A. K., Bowes L. (2017). Cyberbullying and adolescent well-being in England: a population-based cross-sectional study. Lancet Child Adolesc. Health 1 19–26. 10.1016/s2352-4642(17)30011-1 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Przybylski A. K., Weinstein N. (2017). A large-scale test of the goldilocks hypothesis. Psychol. Sci. 28 204–215. 10.1177/0956797616678438 [ PubMed ] [ CrossRef ] [ Google Scholar ]
R Core Team (2014). R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. [ Google Scholar ]
Richards D., Caldwell P. H., Go H. (2015). Impact of social media on the health of children and young people. J. Paediatr. Child Health 51 1152–1157. 10.1111/jpc.13023 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Rousseau A., Eggermont S., Frison E. (2017). The reciprocal and indirect relationships between passive Facebook use, comparison on Facebook, and adolescents’ body dissatisfaction. Comput. Hum. Behav. 73 336–344. 10.1016/j.chb.2017.03.056 [ CrossRef ] [ Google Scholar ]
Salmela-Aro K., Upadyaya K., Hakkarainen K., Lonka K., Alho K. (2017). The dark side of internet use: two longitudinal studies of excessive internet use. depressive symptoms, school burnout and engagement among finnish early and late adolescents. J. Youth Adolesc. 46 343–357. 10.1007/s10964-016-0494-2 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Sampasa-Kanyinga H., Chaput J. P. (2016). Use of social networking sites and alcohol consumption among adolescents. Public Health 139 88–95. 10.1016/j.puhe.2016.05.005 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Sampasa-Kanyinga H., Hamilton H. A., Chaput J. P. (2018). Use of social media is associated with short sleep duration in a dose-response manner in students aged 11 to 20 years. Acta Paediatr. 107 694–700. 10.1111/apa.14210 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Sampasa-Kanyinga H., Lewis R. F. (2015). Frequent use of social networking sites is associated with poor psychological functioning among children and adolescents. Cyberpsychol. Behav. Soc. Netw. 18 380–385. 10.1089/cyber.2015.0055 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Scharkow M. (2016). The accuracy of self-reported internet Use—A validation study using client log data. Commun. Methods Meas. 10 13–27. 10.1080/19312458.2015.1118446 [ CrossRef ] [ Google Scholar ]
Scharkow M. (2019). The reliability and temporal stability of self-reported media exposure: a meta-analysis. Commun. Methods Meas. 13 198–211. 10.1080/19312458.2019.1594742 [ CrossRef ] [ Google Scholar ]
Schønning V., Aarø L. E., Skogen J. C. (2020). Central themes, core concepts and knowledge gaps concerning social media use, and mental health and well-being among adolescents: a protocol of a scoping review of published literature. BMJ Open 10 : e031105 . 10.1136/bmjopen-2019-031105 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Scott H., Woods H. C. (2018). Fear of missing out and sleep: cognitive behavioural factors in adolescents’ nighttime social media use. J. Adolesc. 68 61–65. 10.1016/j.adolescence.2018.07.009 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Seabrook E. M., Kern M. L., Rickard N. S. (2016). Social networking sites, depression, and anxiety: a systematic review. JMIR Ment. Health 3 : e50 . 10.2196/mental.5842 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Settanni M., Marengo D., Fabris M. A., Longobardi C. (2018). The interplay between ADHD symptoms and time perspective in addictive social media use: a study on adolescent Facebook users. Child. Youth Serv. Rev. 89 165–170. 10.1016/j.childyouth.2018.04.031 [ CrossRef ] [ Google Scholar ]
Silge J., Robinson D. (2016). tidytext: text mining and analysis using tidy data principles in RJ. Open Source Softw. 1 : 37 10.21105/joss.00037 [ CrossRef ] [ Google Scholar ]
Spears B. A., Taddeo C. M., Daly A. L., Stretton A., Karklins L. T. (2015). Cyberbullying, help-seeking and mental health in young Australians: implications for public health. Int. J. Public Health 60 219–226. 10.1007/s00038-014-0642-y [ PubMed ] [ CrossRef ] [ Google Scholar ]
Teesson M., Newton N. C., Slade T., Chapman C., Birrell L., Mewton L., et al. (2020). Combined prevention for substance use, depression, and anxiety in adolescence: a cluster-randomised controlled trial of a digital online intervention. Lancet Digital Health 2 e74–e84. 10.1016/s2589-7500(19)30213-4 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Throuvala M. A., Griffiths M. D., Rennoldson M., Kuss D. J. (2019). Motivational processes and dysfunctional mechanisms of social media use among adolescents: a qualitative focus group study. Comput. Hum. Behav. 93 164–175. 10.1016/j.chb.2018.12.012 [ CrossRef ] [ Google Scholar ]
Tiggemann M., Slater A. (2017). Facebook and body image concern in adolescent girls: a prospective study. Int. J. Eat. Disord. 50 80–83. 10.1002/eat.22640 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Tricco A. C., Lillie E., Zarin W., O’Brien K. K., Colquhoun H., Levac D., et al. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann. Int. Med. 169 467–473. [ PubMed ] [ Google Scholar ]
Tseng F.-Y., Yang H.-J. (2015). Internet use and web communication networks, sources of social support, and forms of suicidal and nonsuicidal self-injury among adolescents: different patterns between genders. Suicide Life Threat. Behav. 45 178–191. 10.1111/sltb.12124 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Twenge J. M., Campbell W. K. (2019). Media use is linked to lower psychological well-being: evidence from three datasets. Psychiatr. Q. 11 311–331. 10.1007/s11126-019-09630-7 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Twenge J. M., Joiner T. E., Rogers M. L., Martin G. N. (2018). Increases in depressive symptoms, suicide-related outcomes, and suicide rates among U.S. adolescents after 2010 and links to increased new media screen time. Clin. Psychol. Sci. 6 3–17. 10.1177/2167702617723376 [ CrossRef ] [ Google Scholar ]
van den Eijnden R., Koning I., Doornwaard S., van Gurp F., ter Bogt T. (2018). The impact of heavy and disordered use of games and social media on adolescents’ psychological, social, and school functioning. J. Behav. Addict. 7 697–706. 10.1556/2006.7.2018.65 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Verduyn P., Ybarra O., Resibois M., Jonides J., Kross E. (2017). Do social network sites enhance or undermine subjective well-being? a critical review: do social network sites enhance or undermine subjective well-being?. Soc. Issues Policy Rev. 11 274–302. 10.1111/sipr.12033 [ CrossRef ] [ Google Scholar ]
Wallaroo (2020). TikTok Statistics – Updated February 2020. Available online at: https://wallaroomedia.com/blog/social-media/tiktok-statistics/ (accessed February 20, 2020). [ Google Scholar ]
Wang P., Wang X., Wu Y., Xie X., Wang X., Zhao F., et al. (2018). Social networking sites addiction and adolescent depression: A moderated mediation model of rumination and self-esteem. Personal. Individ. Differ. 127 162–167. 10.1016/j.paid.2018.02.008 [ CrossRef ] [ Google Scholar ]
Wartberg L., Kriston L., Thomasius R. (2018). Depressive symptoms in adolescents. Dtsch. Arztebl. Int. 115 549–555. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Weinstein E. (2018). The social media see-saw: positive and negative influences on adolescents’ affective well-being. New Media Soc. 20 3597–3623. 10.1177/1461444818755634 [ CrossRef ] [ Google Scholar ]
Wolke D., Lee K., Guy A. (2017). Cyberbullying: a storm in a teacup? Eur. Child Adolesc. Psychiatry 26 899–908. 10.1007/s00787-017-0954-6 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Woods H. C., Scott H. (2016). #Sleepyteens: social media use in adolescence is associated with poor sleep quality, anxiety, depression and low self-esteem. J. Adolesc. 51 41–49. 10.1016/j.adolescence.2016.05.008 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Yan H., Zhang R., Oniffrey T. M., Chen G., Wang Y., Wu Y., et al. (2017). Associations among screen time and unhealthy behaviors. academic performance, and well-being in chinese adolescents. Int. J. Envion. Res. Public Heath. 14 : 596 . 10.3390/ijerph14060596 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

Eating Disorders In Media: What's Accurate?

Eating disorders are a form of mental illness often causing body dissatisfaction and a preoccupation with weight loss or changing one's body shape. Eating disorders can majorly negatively impact a person's physical and mental health, leading to death in some cases. Preventing, identifying, and treating eating disorders are important strategies to reduce the adverse effects these mental health disorders can have on a person, their family, and society at large. However, media can often play into stigma and fear surrounding these conditions, so understanding the media’s role in eating disorder recovery can help you make positive changes, whether in your life or for those you care about.

A woman ists hunched over on a couch while sitting next to her female therapist who is writting on a clipboard.

Media as a risk of eating disorders

Various factors may increase a person's risk of developing an eating disorder. Experts agree that media is one such factor, noting that exposure to media that promotes a thin body as an ideal can "play a major role in increasing eating disorders' prevalence worldwide." Exploring the accuracy of the portrayal of eating disorders in media and other ways in which mass media, social media, and other media images may contribute to disordered eating can be a helpful first step to reducing media’s impacts.

Media's portrayal of eating disorders

Movies and TV shows often portray eating disorders inaccurately. Researchers examined 66 US TV and movie characters with eating disorders from 1981 to 2022 and found that the characters were primarily heterosexual white women under age 30, which doesn't accurately represent reality. This phenomenon is sometimes called the "SWAG" stereotype, in which the media portrays people with eating disorders as "skinny, white, affluent girls" instead of showing a more accurate, diverse, and well-rounded portrayal.

Inaccurate media portrayal of eating disorders can lead to stereotypes and stigma that keep people from receiving eating disorder diagnoses and treatment. Researchers point out that men, middle-aged adults, Black, Indigenous, People of Color (BIPOC) individuals, and the lesbian, gay, bisexual, trans, and queer (LGBTQ) community are all underrepresented in media portrayals of eating disorders , even though many people in these groups have a high or higher risk of eating disorders compared to young, white women. Those who are underrepresented may be less likely to recognize that they have an eating disorder or be less comfortable seeking treatment as a result of the stereotypes and stigma stemming from inaccurate media portrayals.

News media's coverage of eating disorders

TV shows, movies, and magazines aren't the only forms of media that can promote stereotypes and stigma surrounding eating disorders. The National Eating Disorders Association (NEDA) recognizes that the way news media covers stories related to eating disorders can also have adverse effects. NEDA provides these suggestions, among others, as ways to cover eating disorders safely and sensitively:

Don't share graphic images or descriptions of the bodies of people who have eating disorders
Don't portray larger bodies as unkept or unflattering
Don't share weight or weight loss numbers or calorie counts
Don't glamorize eating disorders or present them as involving willpower
Don't assume everyone with anorexia nervosa is underweight or that everyone with binge eating disorder is overweight

Media and weight loss pressure

Eating disorders like anorexia nervosa and bulimia nervosa center on a desire to lose weight and a fear of becoming overweight. While the media may not directly cause a person to develop an eating disorder, messages in the media are thought to contribute to eating disorder development by creating weight loss pressure. Researchers say the media provides "a social context for eating disorders" by emphasizing thinness in models, actresses, and pageant contestants, as well as through dieting advertisements. TV shows, movies, magazines, and mass media may make the development of eating disorders more likely among people who already have low body satisfaction or a desire to lose weight.

Media normalizes weight loss desire

The desire to lose weight extends beyond people with eating disorders, and experts believe the media is at least in part to blame. A desire to lose weight has become common among women of all ages, from adolescence to older adulthood, especially in North America. Because the desire to lose weight is so pervasive, researchers call it a "normative discontent," which means it has become a norm for women and girls to be discontent with their bodies. Experts state that media literacy and activism to change the media's portrayals of bodies are needed to change this discontent.

Media and body image issues

When a person has a negative body image, they have negative thoughts and feelings about their body's shape, size, or appearance. Often, body image issues stem from comparing one's body to an ideal and feeling like it doesn't measure up. A negative body image often negatively impacts a person's self-esteem.

Researchers have found that a negative body image plays a role in the development of an eating disorder and disordered eating behaviors. They've also found that body image dissatisfaction increases in response to media exposure. Experts have called the media "channels of transmission of the current body aesthetic model ." When that current body ideal is thin, people may turn to disordered eating to try and achieve the “ideal” in their own bodies.

Social media platforms and eating disorders

Traditional media isn't the only form of media that affects the development of eating disorders. A scoping review of 50 studies spanning 17 countries found that social media use increases body image concerns , disordered eating, eating disorders, and the thin ideal. The authors of the study suggest that social media's role in the development of eating disorders among adolescents and young adults "is worthy of attention as an emerging global public health issue."

A woman in a green shirt lays on a therapist couch while talking to her male therapist.

Social media platforms and body image issues

Experts argue that social media platforms may lead to body image issues in three ways: through social comparison , internalization of the thin ideal, and self-objectification. In social comparison, people see images of other people and compare themselves to them. Internationalization of the thin ideal involves repeatedly seeing images of thin or fit people and adopting the belief that bodies that look like those are best. Self-objectification involves an increased awareness of one's own appearance and may lead to self-criticism, looking at photos of oneself to find flaws, and intentionally posting photos in which the body looks a certain way.

Instagram, food, and health

Research shows that using the social media platform Instagram , in particular, is associated with symptoms of orthorexia nervosa. Orthorexia nervosa is an eating disorder many experts recognize that is not present in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). In orthorexia nervosa, a person has an unhealthy fixation on eating healthy foods. They may become obsessively focused on the perceived purity of their food and avoid foods or ingredients they deem unhealthy.

Unlike other eating disorders, orthorexia nervosa doesn't involve a preoccupation with weight or size. That said, a person with orthorexia nervosa may lose weight or become underweight if they engage in severe food restrictions. People with orthorexia nervosa may obsessively check ingredient lists and nutrition labels, adhere to a restrictive diet for health reasons, spend an unusual amount of time planning and preparing healthy meals, or be especially interested in or critical of other people's diets.

Instagram and food

Research shows that food is among the top eight categories of photos posted on Instagram, and "#food" is among the top 25 hashtags used on the platform. Photos labeled “healthy food photos” are more popular than those deemed unhealthy food photos by viewers. While seeing these photos could benefit some, others may experience adverse effects if these exposures promote obsessive behavior.

Some Instagram celebrities encourage their followers to cut out entire food groups for health reasons, even if research doesn't support that advice. Many health influencers on Instagram give nutrition advice they aren't qualified to offer, which could be harmful. While more Instagram use has been linked to more orthorexic symptoms , more research is needed to better understand how Instagram can influence people's food choices.

Instagram and health

In addition to being a home for many food photos, Instagram also houses fitness content. Photos with the hashtag "#fitspiration" have been found to display thin and toned women often presented in an objectified way. While other media types may pressure people to become thinner, these photos may pressure people to become healthier and more fit by the poster’s standards. Some viewers could experience adverse effects on body image and be more likely to develop an eating disorder as a result.

Eating disorder treatment options

Often, eating disorders are treated using multiple methods. A person with an eating disorder may receive treatment from a therapist, psychiatrist, dietitian, personal trainer, primary care physician, peers, or other mental health professionals at different times or in combination. However, two types of eating disorder treatment options appear to be most widely studied: medication and therapy.

Medication for eating disorders

Current research suggests that children and adolescents with eating disorders can benefit from psychological treatments , like therapy, more than medications. Medications haven't been approved for this age group, though some doctors may try certain medications in specific circumstances. In adults, both psychological treatments and medications may be considered, depending on the type of eating disorder present. While medications haven't had much success with anorexia nervosa, some are approved for bulimia nervosa and binge eating disorder.

The BetterHelp platform is not intended to provide any information regarding which drugs, medication, or medical treatment may be appropriate for you. The content provides generalized information that is not specific to one individual. You should not take any action without consulting a qualified medical professional.

A woman in a doctors coat sits at a wooden table and talk to her laptop screen with a smile during a telehealth call.

Therapy for eating disorder treatment

Many people with eating disorders opt to receive therapy as part of their treatment. A therapist can provide a safe space in which a person can discuss their disordered eating habits, as well as related thoughts and feelings, which are often difficult to discuss with friends and family.

Remote therapy may offer a greater sense of safety than in-person therapy since a person can attend sessions from their chosen location as long as they have an internet connection. Through a remote therapy platform like BetterHelp , clients can be matched with a therapist with experience in eating disorders after signing up, often within 48 hours.

A 2023 systematic meta-review of meta-analyses identified the most evidence-based treatments for eating disorders. The authors named cognitive-behavioral therapy the top treatment for adults with anorexia nervosa or bulimia nervosa. Evidence supported medications as the most supported treatment for binge eating disorder but confirmed that cognitive-behavioral therapy was also effective. Online eating disorder therapy has also been proven highly effective , especially for binge eating disorder and bulimia.

Exposure to media, including TV, movies, magazines, news, and social media, can increase the prevalence of eating disorders. Media may make eating disorders more likely by promoting a thin ideal, glamorizing disordered eating, and normalizing a negative body image. Inaccurate portrayal of eating disorders may also increase stereotypes and stigma, making it more difficult for people with eating disorders to get diagnoses or treatment. Regardless of what caused a person's eating disorder, therapy is available as an evidence-based treatment option.

What To Know About Eating Disorders And Peer Pressure In Young People Medically reviewed by Corey Pitts , MA, LCMHC, LCAS, CCS
The Importance Of Eating Disorders Education Medically reviewed by Corey Pitts , MA, LCMHC, LCAS, CCS
Eating Disorders
Relationships and Relations

COMMENTS

Social Media and Mental Health: Benefits, Risks, and Opportunities for
Future research is necessary to explore the opportunities and risks for social media to support mental health promotion in low-income and middle-income countries, especially as these countries face a disproportionate share of the global burden of mental disorders, yet account for the majority of social media users worldwide (Naslund et al., 2019).
Social Media Use and Its Connection to Mental Health: A Systematic
Social media are responsible for aggravating mental health problems. This systematic study summarizes the effects of social network usage on mental health. Fifty papers were shortlisted from google scholar databases, and after the application of various inclusion and exclusion criteria, 16 papers were chosen and all papers were evaluated for ...
The Relationship between Social Media and the Increase in Mental Health
The prevalence of mental health issues in the KSA is estimated to be around 20.2% [10]. Depression is the most common mental health condition, affecting 21% of the population, followed by anxiety (17.5%) and stress (12.6%) [11]. Research has shown that social media use in Saudi Arabia is correlated with increased mental health issues [12].
Pros & cons: impacts of social media on mental health
Benefits. The use of social media significantly impacts mental health. It can enhance connection, increase self-esteem, and improve a sense of belonging. But it can also lead to tremendous stress, pressure to compare oneself to others, and increased sadness and isolation. Mindful use is essential to social media consumption.
Social Media and Mental Health: Benefits, Risks, and ...
In this commentary, we consider the role of social media as a potentially viable intervention platform for offering support to persons with mental disorders, promoting engagement and retention in care, and enhancing existing mental health services. Specifically, we summarize current research on the use of social media among mental health ...
PDF Social Media and Mental Health: Benefits, Risks, and ...
The wide reach and near ubiquitous use of social media platforms may afford novel opportunities to. John A. Naslund [email protected]. Department of Global Health and Social Medicine, Harvard Medical School, 641 Huntington Avenue, Boston, MA 02115, USA. Digital Mental Health Research Consultant, Mumbai, India.
The mental health and well-being profile of young adults using social media
The relationship between mental health and social media has received significant research and policy attention. However, there is little population-representative data about who social media users ...
A systematic review: the influence of social media on depression
Impact on mental health. Understanding the impact of social media on adolescents' well-being has become a priority due to a simultaneous increase in mental health problems (Kim, Citation 2017).Problematic behaviours related to internet use are often described in psychiatric terminology, such as 'addiction'.
Association of Social Media Use With Social Well-Being, Positive Mental
Social media use is an ever-increasing phenomenon of the 21st century. In the United States, about 7 of 10 individuals use social media to connect with others, receive news content, share information, and entertain themselves (Pew Research Center, 2018).According to a recent study, young individuals pervasively use social media for a variety of reasons including entertainment, identity ...
Social media use and its impact on adolescent mental health: An
Literature reviews on how social media use affects adolescent mental health have accumulated at an unprecedented rate of late. Yet, a higher-level integration of the evidence is still lacking. We fill this gap with an up-to-date umbrella review, a review of reviews published between 2019 and mid-2021. Our search yielded 25 reviews: seven meta ...
(PDF) Social Media and Mental Health
The diffusion of social media coincided with a worsening of mental health conditions. among adolescents and young adults in the United States, giving rise to speculation that. social media might ...
Social Media Use and Mental Health: A Global Analysis
Research indicates that excessive use of social media can be related to depression and anxiety. This study conducted a systematic review of social media and mental health, focusing on Facebook, Twitter, and Instagram. Based on inclusion criteria from the systematic review, a meta-analysis was conducted to explore and summarize studies from the ...
Media overload is hurting our mental health. Here are ways to manage
One study, which surveyed 2,251 adults in the spring of 2020, found that the more frequently people sought information about Covid-19 across various mediums—television, newspapers, and social media—the more likely they were to report emotional distress (Hwang, J., et al., International Journal of Environmental Research and Public Health ...
Teens are spending nearly 5 hours daily on social media. Here are the
41%. Percentage of teens with the highest social media use who rate their overall mental health as poor or very poor, compared with 23% of those with the lowest use. For example, 10% of the highest use group expressed suicidal intent or self-harm in the past 12 months compared with 5% of the lowest use group, and 17% of the highest users expressed poor body image compared with 6% of the lowest ...
Social Media Use and Mental Health: A Global Analysis
Research indicates that excessive use of social media can be related to depression and anxiety. This study conducted a systematic review of social media and mental health, focusing on Facebook, Twitter, and Instagram. Based on inclusion criteria from the systematic review, a meta-analysis was conducted to explore and summarize studies from the ...
Social media brings benefits and risks to teens. Psychology can help
Just days earlier, APA issued its first-ever health advisory, providing recommendations to protect youth from the risks of social media (Health Advisory on Social Media Use in Adolescence, 2023). As youth mental health continues to suffer, parents, teachers, and legislators are sounding the alarm on social media.
Social Media and Mental Health: Benefits, Risks, and Opportunities for
Future research is necessary to explore the opportunities and risks for social media to support mental health promotion in low-income and middle-income countries, especially as these countries face a disproportionate share of the global burden of mental disorders, yet account for the majority of social media users worldwide (Naslund et al. 2019).
How Social Media Affects Your Teen's Mental Health: A Parent's Guide
2. Keep devices out of the bedroom. Research shows a relationship between social media use and poor sleep quality, reduced sleep duration, and sleep difficulties in young people, according to Dr. Murthy's advisory. For teens, poor sleep is linked to emotional health issues and a higher risk for suicide.
The truth about teens, social media and the mental health crisis
A striking decline in teen mental health has coincided with the rise of smartphones and social media. Is social media causing the mental health challenges? Finally, research can answer that question.
Social Media and Mental Health: What's the Connection?
Various research studies suggest a connection between social media and symptoms of anxiety and depression. A 2016 study using survey data from 1,787 U.S. adults between the ages of 19 and 32 found ...
Associations Between Social Media Time and Internalizing and
Key Points. Question Is time spent using social media associated with mental health problems among adolescents?. Findings In this cohort study of 6595 US adolescents, increased time spent using social media per day was prospectively associated with increased odds of reporting high levels of internalizing and comorbid internalizing and externalizing problems, even after adjusting for history of ...
Social media recruitment of Black adolescents with internalizing
This study outlines the process of social media as a tool for recruiting Black adolescents (ages 14-18) into a mental health help-seeking study, as well as the selecting appropriate social media platforms, creating effective and culturally responsive social media materials, considering parental consent waivers, and including bot deterrents. Google and Instagram advertisements were chosen as ...
Navigating Mental Health Content on Social Media
Social media, when used cautiously, can be a powerful tool for building community and forming connections, both of which may be important to your mental health journey. But not everyone on social media, including people with good intentions, uses evidence-based practices, and this can create tension and confusion.
Social Media Smarts: Navigating Sleep and Mental Health for K-12
The state of Iowa's hub for school mental health research, professional development, and clinical services. ... the negative consequences of social media use and (2) the declining mental health and wellbeing of parents, as well as emerging policies in school districts banning or restricting smart phone use at school. In this webinar we will ...
Why Gen Z Blames Social Media for Mental Health Issues
Gen Z and Social Media Woes. Three in four Gen Zers are putting the blame on social media for having a negative impact on their mental health, according to a new study.. The poll of 2,000 Gen Z ...
Effects of Social Media Use on Psychological Well-Being: A Mediated
Introduction. The use of social media has grown substantially in recent years (Leong et al., 2019; Kemp, 2020).Social media refers to "the websites and online tools that facilitate interactions between users by providing them opportunities to share information, opinions, and interest" (Swar and Hameed, 2017, p. 141).Individuals use social media for many reasons, including entertainment ...
Iowa State Researchers Find Cutting back on social media reduces
Young people are using social media more, and their mental health is suffering. Researchers at Iowa State University found a simple intervention could help. During a two-week experiment with 230 college students, half were asked to limit their social media usage to 30 minutes a day and received automated, daily reminders.
Don't Just Blame Social Media for Kids' Poor Mental Health—Blame a Lack
There is a rich body of research showing that poor sleep leads to poor mental health, said Andrew Fuligni, a psychology professor and director of the Adolescent Development Lab at UCLA.
Social Media Use and Mental Health and Well-Being Among Adolescents
Introduction: Social media has become an integrated part of daily life, with an estimated 3 billion social media users worldwide. Adolescents and young adults are the most active users of social media. Research on social media has grown rapidly, with the potential association of social media use and mental health and well-being becoming a polarized and much-studied subject.
Eating Disorders In Media: What's Accurate?
Instagram, food, and health. Research shows that using the social media platform Instagram, in particular, is associated with symptoms of orthorexia nervosa. Orthorexia nervosa is an eating disorder many experts recognize that is not present in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5).

Pros & cons: impacts of social media on mental health

Data Availability

Acknowledgements

Author information

Contributions

Corresponding author

Ethics declarations

Additional information

Rights and permissions

About this article

Share this article

BMC Psychology

The mental health and well-being profile of young adults using social media

Similar content being viewed by others

Cross-platform- and subgroup-differences in the well-being effects of Twitter, Instagram, and Facebook in the United States

The effect of social media on well-being differs from adolescent to adolescent

Variation in social media sensitivity across people and contexts

Sample description

Demographics

Mental health and well-being

Data availability

Code availability

Acknowledgements

Author information

Authors and Affiliations

Contributions

Corresponding author

Ethics declarations

Additional information

Supplementary information

About this article

Share this article

This article is cited by

Quick links

Social Media and Mental Health

Save citation to file

Add to My Bibliography

Social Media Use and Mental Health: A Global Analysis

Conflict of interest statement

Similar articles

Publication types

Related information

Social media brings benefits and risks to teens. Psychology can help identify a path forward

Social media charms and harms

The cost of connection

Dangerous content

Ingrained racism

A role for psychology

Social media recommendations

Further reading

Recommended Reading

Six Things Psychologists are Talking About

Speaking of Psychology

Contact APA

Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice

Cite this article

Introduction

Social Media Use and Mental Health

Facilitate Social Interaction

Access to a Peer Support Network

Promote Engagement and Retention in Services

Challenges with Social Media for Mental Health

Impact on Symptoms

Facing Hostile Interactions

Consequences for Daily Life

Future Directions for Social Media and Mental Health

Author information

Corresponding author

Ethics declarations

Additional information

Rights and permissions

About this article

Share this article

How Social Media Affects Your Teen’s Mental Health: A Parent’s Guide

Social media use and teens: Background, benefits, and harms

What are the potential benefits of social media use by teens?

What are the potential harms of social media use by teens?

When does the kind of content teens see become an issue?

Why is excessive use of social media a problem?

Social media use in teens: A guide for parents