social media addiction research example

• Open access
• Published: 13 July 2023

Conceptualising social media addiction: a longitudinal network analysis of social media addiction symptoms and their relationships with psychological distress in a community sample of adults

• Deon Tullett-Prado 1 ,
• Jo R. Doley 1 ,
• Daniel Zarate 2 ,
• Rapson Gomez 3 &
• Vasileios Stavropoulos 2 , 4  

BMC Psychiatry volume  23 , Article number:  509 ( 2023 ) Cite this article

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Problematic social media use has been identified as negatively impacting psychological and everyday functioning and has been identified as a possible behavioural addiction (social media addiction; SMA). Whether SMA can be classified as a distinct behavioural addiction has been debated within the literature, with some regarding SMA as a premature pathologisation of ordinary social media use behaviour and suggesting there is little evidence for its use as a category of clinical concern. This study aimed to understand the relationship between proposed symptoms of SMA and psychological distress and examine these over time in a longitudinal network analysis, in order better understand whether SMA warrants classification as a unique pathology unique from general distress.

N  = 462 adults ( M age  = 30.8, SD age  = 9.23, 69.3% males, 29% females, 1.9% other sex or gender) completed measures of social media addiction (Bergen Social Media Addiction Scale), and psychological distress (DASS-21) at two time points, twelve months apart. Data were analysed using network analysis (NA) to explore SMA symptoms and psychological distress. Specifically, NA allows to assess the ‘influence’ and pathways of influence of each symptom in the network both cross-sectionally at each time point, as well as over time.

SMA symptoms were found to be stable cross-sectionally over time, and were associated with, yet distinct, from, depression, anxiety and stress. The most central symptoms within the network were tolerance and mood-modification in terms of expected influence and closeness respectively. Depression symptoms appeared to have less of a formative effect on SMA symptoms than anxiety and stress.

Conclusions

Our findings support the conceptualisation of SMA as a distinct construct occurring based on an underpinning network cluster of behaviours and a distinct association between SMA symptoms and distress. Further replications of these findings, however, are needed to strengthen the evidence for SMA as a unique behavioural addiction.

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Introduction

In recent years, increased attention has been paid to phenomena of excessive social media use, impacting users’ lives in a way not dissimilar to substance addiction [ 1 ]. When in this state, known as ‘Problematic Social Media Use (PSMU), one’s social media usage occupies their daily life, to the extent that their other roles and obligations maybe compromised (e.g., family, romance, employment; [ 1 , 2 ]. In that line, PSMU impact has been demonstrated by its significant associations with mood disorder symptoms, low self-esteem, disrupted sleep, reduced physical health and social impairment [ 3 , 4 ]. Given that PSMU prevalence has been estimated to vary globally between 5%-10% of the social media users’ population [ 1 , 5 , 6 ], which exceeds 80% among more developed countries, such as Australia, and has the prospective to rise [ 7 , 8 ], PSMU related mental health concerns present compelling. Despite these, a rather disproportional paucity of longitudinal research regarding the nature, causes and treatment of PSMU has been repeatedly illustrated [ 1 , 9 ]. Attending such remarks, the present study aspires to examine the structure of PSMU’s most popular conceptualisation (as inspired by the behavioural addiction model [ 2 ]), whilst concurrently assessing its relationship with depression/distress behaviours via adopting and innovative network approach.

Conceptualizing problematic social media use

When attempting to conceptualise PSMU, the most employed definitions involve the so called “behavioural addiction model” [ 1 , 9 ]. Labelled as ‘Social Media Addiction’ (SMA), this conceptualization of PSMU is characterized by a deep fixation/drive towards the use of social media that has become uncontrollable and unhealthy. This model features a number of addiction symptoms drawn from those experienced by substance and gambling addicts, with six symptoms derived from Griffiths key-components of addiction [ 10 , 11 ]. These symptoms entail salience (i.e., preoccupation with social media usage), mood modification (i.e. using Social Media to alleviate negative moods/states), tolerance (i.e. requiring more social media engagement over a period of time in order to attain the same degree of satisfaction/mood modification), withdrawal (i.e. the experience of discomfort/distress/irritability/frustration, when attempting to cease/reduce use), relapse (i.e. failed attempts to control social media usage) and conflict/social impairment (i.e. social media use interferes with, and damages, one’s social life, emotional wellbeing, educational attainment, career and/or other activities/needs; [ 12 ]).

A number of separate theories have also been put forwards, such as models describing Problematic Social Media Use in terms of dysfunctional motivations or contexts for use [ 13 , 14 ]. Similarly, various instruments have been developed to reflect conceptual variability when assessing PSMU (e.g., Social Media Disorder Scale [ 15 ]; Bergen Social Media Addiction Scale [ 11 ]). However, the SMA model, as characterized by Griffiths 6 core components of addiction has seen the most use and acceptance, with a number of studies having evidenced the manifestation of those symptoms (e.g., tolerance, relapse, conflicts [ 11 , 16 ], identified motivations and risk factors similar to addiction (e.g., brain/neurological similarities between substance and SMA addicts [ 13 , 14 , 17 ]) and developed measurement tools based on this model [ 9 , 11 , 15 , 18 ]. Based on the above, the six symptom SMA model of PSMU, as measured via the Bergen Social Media Addiction Scale (BSMAS [ 11 ]) is employed going forward in this study.

Despite this level of acceptance, this “addiction” like definition of PSMU/SMA remains the object of controversy [ 19 ]. Criticisms abound regarding the model, with some labelling it a premature pathologizing of ordinary social media use behaviours with low construct validity and little evidence for its existence [ 19 , 20 ]. For example, Huang [ 21 ] highlight positive associations between social media and physical activity, denoting that not all social media use would necessarily represent a problematic behavior. Nonetheless, the lack of clarity surrounding the links between excessive social media use symptoms and markers of impairment, such as distress has been pointed out as cause for caution [ 19 ]. For instance, it has been argued that while preoccupation behaviours may be harmful when involving substances, they don’t necessarily carry the same weight in a behavioural addiction such as SMA [ 22 ]. In addition, it is argued that links between SMA and more well recognised disorders, such as Depression, may imply that SMA is in fact a secondary symptom of pre-existing depression, and not a distinct condition itself [ 19 ]. Given that research in this area is still highly exploratory these criticisms are difficult to dispel [ 9 ]. Thus, there is a need for research clarifying the nature of SMA, its longitudinal effects, and the relative importance of each SMA proposed symptom, as well as ways in which symptoms associate risk factors/negative outcomes.

SMA and longitudinal network analysis

One avenue of addressing this need could be offered via the implementation of longitudinal network analysis [ 23 ]. Network analysis is an exploratory approach of assessing constructs, as mirroring networks of symptoms/behaviours, where a number of variables/behaviours are examined together, whilst information is simultaneously collected regarding their inter-relationships and relative influence, so as to create a graphical ‘network’ (i.e., visualization of the construct’s underpinning behaviours; [ 23 , 24 , 25 ]). This analysis allows one to examine a set of symptoms from an utterly different viewpoint than traditional latent-variable perspectives. Rather than viewing symptoms as resulting from the presence of a latent construct (SMA for example), network analysis assumes symptoms are formative. Which is to say, as causes in themselves, interacting with each other and with other risk factors/negative outcomes to compose/form the “disorder” [ 24 ]. This allows the unique relationships, known as “edges”, between all considered variables/behaviours/manifestations, called “nodes”, to be observed, in a capacity not available with traditional structural equation modelling (SEM [ 26 ]). For example, examination of the so called symptom “centrality” (i.e. relative influence of each distinct symptom on other symptoms/behaviours included in an examined network), instead of symptom severity, may enable the detection of symptoms/behaviours with the largest influence on others, and thus contribute in evaluating: a) their “central” (or more peripheral role) in defining a proposed disorder (e.g. SMA), and; b) their targeted priority in a potential intervention program [ 27 ]. This can be done in great detail with separate centrality indices providing an indication of: a) the summed associations between a symptom/behaviour and all others examined (i.e., strength; Expected Influence in the case of psychopathology); b) the degree to which a symptom serves as an intermediary between others (i.e. betweenness) and; c) how closely a symptom aligns with others (i.e., closeness [ 28 ]). Furthermore, similar centrality relationships between distinct clusters of symptoms can be examined, with the so called “bridge” (i.e. a point that connects two distinct groups of behaviours) centrality indices (i.e. bridge strength; bridge expected influence; bridge betweenness and closeness) providing indications of which symptoms bind distinct disorders, such as SMA and depression together, either serving as intermediaries between disorders and/or by being more proximal to other disorders [ 28 ].

Such detailed examination of the relationships between symptoms, and clusters of symptoms, can further serve to test the veracity of models and constructs, which is particularly important for solidifying the occurrence of SMA [ 19 ]. For example, if the symptoms/behaviours informing a model, don’t relate at all, or accumulate into tight, separate ‘clusters’, then the construct may not be valid [ 29 ]. Additionally, with testing identical construct networks across two or more timepoints, the over-time stability of a proposed network can be examined, further validating a given construct (i.e., if the SMA symptoms’ network remains stable over time, then the construct is likely experienced longitudinally similarly [ 30 ]).

Aside of considering the stability of a network over time, network analysis procedures enable attaining stability coefficients for the edge weights and centrality indicators irrespective of the population/data examined via the use of case-dropping bootstrapping to examine the potential variance in these indices (i.e. network analysis indices such as strength and/or expected influence are re-estimated based on various alternative compositions/ re-samples of the data considered [ 31 , 32 ]. Unstable indices, either population-wise or over time are invalid, and their use is generally dismissed [ 33 ]. Finally, network analysis gives one the opportunity to evaluate not only the relationships of behaviours being considered as composing a single disorder, but also to examine how these distinct disorder informing symptoms/behaviours may interact with other separate comorbid disorders (i.e. in this case SMA behaviours and depression/ anxiety [ 31 ]). This allows the examination of how these variables formatively interact with one another, as well as indicating their separate/distinct concurrent validity [ 34 ].

Indeed, the need of securing such information regarding the distinct proposed SMA symptoms and their associations with comorbid depression and/or distress behaviours experienced is reinforced by recent item response theory (IRT) and network analysis findings of responses on the Bergen Social Media Addiction Scale [ 35 , 36 ]. Stănculescu [ 35 ] identified SMA behaviours of “salience” and “withdrawal” as having the highest centrality, whilst SMA “relapse” behaviours as having the lowest centrality, in the context of the 6 SMA symptoms consisting of a single unitary cluster with strong inter-relations. However, these findings despite constituting an important step, present limited in a number of ways. Firstly, they are derived from a Romanian sample ( N  = 705), where specific cultural characteristics may apply, restricting their generalizability to different populations. Secondly, due to being cross-sectional they don’t allow the examination of the stability of the network associations over-time [ 29 , 31 , 32 ]. Thirdly, Stănculescu’s [ 35 ] examination of the SMA symptom network only took expected influence into account considering centrality and did not consider the significance of differences in the centrality of nodes. Finally, the network examined by Stănculescu [ 35 ] involved no covariates aside of the 6 SMA symptoms. Thus, the extent of differentiation of various SMA behaviours/criteria from comorbid conditions and/or their specific associations with other commonly proposed SMA risk factors and negative outcomes (e.g. depression, anxiety) could not be established [ 37 ]. To contribute to the available knowledge in the field, the present study aims to use network analysis modelling to longitudinally examine SMA symptoms in conjunction with commonly proposed comorbid excessive digital media usage conditions involving experiencing distress (i.e., depression and anxiety [ 37 , 38 , 39 ]).

Distress and SMA

Psychological distress is defined as a state of psychological suffering characterized by anxiety, depression and stress, and often serves as a general measure of mental health [ 37 , 40 ]. In this capacity, investigating the ways in which SMA and distress behaviours interact, can potentially produce a clearer understanding for how a person’s mental health could be distinctly affected by the separate symptoms of SMA and/or the vice versa (e.g., Is it SMA related preoccupation, tolerance and/or withdrawal more related to anxiety and/or depression experiences?). As distress involves some of the most well researched comorbidities of SMA (e.g., depression, anxiety), there is a wealth of prior research indicating the presence of distress-SMA interactions [ 41 , 42 ]. For instance, different aspects of social media use, such as the purpose of using social media (e.g., adaptive/maladaptive coping mechanisms [ 43 ]), their preferred social media activities, as well as behaviours of excessive social media usage have been consistently associated with an individual’s proneness/risk for depression, anxiety and stress [ 41 , 42 ]. Such links tend to be more evident in younger populations, where social media use often drives/underpins psychological distress for a proportion of users (e. g. a developing individual might feel distressed for deviating from what is presented as ideal or common by their peers online [ 44 ]). A wide variety of explanations have been put forth as potential reasons for such distress-SMA links involving: a) distressed individuals excessively utilizing social media use as a way to cope; b) the deleterious effects excessive social media use has on sleep, time management, physical activity, the development of social skills and; c) the near constant access social media provides to information of others, prompting comparisons and negative social interactions [ 42 ]. However, these, independent findings present as fragmented, the clinically relevant, over-time links/associations between specific SMA symptoms and the levels of depression, anxiety and stress one experiences remaining unclear. Such clinically important knowledge can be offered by longitudinal network analysis, which has not been yet, to the best of the authors’ knowledge, attempted concerning these variables.

The findings of such an analysis are envisaged to also have significant epidemiological utility. Given the acknowledged connection between psychological distress and SMA behaviours [ 41 , 42 ], and the noted drive of psychologically distressed individuals towards coping strategies involving escapism via social media facilitated pleasurable activities [ 44 ], it is possible-and indeed argued by some-that PSMU may not in fact represent an addiction (the SMA model) but simply be a secondary symptom of distress [ 19 ]. By examining the SMA model in conjunction with symptoms of distress, the connections between the SMA symptoms and Distress symptoms can be demystified with detail, their bridges can be identified, whilst deeper insight may be gleaned into the relationship between Distress and SMA.

The present study

Prompted by the above literature, the present study aimed to contribute to the field via innovatively, longitudinally, examining a normative, community sample of social media users, assessed across two time points, one year apart, regarding both their SMA and distress behaviours. Specifically, it assessed their responses via advanced longitudinal network analysis’ modelling, enhanced by the use of machine learning algorithms to increase knowledge regarding: a) the validity/sufficiency of the widely popular SMA conceptualization; b) persistent differential diagnosis considerations regarding SMA and distress conditions entailing depression, anxiety and stress and; c) pivotal/central behaviours considering SMA manifestations over time. Thus, the following three aims were devised: 1) To reveal/describe the network structure of the six SMA symptoms and symptoms of depression, anxiety and stress; 2) To examine potential clustering in this revealed SMA-distress network, as well as to identify any specific bridges or routes between the clusters in this network, and; 3) To examine the stability of the revealed SMA-distress network over time and across different potential sample compositions.

Participants

An online sample of adult, English speaking participants aged 18 to 64 who were familiar with social media [ N  = 462, M age  = 30.8, SD age  = 9.23, n males  = 320 (69.3%), n females  = 134, (29%), n other  = 9, (1.9%); 968 complete responses wave 1- 506 attrition between waves = 462] was assessed across two time points, 12 months apart. Acknowledging that adequate sample size rules of thumb are still explored for longitudinal network analysis [ 45 ], the current sample size well exceeds the threshold of 350 recommended for sparse networks up to 20 nodes in order to accurately estimate moderate sensitivity, high specificity and likely high edge weights correlations [ 46 ]. Furthermore, the 53.27% attrition ( N  = 506) between the two waves of data collection was studied. Specifically, attrition/retention was inserted as an independent dummy coded variable (i.e. 1 = attrition, 0 = retention between wave 1 and wave 2) to assess its associations with sociodemographic characteristics of the sample (via crosstabulation, X 2 ), as well with SMA, depression, anxiety and stress rates (via t test). There were no significant associations between social media scores at time-point 1 and 2 ( Welch’s t [953]  = 1.60, p  = 0.11, Cohen’s d  = 0.10). Moreover, older straight males showed decreased attrition rates (Age: Welch’s t [960]  = -4.05, p  < 0.01, Cohen’s d  = -0.26; Gender: χ 2 [2] = 12.4, p  < 0.01, Cramer’s V  = 0.11); however, all differences represented a small effect size. In terms of sociodemographic, variations were observed, with very significant amounts of our sample heralding from diverse backgrounds. For example, 38.1% of the sample heralded from non-white backgrounds and 30.5% of the sample was female or nonbinary. See Table 1 for the sociodemographic information of those addressing both waves and included in the current analyses.

Aside of collecting socio-demographic information the following instruments were employed for the current study:

Bergen Social Media Addiction Scale (BSMAS; [ 11 ] )

The BSMAS measures the severity of one’s experience of the six proposed SMA symptoms via an equivalent number of items that ask to which degree certain behaviours associated with these symptoms relate to one’s own life (i.e., salience, tolerance, mood modification, relapse, withdrawal and conflict [ 11 ]). The items of the BSMAS include “ You spend a lot of time thinking about social media or planning how to use it ” (salience), “You feel an urge to use social media more and more” (tolerance), “You use social media in order to forget about personal problems” (mood modification), “You have tried to cut down on the use of social media without success” (Relapse), “You become restless or troubled if you are prohibited from using social media” (withdrawal) and “You use social media so much that it has had a negative impact on your job/studies” [ 11 ]. These items are rated on a 5-point scale scored from 1 (very rarely) to 5 (very often), with higher scores indicating a greater experience of SMA Symptoms [ 11 ]. A total score ranging between 6 and 30 is comprised by the accumulation of the different items’ points reflecting overall SMA behaviors. Considering the current sample, Cronbach’s α and the McDonalds ω internal reliability indices were both 0.88 for time point one and increased to 0.90 for time point two.

Depression, Anxiety and Stress Scales-1 (DASS-21; [ 47 ] )

The DASS measures distress experiences and comprises 21 items, subdivided into three equal subscales (7 items each) addressing depression, anxiety and stress respectively [ 47 ]. Items examine distress behaviors with a 4-point likert-type scale ranging from 0 (did not apply) to 3 (applied most of the time). Total scores for each dimension are derived by the accumulation of the relevant items’ points ranging between 0–21 for the three factors. Considering time point 1, the Cronbach’s α indices for the subscales of depression, anxiety and stress were 0.94, 0.85 and 0.88 respectively and their corresponding McDonalds ω reliabilities were 0.94, 0.86 and 0.88. For time point 2, the same Cronbach α reliabilities were 0.93, 0.85 and 0.86 and their McDonalds ω reliabilities were 0.93, 0.86 and 0.86.

Approval was received from the Victoria University Human Research Ethics Committee (HRE20-169) and data for both time points was collected between 2020 and 2022. Time point 1 data ( N t1  = 968) was collected via an online survey link distributed via social media (e. g. Facebook; Instagram; Twitter), digital forums (e.g., reddit) and the Victoria University learning management system. The link first took potential participants to the Plain Language Information Statement (PLIS), which informed about the study requirements, responses’ anonymity and free of penalty withdrawal rights. After completing this step, eligible participants were asked to voluntarily provide their email address to be included in prospective data collection wave(s), and to digitally sign the study consent form (box ticking). Twelve months later (between August 2021 and August 2022), follow up emails involving an identical survey link (i.e., PLIS, email provision for the second wave, consent form and survey questions) were sent out for those interested to participate in the second data collection wave ( N t2  = 462). Participation in this study was voluntary.

Statistical analyses

A network model involving the six BSMAS symptoms and three DASS subscales was estimated for the two timepoints using the qgraph and networktools R packages [ 32 , 48 ]. Network models involve the creation of a network nodes and edges, where nodes represent considered variables/observations and edges the relationships between them [ 49 ]. Stronger relationships/edges are represented by thicker, darker lines with the distance between variables/nodes indicating their relevance/association (closer = higher relevance) and the colour indicating the direction of the relationship (Blue = positive, red = negative). This is done in the present case via the use of zero order correlations (i.e., no control for the influence of any other variables) combined with a graphical Least Absolute Shrinkage and Selection Operator algorithm (g-lasso; [ 49 ]) employed to shrink partial correlations to zero. Practically, this reduces the chance of false positives (i.e., Type 1 error), providing more precise judgements about the relationships between variables, whilst concurrently pruning excessively weak links to simplify networks [ 50 ].

Cross-sectional network stability

Once network models are estimated across time points, their respective centrality, edge weights and bridge values are assessed [ 49 ]. Centrality measures used here involve: a) degree (i.e., the number of links/edges held by each node); b) betweenness (i.e. the number of times a node lies on the shortest path between other nodes); c) closeness (i.e. the ‘closeness’ of each node to all other nodes); d) eigenvector (i.e. node centrality based not the node’s connections and additionally the centrality of the nodes they are connected with)] and; d) the ‘expected influence’ of a node for the whole network [ 51 ]. The latter accounts for negative influences/edges, promotes the overall stability in the network, and it is recommended for psychopathological networks [ 29 ]. Finally, bridge values represent the rate of nodes serving as connections between distinct network clusters and are measured via bridge expected influence indices [ 48 ].

The prerequisite for estimating these values is calculating their stability coefficients across time points. These denote the estimated maximum number of cases that can be dropped from the data to retain, with 95% probability, a correlation of at least 0.7 (default) between original network indices and those computed with less cases with an acceptable minimum probability of > 0.25 and preferably > 0.5 [ 32 ]. These were calculated using a modified version of the bootnet package with an end coefficient representing the proportion of the original sample that can be dropped before the centrality, bridge and edge weight values vary significantly [ 32 ].

Cross sectional network characteristics

Once network stability is confirmed, the networktools package estimates the centrality, edge weight and bridge indices and graphs the network. Judgements regarding differences in centrality across nodes or in the strength of edges are made using the centrality/edge difference tests via the bootnet R package [ 32 ]. These construct a confidence interval between the two regarded results, adjusted so that the lower the stability the greater the interval, with the difference deemed non-significant if the points are within it.

Stability of the network across time

To compare network stability across time points, the NetworkComparisonTest package is employed to specifically estimate their variance in terms of the global network structure, the global strength of the nodes, edges and centrality. Each of these tests is carried out in succession, with the latter two tests only being conducted by the package if the first two detected significant differences (i.e., if the networks across the two time points do not differ significantly, there is no point examining differences in more specificity; [ 52 ]). P -values less than 0.05 for these tests indicate significant differences.

Network generation and stability

Network Analyses generated two networks, one for each timepoint, depicted in Figs. 1 and 2 . Edge strengths and calculated centrality statistics for time point 1 are featured in Tables 2 and 3 , and for time point 2 in Tables 4 and 5 . Note that within the following figures, the BSMAS symptoms of salience, tolerance, mood modification, relapse, withdrawal and conflict are referred to as BSMAS_1, BSMAS_2, BSMAS_3, BSMAS_4, BSMAS_5 and BSMAS_6 respectively.

Network of the BSMAS symptoms and DASS subscales at time point 1

Network of the BSMAS symptoms and DASS subscales at time point 2

The network at time point one showed excellent stability in terms of its basic structure (edge stability coefficient = 0.75, expected influence centrality stability coefficient = 0.60) and marginal stability regarding secondary measures of centrality (closeness centrality stability coefficient = 0.13, betweenness centrality stability coefficient = 0.05). In terms of bridges between network clusters, stability ranged from acceptable (bridge expected influence stability coefficient = 0.36), to marginal (bridge betweenness stability coefficient = 0.0) to insufficient (bridge closeness stability coefficient = 0.0).

These structural network characteristics were shared with the network at time point two both in terms of basic structure (edge stability coefficient = 0.75, expected influence centrality stability coefficient = 0.60) and secondary measures of centrality (closeness centrality stability coefficient = 0.13, betweenness centrality stability coefficient = 0.05). Though the bridges between clusters featured greater stability than time point 1 (bridge expected influence stability coefficient = 0.52, bridge betweenness = 0.05, bridge closeness = 0.21).

With all necessary structural measure’s stability within acceptable limits, further analysis of the network structures and network comparison was undertaken. However, given the marginal to unacceptable stability of both closeness and betweenness as measures of centrality, it was deemed that results from these measures cannot be safely generalised, or safely used to draw inferences about the data. Thus, these measures are only considered in the following as potential indicators that may point to avenues of further investigation, unless a result of 0.0 was scored on their stability coefficient, in which case they are completely disregarded.

Network characteristics at Time Point 1

Figure  3 depicts the expected influence of all nodes at time point 1, and Fig.  4 depicts centrality difference tests determining the significance of differences in expected influence between all nodes, with black squares indicating significant differences. In terms of overall centrality, stress had the most and strongest connections with other nodes. Stress had expected influence significantly greater than the majority of nodes, with the exception of anxiety and the BSMAS symptoms of tolerance and mood modification (Items 2 & 3). These BSMAS symptoms formed a consistent plateau of centrality, significantly above the symptoms of Relapse and Withdrawal (Item 4 & 5 respectively). Depression was relatively low in centrality, with a result significantly lower than every other node except relapse and withdrawal.

Expected Influence across all nodes at time point 1

Centrality difference tests of Expected Influence at time point 1

Accordingly, Fig.  5 depicts nodes’ closeness and betweenness at time point 1, while Figs. 6 , 7 depict centrality difference tests determining the significance of differences in betweenness and closeness, with black squares indicating a significant difference. In terms of the number of times a node was on the shortest path (i.e., betweenness), there were no significant differences. In terms of the distance between nodes (i.e., closeness), BSMAS symptoms of mood modification and withdrawal displayed the greatest centrality, with each displaying significantly higher centrality in the network than the DASS subscales.

Closeness and betweenness across all nodes at time point 1

Centrality difference tests of betweenness at time point 1

Centrality difference tests of closeness at time point 1

Figure  8 depicts edge difference tests, indicating that the edges between anxiety and stress, depression and stress, and between the BSMAS symptoms of salience and tolerance were significantly stronger than those of other nodes.

Edges’ difference tests at time point 1

Bridge characteristics at Time Point 1

Figures  9 and 10 depict bridge expected influence, closeness and betweenness centralities between the BSMAS symptoms and the DASS subscales. SMA symptoms of mood modification and conflict demonstrated markedly higher expected influence connections with the DASS subscales cluster than other SMA symptoms. With regards to the DASS subscales, anxiety and stress were in a similar position, with a bridge expected influence on the BSMAS symptoms substantially greater than that of depression (see Fig.  9 ). In terms of the proximity/closeness between nodes in the two subgroups, the BSMAS symptom of mood modification (Item 3) and withdrawal (Item 5) were the most proximal to the distress subgroup, with depression serving as the closest connecting point.

Bridge Expected Influence Centrality at time point 1

Bridge Closeness Centrality at time point 1

Network characteristics at Time Point 2

Figure  11 depicts the expected influence of all nodes at time point 2, whilst Fig.  12 depicts the significance of nodes’ differences in terms of their expected influence. The highest overall centrality in terms of expected influence was demonstrated by the BSMAS symptom of tolerance (Item 2), which was closely followed by the DASS subscale of stress. As is evidenced in Fig.  12 , both stress and tolerance were significantly greater in their expected influence centrality than the other network nodes.

Expected Influence across all nodes at time point 2

Centrality difference tests of Expected Influence at time point 2

Figures  13 and 14 depict the betweenness and closeness respectively of all nodes at time point 2, whilst Figs. 15 and 16 depict centrality difference tests determining the significance of differences in betweenness and closeness respectively. No significant differences in the number of times a node was on the shortest path (i.e., betweenness) identified between the nodes, nor were there any nodes significantly higher in closeness, with the exception of withdrawal (Item 5).

Betweenness across all nodes at time point 2

Closeness across all nodes at time point 2

Centrality difference tests of betweenness at time point 2

Centrality difference tests of closeness at time point 2

Figure  17 depicts edge difference tests at time point 2. As with time point 1, the edges between anxiety and stress, depression and stress, and between the BSMAS symptoms of salience and tolerance (Items 1 & 2) were significantly stronger than those between other nodes. Additionally, the connection between the BSMAS symptoms of tolerance and mood modification (Items 2 & 3) was a significantly stronger connection than over half of those assessed.

Edges’ difference tests at time point 2

Bridge characteristics at Time Point 2

Figures  18 , 19 and 20 depict bridge centralities between the BSMAS symptoms cluster and the DASS subscales cluster at time point 2. As in time point 1, the SMA symptoms of mood modification (Item 3) and conflict (Item 6) bridged the SMA behaviours cluster to the DASS subscales cluster via the nodes of anxiety and stress. These results were displayed in both the number and strength of connections between these nodes (expected influence centrality) and the number of times these nodes were used as connecting joints in paths between other nodes in these two networks (betweenness centrality). Further, in terms of the proximal distance between nodes in the two subgroups, the BSMAS symptom of conflict was the most central symptom, with anxiety and stress being the most proximal distress experiences.

Bridge Expected Influence Centrality at time point 2

Bridge Closeness Centrality at time point 2

Bridge Betweenness Centrality at time point 2

Longitudinal network comparison

Finally, a network invariance test revealed no significant differences between the network at time point 1 and time point 2 in terms of global network invariance ( p  = 0.36) and global strength Invariance ( p  = 0.42).

The rapid expansion of social media use has generated concerns regarding the development of PSMU behaviours. These have been noted to closely resemble those displayed in substance/behavioural addictions [ 1 , 2 ]. In that line, a portion of scholars have defined these behaviour as social media addiction (SMA) and have advocated in favour of describing it via the lenses of the components model of addiction framework (i.e. salience; mood-modification; tolerance; relapse; withdrawal; losing of interest into other activities/functional impairment; [ 1 , 9 ]. Such suggestions have been criticised as accommodating the risk of pathologizing common everyday behaviours, such as the use of social media, and lacking validity due to adhering to substance abuse criteria/behaviours that may fail to correctly depict this emerging condition [ 19 , 20 ]. Additionally, there is a lack of clarity regarding the details of links between excessive use symptoms and markers of impairment, such as distress, which cause further doubts [ 19 , 20 ]. Finally, the occurrence of SMA behaviour as an independent diagnostic condition has been contested on the basis of SMA related behaviours constituting biproducts/ secondary symptoms of primarily distress conditions such as depression, anxiety and stress [ 19 , 20 ].

To address these concerns, the current research innovated via longitudinally assessing a normative cohort of adult social media users twice over a period of two years considering concurrently their SMA and depression, anxiety and stress self-reported experiences. Advanced longitudinal network analysis models, enriched via the LASSO algorithm, were calculated for both time points [ 29 , 32 ]. These aimed to firstly clarify whether SMA criteria, as described on the basis of the components model of addiction, formed indeed an underpinning network of behaviours, stable over time and across different sample compositions [ 10 ]. Answering this question would indicate that the construct is rather formative and not reflective (i.e., it is not just a conception of scholars or a sample specific construct, while it is steadily reflected the same way over time [ 19 , 20 ]).

Secondly, the analysis aimed to dispel to what extent SMA behaviours may mix/blend or closely relate to distress behaviours such as depression, anxiety and stress [ 53 ]. If the latter was to be true, then the SMA and distress components of the network would be expected to mix and not to represent distinctly different network clusters (i.e. SMA and distress related behaviours would represent different behavioural network clusters and thus should be classified independently). Thirdly, it was aimed to identify key/central/pivotal behaviours in the broader network, that should be prioritized in prevention and/or intervention for those presenting with SMA and/or comorbid depression, anxiety and stress (i.e. central nodes of the network with higher expected influence). Findings indicated that SMA behaviours/criteria, as per the components model of addiction, do constitute a formative network of symptoms, which is not sample or time specific. Furthermore, the SMA behaviours cluster was distinct to that of depression, anxiety and stress experiences across both measurements, favouring its classification as an independent diagnostic condition. Lastly, mood modification appeared to be consistently (across both time points) a central network node and has been facilitating as the main bridge primarily with distress symptoms of stress and anxiety rather than depression.

SMA and distress network

As summarized prior, results portrayed a stable overtime network cluster of SMA symptoms, which is associated yet distinct, to the distress related cluster of nodes composed by depression, anxiety and stress. These findings appear to align with the recent SMA, cross-sectional, network analysis study of Romanian data, which also supported the SMA defined behaviours of salience, tolerance, mood-modification, withdrawal, relapse and functional impairment being closely related and informing a clear cluster of nodes [ 35 ]. Therefore, the present study argues in favour of the idea of SMA operating as a formative construct, which occurs independently of the conception of scholars (i.e. does not only reflect theoretical conceptualizations [ 19 , 20 ]. This provides an indication in favour of those who support the SMA conceptualization and potentially the introduction of a distinct diagnostic category to capture the syndrome [ 35 , 36 ]. In that context, SMA behaviours related to mood-modification appeared to be central across both time points, reinforcing the idea of addictions, such as SMA, acting the problematic solution (e.g., way to either experience more positive or buffer negative emotions) of the distress generated by other problems [ 53 ]. Nevertheless, one cannot exclude the need of additional nodes, such as those likely reflecting “deception behaviours associated to the use of social media” (e.g. an individual concealing the amount of time they consume on social media usage) and/or relationship difficulties (e.g. as with other forms of addictions, a person may be marginalized within their social surrounding) to better describe the phenomenon [ 54 ]. Thus, although findings support the six, adjusted to the abuse of social media, addiction criteria operating as a distinct, SMA underpinning, formative network, the need for additional behavioural nodes to better describe the condition cannot be excluded.

Despite these, and in contrast to the results of the Stănculescu [ 35 ] Romanian study, where salience and withdrawal were identified as the most ‘central’ symptoms, the current study identified tolerance and mood-modification as the most highly central in terms of expected influence and closeness respectively. A possible explanation for this discrepancy may refer to the more rigorous methodology and wider aims applied in the current study, compared to that conducted by Stănculescu [ 35 ]. Firstly, the current analysis examined network stability across different resamples (i.e., potential population compositions) and over time (i.e. longitudinally), which was not the case in the Stănculescu [ 35 ] study. Secondly, the present study thoroughly examined centrality differences based on t-test comparisons in conjunction with the visual graph/network inspection, whilst such comparisons were not reported in the Romanian study [ 35 ]. Thirdly, centrality indices informing the present findings were referring to the extended network of SMA and distress behaviours, and not the narrower network of SMA behaviours only [ 35 ]. Thus, it is likely that whilst salience and withdrawal may be more central in the context of SMA behaviours, without taking into consideration concurrent depression, anxiety and stress behaviours; tolerance and mood modification maybe more pivotal in the broader context of SMA and distress comorbidities together. Finally, it is also likely that cultural differences between the two samples may alternate the experience of SMA between the populations, such that withdrawal and salience maybe more central for the Romanian sample [ 35 ]. Such differences inevitably invite further investigation regarding the cross-cultural invariance of the SMA network, as with other behavioural addictions related to the abuse of digital media (see gaming disorder [ 53 , 54 ]).

The current findings were also revealing considering the differential diagnosis concerns referring to SMA behaviours constituting primarily a secondary symptom of distress behaviours related to depression, anxiety and stress, rather than a distinct condition itself [ 54 ]. Specifically, network models across both time points consistently revealed two distinguishable clusters of nodes within the broader network, clearly dividing SMA and distress behaviours. Thus, although distress and SMA behaviours appeared related, they were not blended/mixed in a way that would advocate a common classification [ 41 ].

Furthermore, the current study also expands available knowledge regarding the relationship between SMA and distress, via the examination of the ‘bridging centrality’ of the various symptoms [ 54 ]. Primarily, the connections between the SMA behaviours of mood-modification and conflict, with anxiety and stress, appear to have acted as comorbidity bridges, featuring the highest expected influence bridge centrality values amongst their respective subnetworks (i.e., the number and strength of connections to other subnetworks). In addition, withdrawal symptoms served as a “go-between” in this link between subnetworks, with the highest betweenness bridge centrality (the amount of and strength of the connections between SMA and distress that used it as a go-between). Thus, these findings imply that the need to moderate one’s negative feelings via SMA, and/or the stress/anxiety related to the occurrence of functional impairments in a person’s life (e.g., conflicts with others due to SMA behaviours) could operate as the main connection points in the cyclical relationship between distress and SMA. This hypothesized process aligns with evidence relevant to other behavioural addictions [ 55 ]. Thus, one could support that stressed and anxious individuals may excessively use social media to cope with, and to modify their anxious manifestations, suffering conflicts with their real-world obligations and desires as a result of that use. The latter might induce more stress and anxiety, and perhaps even more when withdrawals ensue after failed attempts to reduce use. Further SMA and depression symptoms could follow as a result of the development of conflict/mood-modification and stress/anxiety respectively. This interpretation is reinforced by prior cross-sectional and longitudinal research in the field of addiction psychology that: a) portrays stress, as well as unhealthy coping mechanisms in response to stress, to operate as primary causes of addictions [ 56 , 57 , 58 , 59 ] and; b) proposes the need to escape from negative moods as highly associated to addictive tendencies [ 6 ]. These results may thus imply, that clinicians treating clients with comorbid SMA/distress, may wish to target these bridging symptoms in particular, in order to cut any possible bidirectional feedback loops between these disorders.

On a separate note, the depression node was found to display a seeming lack of importance in the network. Specifically, depressive behaviours were shown to possess significantly lower general centrality and bridge centrality, implying that they may not have as a formative effect on the experience of SMA symptoms, as stress and anxiety. Furthermore, depression displayed a negative association with withdrawal symptoms, the only negative association in the network. While initially this may seem to contradict prior research associating depression and social media use [ 41 ], this is not necessarily the case. Depression still displayed a positive association with the symptom of mood-modification, accommodating prior research linking addiction with the use of social media as a relief mechanism [ 6 ]. Furthermore, while at first it might seem oxymoronic that the experience of depression might associate with a reduction in SMA withdrawal symptoms, this may not be the case. It is likely that, as with other addictions, those experiencing depression are less able to attempt containing their addictive patterns, whilst when/if they do make attempts, those attempts may be less successful and thus they do not experience withdrawal [ 60 ]. Those experiencing depression have depressed mood, lack of energy and a lack of motivation all of which negate action and make it harder to quit or make an attempt to cease problematic behaviours [ 12 , 16 ]. Furthermore, a lack of direct impact of depressive experiences on SMA symptoms in the network does not imply a lack of impact overall. In the current findings, depression still displayed very strong relationships with stress and anxiety, allowing it to influence SMA via its influence on these symptoms. However, as causality associations were not directly explored in the current study, these interpretations require further additional evidence to be better supported.

Limitations and further study recommendations

Despite the relevant findings reported here, such conclusions and implications may need to be considered in the light of the several limitations of the present study. Firstly, a convenience, community, western/English speaking sample of adult social media users was collected, potentially restricting the generalization of the findings to non-western, children-adolescent and clinical populations. Secondly, findings were exclusively based on self-reported, psychometric scales and thus risks of subjectivity or self-reporting errors cannot be excluded. Therefore, considering that there is evidence of objectively measuring social media use [ 61 , 62 ] future researchers may wish to consider examining non-adult, non-western and/or clinical samples via multimethod designs entailing additionally physical actigraphy and/or digital monitoring means to further expand the available knowledge. Thirdly, this study focused exclusively on the network between PSMU and distress; however, other variables have been associated with PSMU and should be considered in future studies (e.g., fear of missing out [ 63 ]).

Conclusions and implications

Overall, the findings of the present study appear to have added important knowledge across three areas surrounding problematic social media usage. These involve the conceptualization of this debated condition, its differential diagnosis and key behavioural symptoms informing it [ 34 , 48 ]. In particular, the current findings support: a) the applicability of the SMA definition as a construct/condition naturally occurring based on an underpinning network cluster of behaviours; b) a distinct association between SMA symptoms and distress behaviours related to depression, anxiety and stress, which advocates the separate classification of SMA as a psychopathological condition and; c) the role of mood-modification drives and functional impairment/conflicts with others as the connecting/linking points with stress/anxiety behaviours in the formation of SMA behaviours. Accordingly, results pose three significant taxonomic, assessment and prevention/intervention implications. Firstly, the consideration of SMA as a distinct diagnostic category is strengthened. Secondly, assessment of comorbid stress and anxiety manifestations appears to require priority when addressing clients presenting with problematic social media usage. Thirdly, though individuals of different ages and sexes tend to use social media in different ways, and thus likely experience SMA in different fashions, the effects of age and sex on SMA symptoms and their relationship with distress was not explored. This represents an important and interesting area of future study that deserves to be examined.

Availability of data and materials

The data and materials used in this study are available in this link https://github.com/Vas08011980/SNSNETWORK/blob/main/html.Rmd

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VS has received the Australian Research Council, Discovery Early Career Researcher Grant/Award Number: DE210101107.

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DT-P contributed to the article’s conceptualization, data curation, formal analysis, methodology, project administration, and writing of the original draft. JD, RG and VS contributed to the article’s conceptualization, data curation, writing, review, and editing the final draft and project administration. DZ contributed to the review and edit of the final form of the manuscript.

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Tullett-Prado, D., Doley, J.R., Zarate, D. et al. Conceptualising social media addiction: a longitudinal network analysis of social media addiction symptoms and their relationships with psychological distress in a community sample of adults. BMC Psychiatry 23 , 509 (2023). https://doi.org/10.1186/s12888-023-04985-5

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• Longitudinal network analysis
• Psychological distress
• Social media addiction

BMC Psychiatry

ISSN: 1471-244X

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A review of theories and models applied in studies of social media addiction and implications for future research

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• 1 School of Information, The University of Texas at Austin, USA. Electronic address: [email protected].
• 2 School of Information, The University of Texas at Austin, USA. Electronic address: [email protected].
• PMID: 33268185
• DOI: 10.1016/j.addbeh.2020.106699

With the increasing use of social media, the addictive use of this new technology also grows. Previous studies found that addictive social media use is associated with negative consequences such as reduced productivity, unhealthy social relationships, and reduced life-satisfaction. However, a holistic theoretical understanding of how social media addiction develops is still lacking, which impedes practical research that aims at designing educational and other intervention programs to prevent social media addiction. In this study, we reviewed 25 distinct theories/models that guided the research design of 55 empirical studies of social media addiction to identify theoretical perspectives and constructs that have been examined to explain the development of social media addiction. Limitations of the existing theoretical frameworks were identified, and future research areas are proposed.

Keywords: Facebook addiction; Internet addiction; Literature review; Problematic use; Social media addiction; Theoretical framework.

Copyright © 2020 Elsevier Ltd. All rights reserved.

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• Behavior, Addictive*
• Internet Addiction Disorder
• Interpersonal Relations
• Social Media*

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Exploring the Association Between Social Media Addiction and Relationship Satisfaction: Psychological Distress as a Mediator

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• Published: 05 October 2021
• Volume 21 , pages 2037–2051, ( 2023 )

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• Begum Satici   ORCID: orcid.org/0000-0003-2161-782X 1 ,
• Ahmet Rifat Kayis   ORCID: orcid.org/0000-0003-4642-7766 2 &
• Mark D. Griffiths   ORCID: orcid.org/0000-0001-8880-6524 3  

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Social media use has become part of daily life for many people. Earlier research showed that problematic social media use is associated with psychological distress and relationship satisfaction. The aim of the present study was to examine the mediating role of psychological distress in the relationship between social media addiction (SMA) and romantic relationship satisfaction (RS). Participants comprised 334 undergraduates from four mid-sized universities in Turkey who completed an offline survey. The survey included the Relationship Assessment Scale, the Social Media Disorder Scale, and the Depression Anxiety and Stress Scale. According to the results, there were significant correlations between all variables. The results also indicated that depression, anxiety, and stress partially mediated the impact of SMA on RS. Moreover, utilizing the bootstrapping procedure the study found significant associations between SMA and RS via psychological distress. Consequently, reducing social media use may help couples deal with romantic relationship dissatisfaction, thereby mitigating their depression, anxiety, and stress.

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Establishing social relationships is one of the basic needs of human beings (Heaney & Israel, 2008 ). How this basic need is met can vary greatly. In particular, technological developments, such as computers, the Internet, and smartphones have created new ways for people to communicate with each other. One of the most successful new means of communication is through social media. Social media involves many different communication (i.e., social networking) platforms. Among the most popular are platforms in Western countries are Facebook, Twitter, Instagram, and YouTube. These sites, which are accessed via the Internet, provide many opportunities for communication, such as voice and video messaging, photograph and video sharing, and creating profiles, through which individuals can introduce themselves and make connections with others.

The communication opportunities brought about by social networking sites (SNSs) allow for the development of social relationships (Fuchs, 2017 ; Hazar, 2011 ; Valentini, 2015 ). In addition, social media is used for a wider variety of purposes, including obtaining information, communicating, entertainment, playing games, and sharing photos, videos, and music (Griffiths, 2012 ). However, excessive use of social media including SNSs can cause negative effects (Griffiths, 2013 ; van den Eijnden et al., 2016 ). This phenomenon, which is sometimes referred as “social media addiction,” is defined as the irrational and excessive use of social media at a level that negatively affects the daily life of the user (Griffiths, 2012 ). When social media use reaches the level of addiction, it can prevent the establishment of real, face-to-face social relationships (Glaser et al., 2018 ; Kuss & Griffiths, 2017 ; Young, 2019 ). When general characteristics of social media addiction have been examined, it has been found that individuals tend to have restless thoughts concerning the urges and craving to be on social media, lose their self-control over their use of social media, spend excessive amounts of time staying on (or thinking about) social media which in turn lead to negative impacts on their relationships with their families and friends, and compromise their occupation and/or education (Andreassen et al., 2012 ; Griffiths et al., 2014 ). Therefore, examining social media addiction in terms of its effect on human relationships and mental health is an important pursuit.

Theoretical Framework

Social media addiction and relationship satisfaction.

Research into the effects of social media addiction on romantic relationships has increased (Abbasi, 2019a ; Demircioğlu & Köse, 2018 ). The literature suggests that social media addiction negatively affects romantic relationships due to its tendency to create jealousy and suspicion and facilitate deception between married couples and committed partners (Abbasi, 2019b ). Additionally, problematic social media use can hinder the development of face-to-face relationships (Glaser et al., 2018 ; Kuss & Griffiths, 2017 ; Pollet et al., 2011 ; Young, 2019 ). Therefore, it is possible that some couples’ relationships may become disrupted and that dissatisfaction may be experienced. In some cases, not only has social media use decreased the amount of relationships that individuals have in person, but it has also markedly impaired the quality of the time spent together. Therefore, it can be concluded that some couples may experience relationship dissatisfaction.

Similarly, social media addiction can result in low relationship satisfaction due to the existence of online alternative centers of attraction and investments of time and emotion outside the bilateral relationship in individuals aged between 18 and 73 years (Abbasi, 2019a ). In addition, social media addiction has also been associated with physical and emotional infidelity, romantic separation, decline in the quality of romantic relationships, and relationship dissatisfaction (e.g., Abbasi, 2019a , b ; Demircioğlu & Köse, 2018 ; Valenzuela et al., 2014 ). Therefore, these aforementioned findings indicate that social media addiction negatively affects relationship satisfaction.

Social Media Addiction and Psychological Distress

One of the most important consequences of social media addiction is the mental health of individuals. When social media use reaches the level of addiction, it can create stress and negatively affect mental health rather than being a method of healthy coping. This occurs because social media addiction triggers social media fatigue and, as a result, individuals may experience anxiety and depression (Dhir et al., 2018 ). Social media users may use social media as a means of diversion in order to cope with stress (van den Eijnden et al., 2016 ). However, social media addicts give a lower priority to hobbies, daily routines, and close relationships (Tutgun-Ünal & Deniz, 2015 ) which in turn lead to problems with daily functioning, completion of tasks, and relationship maintenance. This puts such individuals at risk for experiencing negative physical and psychological health.

In fact, some research has claimed that social media addiction triggers psychological distress factors, such as depression, anxiety (Woods & Scott, 2016 ), and stress (Larcombe et al., 2016 ). In addition, a meta-analysis synthesizing the findings of 13 studies found that social media addiction may increase depression, anxiety, and stress levels (Keles et al., 2020 ). In both meta-analyses and cross-sectional studies, it has been found that social media addiction can increase psychological distress (e.g., Hou et al., 2019 ; Keles et al., 2020 ; Marino et al., 2018 ; Meena et al., 2015 ). In sum, these findings consistently associate social media addiction with psychological distress.

Psychological Distress and Relationship Satisfaction

Individuals experiencing psychological discomfort often have non-functional communication styles characterized by highly negative behaviors, such as criticism, complaining, hostility, defensiveness, and tendency to end relationships. They also experience problems actively listening to others (Fincham et al., 2018 ). In this respect, psychological distress prevents healthy communication in relationships, and a lack of healthy communication may cause conflicts that can embitter psychological distress between couples. Such a situation can continue in a cyclical manner that prevents relationship satisfaction. In romantic relationships, couples are supposed to fulfill their partners’ emotional needs (Willard, 2011 ). When individuals have psychological problems due to social media addiction, they will ignore their partner’s emotional needs because they would be trying to deal with their own problems, which, in turn, may lead to lower relationship satisfaction.

When psychological distress and romantic relationship satisfaction are examined, it can be seen that much psychological distress, such as major depression, panic disorder, social phobia, general anxiety disorder, post-traumatic stress disorder, and mood disorder, positively predict relationship dissatisfaction (Whisman, 1999 ). On the other hand, it can also be seen that individuals who are sensitive to negative affect in romantic relationships and who can successfully stop these emotions early on and cope with their feelings are satisfied with their relationships (Fincham et al., 2018 ).

Couples who have high levels of stress are reported to experience less satisfaction in their relationships (Bodenmann et al., 2007 ). In addition, it is known that depression negatively predicts relationship satisfaction (Cramer, 2004a , b ; Tolpin et al., 2006 ). Therefore, it appears that psychological distress negatively affects relationship satisfaction.

The Present Study

The prevalence of the use of the internet and Internet-related tools has consistently increased year on year (Roser et al., 2020 ). Even though the social media use is widespread and facilitates communication when it is used normally, it can negatively affect daily life when it is used excessively by some individuals. Literature reviews have shown that social media addiction has been mostly studied in East Asian countries like China, Japan, and South Korea (e.g., Bian & Leung, 2015 ; Kwon et al., 2013 ; Tateno et al., 2019 ). In this respect, when the prevalence of social media use among Turkish people and the different cultural context of the present study are considered, the findings would arguably make important contributions to the current literature. Furthermore, the present study appears to be the first to examine the mediating role of psychological distress in the relationship between social media addiction and romantic relationship satisfaction.

Older aged adolescents and emerging adults are inextricably connected with technology in terms of their social media use and stand out as an important risk group in relation to problematic social media use (Griffiths et al., 2014 ). Many young adults closely follow technological developments and often adopt every innovation that arises into their lives without wasting time (Kuyucu, 2017 ). When such use becomes problematic, some individuals experience serious difficulty in maintaining their mental health. For example, cross-sectional studies among adolescents (Woods & Scott, 2016 ) and young adults (Larcombe et al., 2016 ) have found that social media addiction can lead to stress, anxiety, and depression. Moreover, the establishment of close relationships as a young adult is an important stage of emotional and social development (Cashen & Grotevant, 2019 ; Orenstein, & Lewis, 2020 ). Romantic relationship satisfaction may be seen as an important indicator of young people’s ability to engage in intimacy in a healthy manner (Orenstein & Lewis, 2020 ). Therefore, the findings obtained as a result of examining the relationships between social media addiction, psychological distress, and romantic relationship satisfaction among young people will contribute to an understanding of the associations between the psychological and social variables regarding maintenance of their mental health and their success in establishing close relationships.

In previous studies of the variables examined in the present study, even though studies examining the three variables dichotomously have been conducted (e.g., Abbasi, 2019a , b ; Bodenmann et al., 2007 ; Keles et al., 2020 ; Larcombe et al., 2016 ; Whisman, 1999 ), no research examining social media addiction, psychological distress (depression, anxiety and stress), and romantic relationship satisfaction together has been published. In particular, there is no study examining the role of psychological distress mediating between social media addiction and relationship satisfaction. In this respect, the results of the present study may also allow the findings of previous studies (which have been conducted with the aim of identifying the relationship between these variables) to be evaluated from a wider perspective.

Consequently, given the aforementioned theoretical explanations and the research findings, it has been demonstrated that social media addiction appears to induce both psychological distress and a low level of romantic relationship satisfaction (e.g., Demircioğlu & Köse, 2018 ; Woods & Scott, 2016 ). This is due to the deterioration of individuals’ mental health that can arise as a result of social media addiction (Baker & Algorta, 2016 ; Dhir et al., 2018 ), and in contrast to the advantages of developing relationships, it can lead to romantic relationship dissatisfaction (Abbasi, 2019b ; Muise et al., 2009 ). Therefore, when the relationships between social media addiction, psychological distress, and romantic relationship satisfaction are evaluated simultaneously, psychological distress may represent a mediating variable between social media addiction and romantic relationship satisfaction. Consequently, it was hypothesized that psychological distress would mediate the association between social media addiction and relationship satisfaction.

Participants and Procedure

The present cross-sectional study was carried out on a convenience sample of university students from three universities that are located in the west, middle, and east part of Turkey. A total of 350 surveys were originally distributed. Of these, 16 participants were removed because of incomplete data, yielding a final sample of 334 participants aged between 18 and 29 years ( M  = 20.71 years, SD  = 2.18). The participants comprised 214 females (64%) and 120 males (36%), of which 90 were freshmen, 87 were sophomores, 84 were junior students, and 73 were senior students. Participants reported that they were currently in a romantic relationship and reported having an average of 3.21 romantic relationships to date ( SD  = 2.21). Table 1 shows the detailed demographic characteristics of the participants. Written informed consent was obtained from the volunteer participants prior to participation in the study. Research participants were assured of the confidentiality of the collected data. Data collection was carried out through a “paper-and-pencil” survey in the classroom environment. The surveys took less than 15 min to complete.

Relationship Assessment Scale (RAS)

The RAS was designed to assess general relationship satisfaction (Hendrick, 1988 ). Items (e.g., “In general, how satisfied are you with your relationship?”) utilize a seven-point Likert scale ranging from 1 ( low ) to 7 ( high ). The total score ranges from 7 to 49. The higher the score, the higher the relationship satisfaction. Hendrick ( 1988 ) reported very good reliability. The RAS was adapted into Turkish by Curun ( 2001 ) with very good internal consistency. In the present study, the internal consistency of this scale was also good ( α  = 0.80).

Social Media Disorder Scale (SMD)

The SMD was designed to assess overall social media addiction, and the items were developed by adapting the DSM-5 criteria for Internet gaming disorder (van den Eijnden et al., 2016 ). This scale includes nine items (e.g., “… regularly found that you can't think of anything else but the moment that you will be able to use social media again?”) to which participants indicate their level of agreement on a five-point Likert scale ranging from 0 ( never ) to 4 ( always ). The total score ranges from 0 to 36. The higher the score, the higher the risk of social media addiction. The SMD was adapted to Turkish by Savci et al. ( 2018 ) and has very good internal consistency. In the present study, the internal consistency of this scale was also very good ( α  = 0.88).

Depression Anxiety and Stress Scale (DASS-21)

The DASS was designed to assess the level of psychological distress (Henry & Crawford, 2005 ). The scale consists of 21 items that are rated on a four-point Likert scale from 0 ( did not apply to me at all ) to 3 ( applied to me very much or most of the time ) and comprises three sub-scales: depression (seven items; e.g., “I found it difficult to work up the initiative to do things”), anxiety (seven items; e.g., “I felt I was close to panic”), and stress (seven items; “I found myself getting agitated”). The scores range from 0 to 21 for each sub-scale. The DASS-21 subscales’ scores were multiplied by two based on Lovibund and Lovibond’s ( 1995 ) suggestion to the cut-offs (see Appendix 1 ). The DASS-21 was adapted to Turkish by Yilmaz et al. ( 2017 ) with good to very good internal consistencies. In the present study, the internal consistency of the sub-scales were all very good ( α  = 0.89, 0.82, 0.85, respectively).

Statistical Analyses

Pearson correlations, means, and standard deviations were examined as preliminary analyses for all study variables. To examine whether the association between social media addiction and relationship satisfaction was mediated by psychological distress, the mediation model was calculated using the PROCESS macro (model 4), developed by Hayes ( 2018 ). As recommended by Hayes ( 2018 ), all regression/path coefficients are in unstandardized form. A total of 10,000 bootstrap samples were generated and bias corrected 95% confidence intervals calculated.

Written informed consent was obtained from the volunteer participants prior to participation in the study. This research was approved by Artvin Coruh University Scientific Research and Ethical Review Board (REF: E.5375).

Descriptive Statistics

Bivariate Pearson correlations among study variables were investigated (see Table 2 ). As expected, social media addiction was significantly and positively correlated with depression, anxiety, and stress. There was a significant negative correlation between social media addiction and relationship satisfaction.

Results indicated that 156 participants had no depressive symptoms (46.7%), 54 participants had mild depressive symptoms (16.2%), and the remainder had depressive symptoms (16.5% moderate, 9.9% severe, and 10.8% extremely severe). Moreover, 101 participants had no anxiety symptoms (30.2%), 30 participants had mild anxiety symptoms (9.0%), and the remainder had anxiety symptoms (20.4% moderate, 15.6% severe, and 24.9% extremely severe). Finally, 163 participants had no stress symptoms (48.8%), 47 participants had mild depressive symptoms (14.1%), and the remainder had stress symptoms (17.7% moderate, 12.6% severe, and 6.9% extremely severe) (see Appendix 1 ).

Statistical Assumption Tests

Prior to mediation analysis, statistical assumptions were evaluated. Skewness and kurtosis values (> ± 2; George & Mallery, 2003 ) were checked for normality, and there were no violations (see Table 3 ). All reliability coefficients were above Nunnally and Bernstein’s ( 1994 ) 0.70 criterion. Multicollinearity was checked with variance inflated factor (VIF), tolerance, and Durbin-Watson (DW) value. The results showed that VIF ranged from 1.47 to 2.09 and tolerance ranged from 0.48 to 0.87. These findings also showed that there was no multiple linearity problem according to Field’s ( 2013 ) recommendation. Also, the DW value was 1.82 indicating no significant correlations between the residuals.

Mediation Analyses

Applying PROCESS model 4, the analysis assessed whether psychological distress mediated the relationship between social media addiction and relationship satisfaction (see Table 4 ; Fig.  1 ). The results showed a significant total direct effect ( path c ; without mediator) of social media addiction on relationship satisfaction (B =  − 0.36, t (334)  =  − 4.74, p  = 0.001, 95% CI =  − 0.51, − 0.21), significant direct effect ( path c ; with mediator) (B =  − 0.16, t (334)  =  − 2.11, p  = 0.03, 95% CI =  − 0.04, − 0.01), and a significant indirect effect via psychological distress (total B =  − 0.20, 95% CI =  − 0.29, − 0.12).

The mediation model. * p  < .05. ** p  < .001

The results also showed that the social media addiction was associated with higher depression scores (path a 1 ; B = 0.23, p  = 0.001), anxiety scores (path a 2 ; B = 0.23, p  = 0.001), and stress scores (path a 3 ; B = 0.27, p  = 0.001), and these, in turn, were negatively associated with relationship satisfaction (path b 1, b 2, b 3 ; B =  − 0.28, B =  − 0.28, B =  − 0.26, all p values < 0.05, respectively).

In contemporary society, rapidly developing technology has entered human life, but some individuals may have difficulty in adapting to the innovations brought by such technology. Consequently, some individuals may experience psychological and social problems. Social media use, which has markedly increased in the past decade, can cause psychological distress (e.g., Keles et al., 2020 ; Marino et al., 2018 ) and the deterioration of interpersonal relationships (e.g., Glaser et al., 2018 ; Kuss & Griffiths, 2017 ; Young, 2019 ) among a minority of individuals. In this context, the main purpose of the present study was to evaluate the mediating role of psychological distress in the relationship between social media addiction and romantic relationship satisfaction.

According to the findings, a high level of social media addiction leads to a decrease in relationship satisfaction. Consequently, the first hypothesis was confirmed. A recent study conducted by Abbasi ( 2019b ) found that social media addiction was negatively associated with romantic relationship commitment. In another recent study, it was emphasized that social media addiction results in deception between couples through social media and may lead to the deterioration of relationships as a consequence (Abbasi, 2019a ). In addition, social media addiction not only leads to physical and emotional deception but also appears to negatively impact on the quality of romantic relationships (Demircioğlu & Köse 2018 ; Valenzuela et al., 2014 ). Therefore, the findings obtained in the present study are in line with the findings of previous research.

In the study here, the findings showed that a high level of social media addiction appears to result in psychological distress. Dhir et al. ( 2018 ) argued that social media addiction triggers social media fatigue, leading to anxiety and depression. Similarly, social media addiction has been found to be associated with depression, anxiety (Woods & Scott, 2016 ) and stress (Larcombe et al., 2016 ). In addition, a recent meta-analysis also concluded that social media addiction is closely and positively associated depression, anxiety and stress (Marino et al., 2018 ). Therefore, the findings of the present study are consistent with previous research.

Thirdly, the findings indicate that individuals who experience psychological distress have a low level of satisfaction in their romantic relationships. Whisman ( 1999 ) found that psychological distress positively predicted relationship dissatisfaction. It has also been suggested that couples with high levels of stress experience dissatisfaction in their romantic relationships (Bodenmann et al., 2007 ). In addition, there have also been a number of studies which indicate that the relationship satisfaction of individuals with high levels of depression is low (Cramer, 2004a , b ; Tolpin et al., 2006 ). In this respect, the findings obtained from the present study are similar to the findings of the previous studies.

Within the scope of this study, it was hypothesized that psychological distress would mediate between social media addiction and relationship satisfaction. In this sense, the study showed that social media addiction predicted romantic relationship satisfaction, partially mediated by psychological distress. Consequently, the fourth hypothesis of the research was also confirmed. No previous studies have examined the effect of psychological distress in the relationship between social media addiction and relationship satisfaction. However, there are research findings which provide evidence that social media addiction predicts both psychological distress (e.g., Larcombe et al., 2016 ; Woods & Scott, 2016 ) and relationship dissatisfaction (e.g., Demircioğlu & Köse, 2018 ; Valenzuela et al., 2014 ) and that psychological distress predicts relationship dissatisfaction (e.g., Bodenmann et al., 2007 ; Whisman, 1999 ). Due to the consideration of a variable’s mediating conditions (Barron & Kenny, 1986 ), it may be asserted that the findings of the previous studies in the literature and the findings of this research are consistent. Furthermore, it has been demonstrated that technological addiction, such as Internet addiction and smartphone addiction, is associated with psychological distress (McNicol & Thorsteinsson, 2017 ; Samaha & Hawi, 2016 ; Young & Rogers, 1998 ). Psychological distress may also predict variables such as closeness in relationships (Manne et al., 2010 ), dating violence (Cascardi, 2016 ), and social support (Robitaille et al., 2012 ) which are based on interpersonal relationships. It is therefore suggested that there is similarity between these findings and the findings of the present study. Consequently, it may be that the results of the studies conducted previously support the findings of this the present research indirectly, if not directly.

In the study here, the mediating role of psychological distress in the relationship between social media addiction and romantic relationship satisfaction was investigated. However, there could be some other variables that can mediate the relationship between social media addiction and romantic relationship satisfaction. For instance, romantic relationships are considered interpersonal (Knap et al., 2002 ); therefore, it can be assumed that interpersonal relationships and communication skills can be seen as potential mediators of the relationship between social media addiction and romantic relationship satisfaction. Additionally, given that psychological problems are the indicators of poor mental health (American Psychiatric Association, 2013 ), it can be assumed that variables (i.e., other indicators of poor mental health such as burnout, somatization, and hostility) would mediate the relationship between social media addiction and romantic relationship satisfaction. Therefore, future studies should investigate such relationships more closely.

When the role of social media addiction in the development of psychological distress is considered, it is necessary for social media addiction to be included in the process of forming the content of the intervention programs that aim to treat psychological distress. As such, it is interesting that an intervention program aimed at decreasing the level of social media addiction was also found to have a beneficial impact on individuals’ mental health (Hou et al., 2019 ). Likewise, the treatment of couples’ social media usage habits in family and couple therapies may be effective in terms of the efficacy of the therapy, since social media addiction decreases satisfaction in romantic relationships. Moreover, given the mediation relationships in the present research, the results may provide a more holistic viewpoint for mental health professionals which consider all of the three variables (social media addiction, psychological distress, and romantic relationship satisfaction) rather than a focus on only one. In this context, the following suggestions are made: to prevent social media addiction, effective Internet use skills can be taught to couples. In addition, awareness-raising skills such as yoga and meditation could be provided to individuals to protect them from social media addiction and psychological distress.

In terms of the study’s participatory group, it is significant that social media addiction (Kittinger et al., 2012 ; Koc & Gulyagci, 2013 ), psychological distress (Canby et al., 2015 ; Larcombe et al., 2016 ), and relationship satisfaction problems (Bruner et al., 2015 ; Roberts & David, 2016 ) are frequently experienced by university students. Consequently, the findings of the present study may be of particular help to specialists who work in the psychological counseling centers of universities. Within this framework, meetings, conferences, and psycho-educational group activities could be carried out to improve relationship building skills, as well as activities preventing social media addiction and psychological distress.

The present study has some limitations. Firstly, the data comprised self-report scales, which may decrease internal reliability, a limitation which may be prevented through the use of different methods of data collection. Secondly, the generalizability of the findings is limited since the sample was based on convenience sampling. Thirdly, the research design was cross-sectional. This may make it difficult to explain the cause-effect relationship of variables in the study, and therefore, experimental and longitudinal studies are recommended in future research which should examine the relationship between these variables. Finally, only the mediating role of psychological distress was examined in the research. Other possible mediating variables were not examined.

In the present research, the mediation of psychological distress in the relationship between social media addiction and romantic relationship satisfaction was empirically tested. Results showed that social media addiction predicted the partial mediation of depression, anxiety, and stress on romantic relationship satisfaction. In other words, social media addiction apparently increased individuals’ depression, anxiety, and stress levels, and this situation decreased the level of satisfaction in individual’s romantic relationships. In the present study, psychological and social variables were examined simultaneously. Overall, this study suggests that social media addiction may have a meaningful but negative impact on romantic relationship satisfaction via depression, anxiety, and stress.

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Satici, B., Kayis, A.R. & Griffiths, M.D. Exploring the Association Between Social Media Addiction and Relationship Satisfaction: Psychological Distress as a Mediator. Int J Ment Health Addiction 21 , 2037–2051 (2023). https://doi.org/10.1007/s11469-021-00658-0

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SYSTEMATIC REVIEW article

Research trends in social media addiction and problematic social media use: a bibliometric analysis.

• 1 Sasin School of Management, Chulalongkorn University, Bangkok, Thailand
• 2 Business Administration Division, Mahidol University International College, Mahidol University, Nakhon Pathom, Thailand

Despite their increasing ubiquity in people's lives and incredible advantages in instantly interacting with others, social media's impact on subjective well-being is a source of concern worldwide and calls for up-to-date investigations of the role social media plays in mental health. Much research has discovered how habitual social media use may lead to addiction and negatively affect adolescents' school performance, social behavior, and interpersonal relationships. The present study was conducted to review the extant literature in the domain of social media and analyze global research productivity during 2013–2022. Bibliometric analysis was conducted on 501 articles that were extracted from the Scopus database using the keywords social media addiction and problematic social media use. The data were then uploaded to VOSviewer software to analyze citations, co-citations, and keyword co-occurrences. Volume, growth trajectory, geographic distribution of the literature, influential authors, intellectual structure of the literature, and the most prolific publishing sources were analyzed. The bibliometric analysis presented in this paper shows that the US, the UK, and Turkey accounted for 47% of the publications in this field. Most of the studies used quantitative methods in analyzing data and therefore aimed at testing relationships between variables. In addition, the findings in this study show that most analysis were cross-sectional. Studies were performed on undergraduate students between the ages of 19–25 on the use of two social media platforms: Facebook and Instagram. Limitations as well as research directions for future studies are also discussed.

Introduction

Social media generally refers to third-party internet-based platforms that mainly focus on social interactions, community-based inputs, and content sharing among its community of users and only feature content created by their users and not that licensed from third parties ( 1 ). Social networking sites such as Facebook, Instagram, and TikTok are prominent examples of social media that allow people to stay connected in an online world regardless of geographical distance or other obstacles ( 2 , 3 ). Recent evidence suggests that social networking sites have become increasingly popular among adolescents following the strict policies implemented by many countries to counter the COVID-19 pandemic, including social distancing, “lockdowns,” and quarantine measures ( 4 ). In this new context, social media have become an essential part of everyday life, especially for children and adolescents ( 5 ). For them such media are a means of socialization that connect people together. Interestingly, social media are not only used for social communication and entertainment purposes but also for sharing opinions, learning new things, building business networks, and initiate collaborative projects ( 6 ).

Among the 7.91 billion people in the world as of 2022, 4.62 billion active social media users, and the average time individuals spent using the internet was 6 h 58 min per day with an average use of social media platforms of 2 h and 27 min ( 7 ). Despite their increasing ubiquity in people's lives and the incredible advantages they offer to instantly interact with people, an increasing number of studies have linked social media use to negative mental health consequences, such as suicidality, loneliness, and anxiety ( 8 ). Numerous sources have expressed widespread concern about the effects of social media on mental health. A 2011 report by the American Academy of Pediatrics (AAP) identifies a phenomenon known as Facebook depression which may be triggered “when preteens and teens spend a great deal of time on social media sites, such as Facebook, and then begin to exhibit classic symptoms of depression” ( 9 ). Similarly, the UK's Royal Society for Public Health (RSPH) claims that there is a clear evidence of the relationship between social media use and mental health issues based on a survey of nearly 1,500 people between the ages of 14–24 ( 10 ). According to some authors, the increase in usage frequency of social media significantly increases the risks of clinical disorders described (and diagnosed) as “Facebook depression,” “fear of missing out” (FOMO), and “social comparison orientation” (SCO) ( 11 ). Other risks include sexting ( 12 ), social media stalking ( 13 ), cyber-bullying ( 14 ), privacy breaches ( 15 ), and improper use of technology. Therefore, social media's impact on subjective well-being is a source of concern worldwide and calls for up-to-date investigations of the role social media plays with regard to mental health ( 8 ). Many studies have found that habitual social media use may lead to addiction and thus negatively affect adolescents' school performance, social behavior, and interpersonal relationships ( 16 – 18 ). As a result of addiction, the user becomes highly engaged with online activities motivated by an uncontrollable desire to browse through social media pages and “devoting so much time and effort to it that it impairs other important life areas” ( 19 ).

Given these considerations, the present study was conducted to review the extant literature in the domain of social media and analyze global research productivity during 2013–2022. The study presents a bibliometric overview of the leading trends with particular regard to “social media addiction” and “problematic social media use.” This is valuable as it allows for a comprehensive overview of the current state of this field of research, as well as identifies any patterns or trends that may be present. Additionally, it provides information on the geographical distribution and prolific authors in this area, which may help to inform future research endeavors.

In terms of bibliometric analysis of social media addiction research, few studies have attempted to review the existing literature in the domain extensively. Most previous bibliometric studies on social media addiction and problematic use have focused mainly on one type of screen time activity such as digital gaming or texting ( 20 ) and have been conducted with a focus on a single platform such as Facebook, Instagram, or Snapchat ( 21 , 22 ). The present study adopts a more comprehensive approach by including all social media platforms and all types of screen time activities in its analysis.

Additionally, this review aims to highlight the major themes around which the research has evolved to date and draws some guidance for future research directions. In order to meet these objectives, this work is oriented toward answering the following research questions:

(1) What is the current status of research focusing on social media addiction?

(2) What are the key thematic areas in social media addiction and problematic use research?

(3) What is the intellectual structure of social media addiction as represented in the academic literature?

(4) What are the key findings of social media addiction and problematic social media research?

(5) What possible future research gaps can be identified in the field of social media addiction?

These research questions will be answered using bibliometric analysis of the literature on social media addiction and problematic use. This will allow for an overview of the research that has been conducted in this area, including information on the most influential authors, journals, countries of publication, and subject areas of study. Part 2 of the study will provide an examination of the intellectual structure of the extant literature in social media addiction while Part 3 will discuss the research methodology of the paper. Part 4 will discuss the findings of the study followed by a discussion under Part 5 of the paper. Finally, in Part 7, gaps in current knowledge about this field of research will be identified.

Literature review

Social media addiction research context.

Previous studies on behavioral addictions have looked at a lot of different factors that affect social media addiction focusing on personality traits. Although there is some inconsistency in the literature, numerous studies have focused on three main personality traits that may be associated with social media addiction, namely anxiety, depression, and extraversion ( 23 , 24 ).

It has been found that extraversion scores are strongly associated with increased use of social media and addiction to it ( 25 , 26 ). People with social anxiety as well as people who have psychiatric disorders often find online interactions extremely appealing ( 27 ). The available literature also reveals that the use of social media is positively associated with being female, single, and having attention deficit hyperactivity disorder (ADHD), obsessive compulsive disorder (OCD), or anxiety ( 28 ).

In a study by Seidman ( 29 ), the Big Five personality traits were assessed using Saucier's ( 30 ) Mini-Markers Scale. Results indicated that neurotic individuals use social media as a safe place for expressing their personality and meet belongingness needs. People affected by neurosis tend to use online social media to stay in touch with other people and feel better about their social lives ( 31 ). Narcissism is another factor that has been examined extensively when it comes to social media, and it has been found that people who are narcissistic are more likely to become addicted to social media ( 32 ). In this case users want to be seen and get “likes” from lots of other users. Longstreet and Brooks ( 33 ) did a study on how life satisfaction depends on how much money people make. Life satisfaction was found to be negatively linked to social media addiction, according to the results. When social media addiction decreases, the level of life satisfaction rises. But results show that in lieu of true-life satisfaction people use social media as a substitute (for temporary pleasure vs. longer term happiness).

Researchers have discovered similar patterns in students who tend to rank high in shyness: they find it easier to express themselves online rather than in person ( 34 , 35 ). With the use of social media, shy individuals have the opportunity to foster better quality relationships since many of their anxiety-related concerns (e.g., social avoidance and fear of social devaluation) are significantly reduced ( 36 , 37 ).

Problematic use of social media

The amount of research on problematic use of social media has dramatically increased since the last decade. But using social media in an unhealthy manner may not be considered an addiction or a disorder as this behavior has not yet been formally categorized as such ( 38 ). Although research has shown that people who use social media in a negative way often report negative health-related conditions, most of the data that have led to such results and conclusions comprise self-reported data ( 39 ). The dimensions of excessive social media usage are not exactly known because there are not enough diagnostic criteria and not enough high-quality long-term studies available yet. This is what Zendle and Bowden-Jones ( 40 ) noted in their own research. And this is why terms like “problematic social media use” have been used to describe people who use social media in a negative way. Furthermore, if a lot of time is spent on social media, it can be hard to figure out just when it is being used in a harmful way. For instance, people easily compare their appearance to what they see on social media, and this might lead to low self-esteem if they feel they do not look as good as the people they are following. According to research in this domain, the extent to which an individual engages in photo-related activities (e.g., taking selfies, editing photos, checking other people's photos) on social media is associated with negative body image concerns. Through curated online images of peers, adolescents face challenges to their self-esteem and sense of self-worth and are increasingly isolated from face-to-face interaction.

To address this problem the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) has been used by some scholars ( 41 , 42 ). These scholars have used criteria from the DSM-V to describe one problematic social media use, internet gaming disorder, but such criteria could also be used to describe other types of social media disorders. Franchina et al. ( 43 ) and Scott and Woods ( 44 ), for example, focus their attention on individual-level factors (like fear of missing out) and family-level factors (like childhood abuse) that have been used to explain why people use social media in a harmful way. Friends-level factors have also been explored as a social well-being measurement to explain why people use social media in a malevolent way and demonstrated significant positive correlations with lower levels of friend support ( 45 ). Macro-level factors have also been suggested, such as the normalization of surveillance ( 46 ) and the ability to see what people are doing online ( 47 ). Gender and age seem to be highly associated to the ways people use social media negatively. Particularly among girls, social media use is consistently associated with mental health issues ( 41 , 48 , 49 ), an association more common among older girls than younger girls ( 46 , 48 ).

Most studies have looked at the connection between social media use and its effects (such as social media addiction) and a number of different psychosomatic disorders. In a recent study conducted by Vannucci and Ohannessian ( 50 ), the use of social media appears to have a variety of effects “on psychosocial adjustment during early adolescence, with high social media use being the most problematic.” It has been found that people who use social media in a harmful way are more likely to be depressed, anxious, have low self-esteem, be more socially isolated, have poorer sleep quality, and have more body image dissatisfaction. Furthermore, harmful social media use has been associated with unhealthy lifestyle patterns (for example, not getting enough exercise or having trouble managing daily obligations) as well as life threatening behaviors such as illicit drug use, excessive alcohol consumption and unsafe sexual practices ( 51 , 52 ).

A growing body of research investigating social media use has revealed that the extensive use of social media platforms is correlated with a reduced performance on cognitive tasks and in mental effort ( 53 ). Overall, it appears that individuals who have a problematic relationship with social media or those who use social media more frequently are more likely to develop negative health conditions.

Social media addiction and problematic use systematic reviews

Previous studies have revealed the detrimental impacts of social media addiction on users' health. A systematic review by Khan and Khan ( 20 ) has pointed out that social media addiction has a negative impact on users' mental health. For example, social media addiction can lead to stress levels rise, loneliness, and sadness ( 54 ). Anxiety is another common mental health problem associated with social media addiction. Studies have found that young adolescents who are addicted to social media are more likely to suffer from anxiety than people who are not addicted to social media ( 55 ). In addition, social media addiction can also lead to physical health problems, such as obesity and carpal tunnel syndrome a result of spending too much time on the computer ( 22 ).

Apart from the negative impacts of social media addiction on users' mental and physical health, social media addiction can also lead to other problems. For example, social media addiction can lead to financial problems. A study by Sharif and Yeoh ( 56 ) has found that people who are addicted to social media tend to spend more money than those who are not addicted to social media. In addition, social media addiction can also lead to a decline in academic performance. Students who are addicted to social media are more likely to have lower grades than those who are not addicted to social media ( 57 ).

Research methodology

Bibliometric analysis.

Merigo et al. ( 58 ) use bibliometric analysis to examine, organize, and analyze a large body of literature from a quantitative, objective perspective in order to assess patterns of research and emerging trends in a certain field. A bibliometric methodology is used to identify the current state of the academic literature, advance research. and find objective information ( 59 ). This technique allows the researchers to examine previous scientific work, comprehend advancements in prior knowledge, and identify future study opportunities.

To achieve this objective and identify the research trends in social media addiction and problematic social media use, this study employs two bibliometric methodologies: performance analysis and science mapping. Performance analysis uses a series of bibliometric indicators (e.g., number of annual publications, document type, source type, journal impact factor, languages, subject area, h-index, and countries) and aims at evaluating groups of scientific actors on a particular topic of research. VOSviewer software ( 60 ) was used to carry out the science mapping. The software is used to visualize a particular body of literature and map the bibliographic material using the co-occurrence analysis of author, index keywords, nations, and fields of publication ( 61 , 62 ).

Data collection

After picking keywords, designing the search strings, and building up a database, the authors conducted a bibliometric literature search. Scopus was utilized to gather exploration data since it is a widely used database that contains the most comprehensive view of the world's research output and provides one of the most effective search engines. If the research was to be performed using other database such as Web Of Science or Google Scholar the authors may have obtained larger number of articles however they may not have been all particularly relevant as Scopus is known to have the most widest and most relevant scholar search engine in marketing and social science. A keyword search for “social media addiction” OR “problematic social media use” yielded 553 papers, which were downloaded from Scopus. The information was gathered in March 2022, and because the Scopus database is updated on a regular basis, the results may change in the future. Next, the authors examined the titles and abstracts to see whether they were relevant to the topics treated. There were two common grounds for document exclusion. First, while several documents emphasized the negative effects of addiction in relation to the internet and digital media, they did not focus on social networking sites specifically. Similarly, addiction and problematic consumption habits were discussed in relation to social media in several studies, although only in broad terms. This left a total of 511 documents. Articles were then limited only to journal articles, conference papers, reviews, books, and only those published in English. This process excluded 10 additional documents. Then, the relevance of the remaining articles was finally checked by reading the titles, abstracts, and keywords. Documents were excluded if social networking sites were only mentioned as a background topic or very generally. This resulted in a final selection of 501 research papers, which were then subjected to bibliometric analysis (see Figure 1 ).

Figure 1 . Preferred reporting items for systematic reviews and meta-analysis (PRISMA) flowchart showing the search procedures used in the review.

After identifying 501 Scopus files, bibliographic data related to these documents were imported into an Excel sheet where the authors' names, their affiliations, document titles, keywords, abstracts, and citation figures were analyzed. These were subsequently uploaded into VOSViewer software version 1.6.8 to begin the bibliometric review. Descriptive statistics were created to define the whole body of knowledge about social media addiction and problematic social media use. VOSViewer was used to analyze citation, co-citation, and keyword co-occurrences. According to Zupic and Cater ( 63 ), co-citation analysis measures the influence of documents, authors, and journals heavily cited and thus considered influential. Co-citation analysis has the objective of building similarities between authors, journals, and documents and is generally defined as the frequency with which two units are cited together within the reference list of a third article.

The implementation of social media addiction performance analysis was conducted according to the models recently introduced by Karjalainen et al. ( 64 ) and Pattnaik ( 65 ). Throughout the manuscript there are operational definitions of relevant terms and indicators following a standardized bibliometric approach. The cumulative academic impact (CAI) of the documents was measured by the number of times they have been cited in other scholarly works while the fine-grained academic impact (FIA) was computed according to the authors citation analysis and authors co-citation analysis within the reference lists of documents that have been specifically focused on social media addiction and problematic social media use.

Results of the study presented here include the findings on social media addiction and social media problematic use. The results are presented by the foci outlined in the study questions.

Volume, growth trajectory, and geographic distribution of the literature

After performing the Scopus-based investigation of the current literature regarding social media addiction and problematic use of social media, the authors obtained a knowledge base consisting of 501 documents comprising 455 journal articles, 27 conference papers, 15 articles reviews, 3 books and 1 conference review. The included literature was very recent. As shown in Figure 2 , publication rates started very slowly in 2013 but really took off in 2018, after which publications dramatically increased each year until a peak was reached in 2021 with 195 publications. Analyzing the literature published during the past decade reveals an exponential increase in scholarly production on social addiction and its problematic use. This might be due to the increasingly widespread introduction of social media sites in everyday life and the ubiquitous diffusion of mobile devices that have fundamentally impacted human behavior. The dip in the number of publications in 2022 is explained by the fact that by the time the review was carried out the year was not finished yet and therefore there are many articles still in press.

Figure 2 . Annual volume of social media addiction or social media problematic use ( n = 501).

The geographical distribution trends of scholarly publications on social media addiction or problematic use of social media are highlighted in Figure 3 . The articles were assigned to a certain country according to the nationality of the university with whom the first author was affiliated with. The figure shows that the most productive countries are the USA (92), the U.K. (79), and Turkey ( 63 ), which combined produced 236 articles, equal to 47% of the entire scholarly production examined in this bibliometric analysis. Turkey has slowly evolved in various ways with the growth of the internet and social media. Anglo-American scholarly publications on problematic social media consumer behavior represent the largest research output. Yet it is interesting to observe that social networking sites studies are attracting many researchers in Asian countries, particularly China. For many Chinese people, social networking sites are a valuable opportunity to involve people in political activism in addition to simply making purchases ( 66 ).

Figure 3 . Global dispersion of social networking sites in relation to social media addiction or social media problematic use.

Analysis of influential authors

This section analyses the high-impact authors in the Scopus-indexed knowledge base on social networking sites in relation to social media addiction or problematic use of social media. It provides valuable insights for establishing patterns of knowledge generation and dissemination of literature about social networking sites relating to addiction and problematic use.

Table 1 acknowledges the top 10 most highly cited authors with the highest total citations in the database.

Table 1 . Highly cited authors on social media addiction and problematic use ( n = 501).

Table 1 shows that MD Griffiths (sixty-five articles), CY Lin (twenty articles), and AH Pakpour (eighteen articles) are the most productive scholars according to the number of Scopus documents examined in the area of social media addiction and its problematic use . If the criteria are changed and authors ranked according to the overall number of citations received in order to determine high-impact authors, the same three authors turn out to be the most highly cited authors. It should be noted that these highly cited authors tend to enlist several disciplines in examining social media addiction and problematic use. Griffiths, for example, focuses on behavioral addiction stemming from not only digital media usage but also from gambling and video games. Lin, on the other hand, focuses on the negative effects that the internet and digital media can have on users' mental health, and Pakpour approaches the issue from a behavioral medicine perspective.

Intellectual structure of the literature

In this part of the paper, the authors illustrate the “intellectual structure” of the social media addiction and the problematic use of social media's literature. An author co-citation analysis (ACA) was performed which is displayed as a figure that depicts the relations between highly co-cited authors. The study of co-citation assumes that strongly co-cited authors carry some form of intellectual similarity ( 67 ). Figure 4 shows the author co-citation map. Nodes represent units of analysis (in this case scholars) and network ties represent similarity connections. Nodes are sized according to the number of co-citations received—the bigger the node, the more co-citations it has. Adjacent nodes are considered intellectually similar.

Figure 4 . Two clusters, representing the intellectual structure of the social media and its problematic use literature.

Scholars belonging to the green cluster (Mental Health and Digital Media Addiction) have extensively published on medical analysis tools and how these can be used to heal users suffering from addiction to digital media, which can range from gambling, to internet, to videogame addictions. Scholars in this school of thought focus on the negative effects on users' mental health, such as depression, anxiety, and personality disturbances. Such studies focus also on the role of screen use in the development of mental health problems and the increasing use of medical treatments to address addiction to digital media. They argue that addiction to digital media should be considered a mental health disorder and treatment options should be made available to users.

In contrast, scholars within the red cluster (Social Media Effects on Well Being and Cyberpsychology) have focused their attention on the effects of social media toward users' well-being and how social media change users' behavior, focusing particular attention on the human-machine interaction and how methods and models can help protect users' well-being. Two hundred and two authors belong to this group, the top co-cited being Andreassen (667 co-citations), Pallasen (555 co-citations), and Valkenburg (215 co-citations). These authors have extensively studied the development of addiction to social media, problem gambling, and internet addiction. They have also focused on the measurement of addiction to social media, cyberbullying, and the dark side of social media.

Most influential source title in the field of social media addiction and its problematic use

To find the preferred periodicals in the field of social media addiction and its problematic use, the authors have selected 501 articles published in 263 journals. Table 2 gives a ranked list of the top 10 journals that constitute the core publishing sources in the field of social media addiction research. In doing so, the authors analyzed the journal's impact factor, Scopus Cite Score, h-index, quartile ranking, and number of publications per year.

Table 2 . Top 10 most cited and more frequently mentioned documents in the field of social media addiction.

The journal Addictive Behaviors topped the list, with 700 citations and 22 publications (4.3%), followed by Computers in Human Behaviors , with 577 citations and 13 publications (2.5%), Journal of Behavioral Addictions , with 562 citations and 17 publications (3.3%), and International Journal of Mental Health and Addiction , with 502 citations and 26 publications (5.1%). Five of the 10 most productive journals in the field of social media addiction research are published by Elsevier (all Q1 rankings) while Springer and Frontiers Media published one journal each.

Documents citation analysis identified the most influential and most frequently mentioned documents in a certain scientific field. Andreassen has received the most citations among the 10 most significant papers on social media addiction, with 405 ( Table 2 ). The main objective of this type of studies was to identify the associations and the roles of different variables as predictors of social media addiction (e.g., ( 19 , 68 , 69 )). According to general addiction models, the excessive and problematic use of digital technologies is described as “being overly concerned about social media, driven by an uncontrollable motivation to log on to or use social media, and devoting so much time and effort to social media that it impairs other important life areas” ( 27 , 70 ). Furthermore, the purpose of several highly cited studies ( 31 , 71 ) was to analyse the connections between young adults' sleep quality and psychological discomfort, depression, self-esteem, and life satisfaction and the severity of internet and problematic social media use, since the health of younger generations and teenagers is of great interest this may help explain the popularity of such papers. Despite being the most recent publication Lin et al.'s work garnered more citations annually. The desire to quantify social media addiction in individuals can also help explain the popularity of studies which try to develop measurement scales ( 42 , 72 ). Some of the highest-ranked publications are devoted to either the presentation of case studies or testing relationships among psychological constructs ( 73 ).

Keyword co-occurrence analysis

The research question, “What are the key thematic areas in social media addiction literature?” was answered using keyword co-occurrence analysis. Keyword co-occurrence analysis is conducted to identify research themes and discover keywords. It mainly examines the relationships between co-occurrence keywords in a wide variety of literature ( 74 ). In this approach, the idea is to explore the frequency of specific keywords being mentioned together.

Utilizing VOSviewer, the authors conducted a keyword co-occurrence analysis to characterize and review the developing trends in the field of social media addiction. The top 10 most frequent keywords are presented in Table 3 . The results indicate that “social media addiction” is the most frequent keyword (178 occurrences), followed by “problematic social media use” (74 occurrences), “internet addiction” (51 occurrences), and “depression” (46 occurrences). As shown in the co-occurrence network ( Figure 5 ), the keywords can be grouped into two major clusters. “Problematic social media use” can be identified as the core theme of the green cluster. In the red cluster, keywords mainly identify a specific aspect of problematic social media use: social media addiction.

Table 3 . Frequency of occurrence of top 10 keywords.

Figure 5 . Keywords co-occurrence map. Threshold: 5 co-occurrences.

The results of the keyword co-occurrence analysis for journal articles provide valuable perspectives and tools for understanding concepts discussed in past studies of social media usage ( 75 ). More precisely, it can be noted that there has been a large body of research on social media addiction together with other types of technological addictions, such as compulsive web surfing, internet gaming disorder, video game addiction and compulsive online shopping ( 76 – 78 ). This field of research has mainly been directed toward teenagers, middle school students, and college students and university students in order to understand the relationship between social media addiction and mental health issues such as depression, disruptions in self-perceptions, impairment of social and emotional activity, anxiety, neuroticism, and stress ( 79 – 81 ).

The findings presented in this paper show that there has been an exponential increase in scholarly publications—from two publications in 2013 to 195 publications in 2021. There were 45 publications in 2022 at the time this study was conducted. It was interesting to observe that the US, the UK, and Turkey accounted for 47% of the publications in this field even though none of these countries are in the top 15 countries in terms of active social media penetration ( 82 ) although the US has the third highest number of social media users ( 83 ). Even though China and India have the highest number of social media users ( 83 ), first and second respectively, they rank fifth and tenth in terms of publications on social media addiction or problematic use of social media. In fact, the US has almost double the number of publications in this field compared to China and almost five times compared to India. Even though East Asia, Southeast Asia, and South Asia make up the top three regions in terms of worldwide social media users ( 84 ), except for China and India there have been only a limited number of publications on social media addiction or problematic use. An explanation for that could be that there is still a lack of awareness on the negative consequences of the use of social media and the impact it has on the mental well-being of users. More research in these regions should perhaps be conducted in order to understand the problematic use and addiction of social media so preventive measures can be undertaken.

From the bibliometric analysis, it was found that most of the studies examined used quantitative methods in analyzing data and therefore aimed at testing relationships between variables. In addition, many studies were empirical, aimed at testing relationships based on direct or indirect observations of social media use. Very few studies used theories and for the most part if they did they used the technology acceptance model and social comparison theories. The findings presented in this paper show that none of the studies attempted to create or test new theories in this field, perhaps due to the lack of maturity of the literature. Moreover, neither have very many qualitative studies been conducted in this field. More qualitative research in this field should perhaps be conducted as it could explore the motivations and rationales from which certain users' behavior may arise.

The authors found that almost all the publications on social media addiction or problematic use relied on samples of undergraduate students between the ages of 19–25. The average daily time spent by users worldwide on social media applications was highest for users between the ages of 40–44, at 59.85 min per day, followed by those between the ages of 35–39, at 59.28 min per day, and those between the ages of 45–49, at 59.23 per day ( 85 ). Therefore, more studies should be conducted exploring different age groups, as users between the ages of 19–25 do not represent the entire population of social media users. Conducting studies on different age groups may yield interesting and valuable insights to the field of social media addiction. For example, it would be interesting to measure the impacts of social media use among older users aged 50 years or older who spend almost the same amount of time on social media as other groups of users (56.43 min per day) ( 85 ).

A majority of the studies tested social media addiction or problematic use based on only two social media platforms: Facebook and Instagram. Although Facebook and Instagram are ranked first and fourth in terms of most popular social networks by number of monthly users, it would be interesting to study other platforms such as YouTube, which is ranked second, and WhatsApp, which is ranked third ( 86 ). Furthermore, TikTok would also be an interesting platform to study as it has grown in popularity in recent years, evident from it being the most downloaded application in 2021, with 656 million downloads ( 87 ), and is ranked second in Q1 of 2022 ( 88 ). Moreover, most of the studies focused only on one social media platform. Comparing different social media platforms would yield interesting results because each platform is different in terms of features, algorithms, as well as recommendation engines. The purpose as well as the user behavior for using each platform is also different, therefore why users are addicted to these platforms could provide a meaningful insight into social media addiction and problematic social media use.

Lastly, most studies were cross-sectional, and not longitudinal, aiming at describing results over a certain point in time and not over a long period of time. A longitudinal study could better describe the long-term effects of social media use.

This study was conducted to review the extant literature in the field of social media and analyze the global research productivity during the period ranging from 2013 to 2022. The study presents a bibliometric overview of the leading trends with particular regard to “social media addiction” and “problematic social media use.” The authors applied science mapping to lay out a knowledge base on social media addiction and its problematic use. This represents the first large-scale analysis in this area of study.

A keyword search of “social media addiction” OR “problematic social media use” yielded 553 papers, which were downloaded from Scopus. After performing the Scopus-based investigation of the current literature regarding social media addiction and problematic use, the authors ended up with a knowledge base consisting of 501 documents comprising 455 journal articles, 27 conference papers, 15 articles reviews, 3 books, and 1 conference review.

The geographical distribution trends of scholarly publications on social media addiction or problematic use indicate that the most productive countries were the USA (92), the U.K. (79), and Turkey ( 63 ), which together produced 236 articles. Griffiths (sixty-five articles), Lin (twenty articles), and Pakpour (eighteen articles) were the most productive scholars according to the number of Scopus documents examined in the area of social media addiction and its problematic use. An author co-citation analysis (ACA) was conducted which generated a layout of social media effects on well-being and cyber psychology as well as mental health and digital media addiction in the form of two research literature clusters representing the intellectual structure of social media and its problematic use.

The preferred periodicals in the field of social media addiction and its problematic use were Addictive Behaviors , with 700 citations and 22 publications, followed by Computers in Human Behavior , with 577 citations and 13 publications, and Journal of Behavioral Addictions , with 562 citations and 17 publications. Keyword co-occurrence analysis was used to investigate the key thematic areas in the social media literature, as represented by the top three keyword phrases in terms of their frequency of occurrence, namely, “social media addiction,” “problematic social media use,” and “social media addiction.”

This research has a few limitations. The authors used science mapping to improve the comprehension of the literature base in this review. First and foremost, the authors want to emphasize that science mapping should not be utilized in place of established review procedures, but rather as a supplement. As a result, this review can be considered the initial stage, followed by substantive research syntheses that examine findings from recent research. Another constraint stems from how 'social media addiction' is defined. The authors overcame this limitation by inserting the phrase “social media addiction” OR “problematic social media use” in the search string. The exclusive focus on SCOPUS-indexed papers creates a third constraint. The SCOPUS database has a larger number of papers than does Web of Science although it does not contain all the publications in a given field.

Although the total body of literature on social media addiction is larger than what is covered in this review, the use of co-citation analyses helped to mitigate this limitation. This form of bibliometric study looks at all the publications listed in the reference list of the extracted SCOPUS database documents. As a result, a far larger dataset than the one extracted from SCOPUS initially has been analyzed.

The interpretation of co-citation maps should be mentioned as a last constraint. The reason is that the procedure is not always clear, so scholars must have a thorough comprehension of the knowledge base in order to make sense of the result of the analysis ( 63 ). This issue was addressed by the authors' expertise, but it remains somewhat subjective.

Implications

The findings of this study have implications mainly for government entities and parents. The need for regulation of social media addiction is evident when considering the various risks associated with habitual social media use. Social media addiction may lead to negative consequences for adolescents' school performance, social behavior, and interpersonal relationships. In addition, social media addiction may also lead to other risks such as sexting, social media stalking, cyber-bullying, privacy breaches, and improper use of technology. Given the seriousness of these risks, it is important to have regulations in place to protect adolescents from the harms of social media addiction.

Regulation of social media platforms

One way that regulation could help protect adolescents from the harms of social media addiction is by limiting their access to certain websites or platforms. For example, governments could restrict adolescents' access to certain websites or platforms during specific hours of the day. This would help ensure that they are not spending too much time on social media and are instead focusing on their schoolwork or other important activities.

Another way that regulation could help protect adolescents from the harms of social media addiction is by requiring companies to put warning labels on their websites or apps. These labels would warn adolescents about the potential risks associated with excessive use of social media.

Finally, regulation could also require companies to provide information about how much time each day is recommended for using their website or app. This would help adolescents make informed decisions about how much time they want to spend on social media each day. These proposed regulations would help to protect children from the dangers of social media, while also ensuring that social media companies are more transparent and accountable to their users.

Parental involvement in adolescents' social media use

Parents should be involved in their children's social media use to ensure that they are using these platforms safely and responsibly. Parents can monitor their children's online activity, set time limits for social media use, and talk to their children about the risks associated with social media addiction.

Education on responsible social media use

Adolescents need to be educated about responsible social media use so that they can enjoy the benefits of these platforms while avoiding the risks associated with addiction. Education on responsible social media use could include topics such as cyber-bullying, sexting, and privacy breaches.

Research directions for future studies

A content analysis was conducted to answer the fifth research questions “What are the potential research directions for addressing social media addiction in the future?” The study reveals that there is a lack of screening instruments and diagnostic criteria to assess social media addiction. Validated DSM-V-based instruments could shed light on the factors behind social media use disorder. Diagnostic research may be useful in order to understand social media behavioral addiction and gain deeper insights into the factors responsible for psychological stress and psychiatric disorders. In addition to cross-sectional studies, researchers should also conduct longitudinal studies and experiments to assess changes in users' behavior over time ( 20 ).

Another important area to examine is the role of engagement-based ranking and recommendation algorithms in online habit formation. More research is required to ascertain how algorithms determine which content type generates higher user engagement. A clear understanding of the way social media platforms gather content from users and amplify their preferences would lead to the development of a standardized conceptualization of social media usage patterns ( 89 ). This may provide a clearer picture of the factors that lead to problematic social media use and addiction. It has been noted that “misinformation, toxicity, and violent content are inordinately prevalent” in material reshared by users and promoted by social media algorithms ( 90 ).

Additionally, an understanding of engagement-based ranking models and recommendation algorithms is essential in order to implement appropriate public policy measures. To address the specific behavioral concerns created by social media, legislatures must craft appropriate statutes. Thus, future qualitative research to assess engagement based ranking frameworks is extremely necessary in order to provide a broader perspective on social media use and tackle key regulatory gaps. Particular emphasis must be placed on consumer awareness, algorithm bias, privacy issues, ethical platform design, and extraction and monetization of personal data ( 91 ).

From a geographical perspective, the authors have identified some main gaps in the existing knowledge base that uncover the need for further research in certain regions of the world. Accordingly, the authors suggest encouraging more studies on internet and social media addiction in underrepresented regions with high social media penetration rates such as Southeast Asia and South America. In order to draw more contributions from these countries, journals with high impact factors could also make specific calls. This would contribute to educating social media users about platform usage and implement policy changes that support the development of healthy social media practices.

The authors hope that the findings gathered here will serve to fuel interest in this topic and encourage other scholars to investigate social media addiction in other contexts on newer platforms and among wide ranges of sample populations. In light of the rising numbers of people experiencing mental health problems (e.g., depression, anxiety, food disorders, and substance addiction) in recent years, it is likely that the number of papers related to social media addiction and the range of countries covered will rise even further.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.

Author contributions

AP took care of bibliometric analysis and drafting the paper. VB took care of proofreading and adding value to the paper. AS took care of the interpretation of the findings. All authors contributed to the article and approved the submitted version.

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.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: bibliometric analysis, social media, social media addiction, problematic social media use, research trends

Citation: Pellegrino A, Stasi A and Bhatiasevi V (2022) Research trends in social media addiction and problematic social media use: A bibliometric analysis. Front. Psychiatry 13:1017506. doi: 10.3389/fpsyt.2022.1017506

Received: 12 August 2022; Accepted: 24 October 2022; Published: 10 November 2022.

Reviewed by:

Copyright © 2022 Pellegrino, Stasi and Bhatiasevi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Alfonso Pellegrino, alfonso.pellegrino@sasin.edu ; Veera Bhatiasevi, veera.bhatiasevi@mahidol.ac.th

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Stanford University School of Medicine blog

Addictive potential of social media, explained

The curious title of Stanford psychiatrist Anna Lembke 's book, Dopamine Nation: Finding Balance in the Age of Indulgence , pays tribute to the crucial and often destructive role that dopamine plays in modern society.

Dopamine , the main chemical involved in addiction, is secreted from certain nerve tracts in the brain when we engage in a rewarding experience such as finding food, clothing, shelter or a sexual mate. Nature designed our brains to feel pleasure when these experiences happen because they increase our odds of survival and of procreation.

But the days when our species dwelled in caves and struggled for survival are long gone. Dopamine Nation explains how living in a modern society, affluent beyond comparison by evolutionary standards, has rendered us all vulnerable to dopamine-mediated addiction . Today, the addictive substance of choice, whether we realize it or not, is often the internet and social media channels, according to Lembke, MD.

"If you're not addicted yet, it's coming soon to a website near you," Lembke joked when I talked to her about the message of Dopamine Nation , which was published in August. This Q&A is abridged from that exchange.

Why did you decide to write this book?

I wanted to tell readers what I'd learned from patients and from neuroscience about how to tackle compulsive overconsumption. Feel-good substances and behaviors increase dopamine release in the brain's reward pathways .

The brain responds to this increase by decreasing dopamine transmission -- not just back down to its natural baseline rate, but below that baseline. Repeated exposure to the same or similar stimuli ultimately creates a chronic dopamine-deficit state, wherein we're less able to experience pleasure.

What are the risk factors for addiction?

Easy access and speedy reward are two of them. Just as the hypodermic needle is the delivery mechanism for drugs like heroin, the smartphone is the modern-day hypodermic needle, delivering digital dopamine for a wired generation.

The hypodermic needle delivers a drug right into our vascular system, which in turn delivers it right to the brain, making the drug more potent. The same is true for the smartphone; with its bright colors, flashing lights and engaging alerts, it delivers images to our visual cortex that are tough to resist. And the quantity is endless. TikTok never runs out.

What makes social media particularly addictive?

We're wired to connect. It's kept us alive for millions of years in a world of scarcity and ever-present danger. Moving in tribes safeguards against predators, optimizes scarce resources and facilitates pair bonding. Our brains release dopamine when we make human connections, which incentivizes us to do it again.

But social connection has become druggified by social-media apps, making us vulnerable to compulsive overconsumption. These apps can cause the release of large amounts of dopamine into our brains' reward pathway all at once, just like heroin, or meth, or alcohol. They do that by amplifying the feel-good properties that attract humans to each other in the first place.

Then there's novelty. Dopamine is triggered by our brain's search-and-explore functions, telling us, "Hey, pay attention to this, something new has come along." Add to that the artificial intelligence algorithms that learn what we've liked before and suggest new things that are similar but not exactly the same, and we're off and running.

Further, our brains aren't equipped to process the millions of comparisons the virtual world demands. We can become overwhelmed by our inability to measure up to these "perfect" people who exist only in the Matrix . We give up trying and sink into depression, or what neuroscientists called "learned helplessness."

Upon signing off, the brain is plunged into a dopamine-deficit state as it attempts to adapt to the unnaturally high levels of dopamine social media just released. Which is why social media often feels good while we're doing it but horrible as soon as we stop.

Is there an antidote to our addiction to social media?

Yes, a timeout -- at least for a day. But a whole month is more typically the minimum amount of time we need away from our drug of choice, whether it's heroin or Instagram, to reset our dopamine reward pathways. A monthlong dopamine fast will decrease the anxiety and depression that social media can induce, and enhance our ability to enjoy other, more modest rewards again.

If and when we return to social media, we can consolidate our use to certain times of the day, avoid certain apps that suck us into the vortex and prioritize apps that connect us with real people in our real lives.

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Social Media and Teen Mental Health: A Complex Mix

There is strong evidence to suggest that teenagers in the United States are collectively in the midst of a mental health crisis, as rates of both depression and suicide have climbed in recent years. Could the popularity of social media among young people be to blame?

Melissa DuPont-Reyes, PhD, MPH is an Assistant Professor of Sociomedical Sciences and Epidemiology

Melissa DuPont-Reyes , assistant professor of sociomedical sciences and epidemiology, says the answer may not be as simple as you think. She is leading a new study that takes a holistic perspective, broadening the focus from how the use of TikTok, Instagram, and other social media platforms can harm mental health to include an understanding of how they can be protective, too.

The National Institutes of Mental Health -funded longitudinal study is focused on Latinx adolescents, who use social media more than all other racial/ethnic or age groups, nationally. Beyond a simple measure of the frequency of social media use, Dupont-Reyes and colleagues will drill down into the diverse content young people encounter, including Spanish-language, Latinx-tailored, and English-language posts on a variety of platforms.

The study will collect data on both protective aspects like anti-stigma awareness campaigns and symptom support, as well as negative effects such as stigmatizing content, hate speech, and cyber-bullying. Researchers will examine how these exposures drive youths’ self-perception, help-seeking, and mental health outcomes, as well as the mediating role played by peers and family members.

To accomplish her study objective, in part, Dupont-Reyes will utilize validated, culturally appropriate survey assessments she developed as part of a project funded through a Robert Wood Johnson Foundation Pioneering Ideas Award. As part of the new study, young people will have the chance to research the question and have a say in how to address it through a process called Youth Participatory Action Research.

When it comes to social media’s effects on an adolescent mental health, Dupont-Reyes hypothesizes that context matters quite a lot. Her preliminary work has shown that for some youth, social media can be a lifeline. For instance, youth who are unaccompanied minors migrating, are LGBTQI+ in nontolerant settings, have a disability such as a speech impediment or even mental illness, or have experienced police brutality, all report that social media can be empowering as a tool to make their voices heard while also lending support and resources.

“I hope that my project demonstrates a more diverse portrait of adolescents in the U.S., and globally, as well as the social media that they encounter, and specifies the contexts in which social media can be beneficial to mental health and the contexts in which it might be harmful,” she says.

DuPont-Reyes says the evidence generated from the project could inform policies that are more equitable, accountable, and transparent—ultimately to create a safer technological landscape for diverse populations to promote mental health on a population level. At the same time, its findings can reach parents, teachers, the tech industry, health care providers, and others with its message that vilifying social media is not the answer.

“I hope my research can inform a more holistic and equitable approach to creating a safer social media environment for youth that doesn’t solely require restricting technology,” she says.

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Social media use and academic achievement

Article by Jessica Henderson Photos by Kathy F. Atkinson March 12, 2024

UD Associate Professor Mellissa Gordon finds that frequent social media use is associated with decreased academic achievement among early adolescents

While most people think about older teens and young adults as social media’s primary users, children as young as 11 are increasingly using these platforms on a daily basis. And as recent research from University of Delaware’s Mellissa S. Gordon shows, their social media use affects their school grades.

In an article published in Youth and Society , Gordon and co-author Christine McCauley Ohannessian analyzed survey data from 1,459 middle schoolers in the northeast United States and found that their academic achievement decreased as their Facebook, Instagram, Snapchat and Twitter (now known as X) use increased. They also found that parental communication made a big difference, playing a part in whether school grades increased or decreased. 

“The landscape of social media is ever changing, and in spite of best efforts, it presents a challenge to keep pace with its effect,” said Gordon, an associate professor specializing in adolescent development in UD’s College of Education and Human Development . “Unfortunately, its impact on our most vulnerable population — children and adolescents — is not fully understood.”

Unlike most other studies, Gordon and Ohannessian’s study focused exclusively on early adolescents aged 11 to 15 years — the fastest growing population of social media users — and assessed four social media platforms, not only Facebook and Twitter. Because most research focuses on older adolescents or young adults, this study offers an important first step in understanding the links between social media use and academic achievement among early adolescents.

Social media use and academic achievement 

Gordon and Ohannessian analyzed middle schoolers’ self-reported data on their school grades and social media use from surveys given in the fall of 2016 and the spring of 2017. Even after controlling for age, gender and race and ethnicity, they found that participants’ grades decreased as the frequency of their social media use increased across all four platforms. 

“There are many explanations for this finding, which aligns with research on older population groups as well,” Gordon said. “For example, social media likely poses a distraction to early adolescents. Attention that they would typically invest in their schoolwork is diverted to social media use, which ultimately affects their ability to perform well in school. But lower academic achievement may also result from other aspects of development that are affected by social media use. For example, social media use can disrupt healthy family functioning or peer relationships, which can then lower early adolescents’ performance in school.”

The role of parental communication 

Gordon and Ohannessian were also interested in how parental communication might impact the relationship between middle schoolers’ academic achievement and social media use. To investigate this question, they asked their study participants to define the quality of their communication with their mothers. For example, participants were asked to agree or disagree with a series of 20 statements, such as “My mother is always a good listener.” 

Gordon and Ohannessian found that less frequent use of Facebook and Instagram, coupled with high-quality mother-adolescent communication, was associated with higher academic achievement.

“We think that mothers who were maintaining positive, frequent communication with their children might have also been monitoring their adolescents’ use of Facebook and Instagram, perhaps by setting daily limits,” Gordon said.

In contrast, low-quality mother-adolescent communication and increased use of Facebook and Instagram was associated with lower academic achievement.

Gordon and Ohannessian suggest that frequent social media use may allow an adolescent to establish more autonomy from their parents, which is a developmentally appropriate behavior for this age group. However, in doing so, communication with their mothers and the monitoring of social media may decrease.

Opportunities for parents 

An important takeaway from Gordon and Ohannessian’s study is that parental involvement and communication can impact the relationship between frequent social media use and decreased academic achievement. Starting with small changes in a family’s daily practice — especially for parents who feel overwhelmed — can be very helpful. 

“Setting parameters on children’s social media use, allowing access to certain platforms relative to others and monitoring the content that children engage with could help them maintain or even increase their academic performance,” Gordon said. 

Andrea Glowatz, director of CEHD’s The College School and a mother to a 13-year-old middle schooler, offered other ideas, while acknowledging the challenges of parenting.

"Parenting is intellectually and emotionally draining, and while our logic and reasoning may tell us that our tweens and teens neither need nor benefit from social media, the forces of society often cause us to succumb to them,” Glowatz said. “Our tweens and teens are still vulnerable to predators, dopamine imbalances, distraction, addiction and other consequences that interfere with academic success. But teaching them about online safety and carefully monitoring their online use can offset some of these effects.” 

The College School serves bright children with learning differences in grades 1 through 8. There, fifth graders have participated in a pilot cybersecurity program pioneered by UD researchers, and as part of their STEAM coursework with Master Teacher Laurie Drumm, children have lessons in online safety. In the coming months, Glowatz also plans to develop new programs on information literacy teaching children how to evaluate online news and other forms of media. 

To learn more about CEHD research in adolescent development, visit our research pages . 

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How teens and parents approach screen time, most teens at least sometimes feel happy and peaceful when they don’t have their phone, but 44% say this makes them anxious. half of parents say they have looked through their teen’s phone.

Pew Research Center conducted this study to better understand teens’ and parents’ experiences with screen time. 

The Center conducted an online survey of 1,453 U.S. teens and parents from Sept. 26 to Oct. 23, 2023, through Ipsos. Ipsos invited one parent from each of a representative set of households with parents of teens in the desired age range from its KnowledgePanel . The KnowledgePanel is a probability-based web panel recruited primarily through national, random sampling of residential addresses. Parents were asked to think about one teen in their household (if there were multiple teens ages 13 to 17 in the household, one was randomly chosen). At the conclusion of the parent’s section, the parent was asked to have this chosen teen come to the computer and complete the survey in private.

The survey is weighted to be representative of two different populations: 1) parents with teens ages 13 to 17 and 2) teens ages 13 to 17 who live with parents. For each of these populations, they survey is weighted to be representative by age, gender, race and ethnicity, household income and other categories.

This research was reviewed and approved by an external institutional review board (IRB), Advarra, an independent committee of experts specializing in helping to protect the rights of research participants.

Here are the questions among parents and among teens used for this report, along with responses, and its methodology ­­­.

Today’s teenagers are more digitally connected than ever. Most have access to smartphones and use social media , and nearly half say they are online almost constantly. But how are young people navigating this “always on” environment?  

To better understand their experiences, we surveyed both teens and parents on a range of screen time-related topics. Our questions explored the emotions teens tie to their devices, the impact of smartphones on youth, and the challenges parents face when raising children in the digital age.

Key findings from the survey:

• Phone-less: 72% of U.S. teens say they often or sometimes feel peaceful when they don’t have their smartphone; 44% say it makes them feel anxious.
• Good for hobbies, less so for socialization: 69% of teens say smartphones make it easier for youth to pursue hobbies and interests; fewer (30%) say it helps people their age learn good social skills.
• Parental snooping: Half of parents say they have looked through their teen’s phone.
• Smartphone standoffs: About four-in-ten parents and teens report regularly arguing with one another about time spent on their phone.
• Distracted parenting: Nearly half of teens (46%) say their parent is at least sometimes distracted by their phone when they’re trying to talk to them.

This Pew Research Center survey of 1,453 U.S. teens ages 13 to 17 and their parents was conducted Sept. 26-Oct. 23, 2023. 1

Jump to read about views among teens on: Screen time | Feelings when disconnected from phones | Thoughts on smartphones’ impact

Jump to read about views among parents on: Parenting in the smartphone age | Their own screen time struggles

Teens’ views on screen time and efforts to cut back

Fully 95% of teens have access to a smartphone, and about six-in-ten say they use TikTok, Snapchat or Instagram . But do teens think they spend too much time in front of screens?

More teens say they spend too much time on their phone or social media than say they don’t spend enough time on them. We found that 38% of teens say they spend too much time on their smartphone. About a quarter say the same regarding their social media use. 2

But the largest shares say the amount of time they spend on their phone (51%) or on social media (64%) is about right. Relatively few teens say they don’t spend enough time with these technologies.

Views on this differ by gender. Teen girls are more likely than boys to say they spend too much time on their smartphone (44% vs. 33%) or social media (32% vs. 22%).

Teens’ efforts to curb their screen time

A minority of teens have taken steps to reduce their screen time. Roughly four-in-ten teens (39%) say they have cut back on their time on social media. A similar share says the same about their phone (36%).

Still, most teens have not limited their smartphone (63%) or social media (60%) use.

How teens’ behaviors vary by gender

About four-in-ten or more girls say they have cut back on their smartphone or social media use. For boys, those figures drop to roughly one-third.

How teens’ behaviors vary based on their screen time

Teens who report spending too much time on social media and smartphones are especially likely to report cutting back on each. For instance, roughly six-in-ten teens who say they are on social media too much say they have cut back (57%). This is far higher than the 32% among those who say they are on social media too little or the right amount.

How teens feel when they don’t have their phone

Teens encounter a range of emotions when they don’t have their phones, but we asked them about five specific ones. Roughly three-quarters of teens say it often or sometimes makes them feel happy (74%) or peaceful (72%) when they don’t have their smartphone.

Smaller but notable shares of teens equate not having their phone with more negative emotions. Teens say not having their phone at least sometimes makes them feel anxious (44%), upset (40%) and lonely (39%).

It is worth noting that only a minority of teens – ranging from 7% to 32% – say they often feel these emotions when they’re phone-less.

Teens’ feelings on this differ by some demographic factors:

• Age and gender: Older girls ages 15 to 17 (55%) are more likely than younger girls (41%) and teen boys who are younger (41%) and older (40%) to say they feel anxious at least sometimes when they don’t have their smartphone.
• Gender: 45% of teen girls say not having their phone makes them feel lonely regularly, compared with 34% of teen boys.

Do teens think smartphones are negatively impacting young people?

As smartphones have become a universal part of teen life, many have asked what impact, if any, phones are having on today’s youth.

Teens shared their perspectives on smartphones’ impact on people their age and whether these devices have made certain aspects of growing up more or less challenging.

Most teens think the benefits of smartphones outweigh the harms for people their age. Seven-in-ten teens say smartphones provide more benefits than harms for people their age, while a smaller share (30%) take the opposing view, saying there are more harms than benefits.

Teens’ views, by gender and age

Younger girls ages 13 and 14 (39%) are more likely than older teen girls (29%) and teen boys who are younger (29%) and older (25%) to say that the harms of people their age using smartphones outweigh the benefits.

The survey also shows that teens see these devices’ impacts on specific aspects of life differently.

More teens believe smartphones make it easier, rather than harder, to be creative, pursue hobbies and do well in school. Majorities of teens say smartphones make it a little or a lot easier for people their age to pursue hobbies and interests (69%) and be creative (65%). Close to half (45%) say these devices have made it easier for youth to do well in school.

Views are more mixed when it comes to developing healthy friendships. Roughly four-in-ten teens say smartphones make it easier for teens to develop healthy friendships, while 31% each say they make it harder or neither easier nor harder.

But they think smartphones have a more negative than positive impact on teens’ social skills. A larger percentage of teens say smartphones make learning good social skills harder (42%) rather than easier (30%). About three-in-ten say it neither helps nor hurts.

How parents navigate raising teens in the smartphone age

With the rise of smartphones, today’s parents must tackle many questions that previous generations did not. How closely should you monitor their phone use? How much screen time is too much? And how often do phones lead to disagreements?

We developed a set of parallel questions to understand the perspectives of both parents and teens. Here’s what we found:

It’s common for parents to look through their teen’s phone – and many of their teens know it. Half of parents of teens say they look through their teen’s phone. When we asked teens if they thought their parents ever look through their phones, 43% believed this had happened.

Whether parents report looking through their child’s smartphone depends on their kid’s age. While 64% of parents of 13- to 14-year-olds say they look through their teen’s smartphone, this share drops to 41% among parents of 15- to 17-year-olds.

Teens’ accounts of this also vary depending on their age: 56% of 13- to 14-year-olds say their parent checks their smartphone, compared with 35% of teens ages 15 to 17.

How often do parents and teens argue about phone time?

Parents and teens are equally likely to say they argue about phone use. Roughly four-in-ten parents and teens (38% each) say they at least sometimes argue with each other about how much time their teen spends on the phone. This includes 10% in each group who say this happens often .

Still, others say they never have these types of disagreements. One-quarter of parents say they never argue with their teen about this, while 31% of teens say the same.

Teens’ and parents’ views, by race and ethnicity

Hispanic Americans stand out for reporting having these disagreements often. While 16% of Hispanic teens say they often argue with their parent about how much time they’re spending on their phone, that share drops to 9% for White teens and 6% for Black teens. 3

A similar pattern is present among parents. Hispanic parents (19%) are more likely than White (6%) or Black (7%) parents to say they often argue with their teen about this.

Teens’ views, by frequency of internet use

The amount of time teens report being online is also a factor. About half (47%) of teens who report being online almost constantly say they at least sometimes argue with their parent about the amount of time they spend on their phone, compared with those who are online less often (30%). 

How much do parents prioritize tracking their teen’s phone use?

Most parents prioritize managing the amount of time their teen spends on the phone. Roughly three-quarters of parents (76%) say managing how much time their teen spends on the phone is an important or a top priority.  Still, 19% of parents don’t consider this a priority.

Parents’ views, by race and ethnicity

Majorities of parents across racial and ethnic groups think of this as a priority. But some groups stand out for how much they prioritize this. For example, Hispanic (25%) or Black (24%) parents are more likely to say managing how much time their teen is on the phone is a top priority. That share drops to 10% among White parents.

Parents’ views, by household income

We also see differences between the lowest and highest income households: 22% of parents whose annual household income is less than $30,000 consider managing the amount of time their teen is on the phone a top priority, compared with 14% of those whose household income is $75,000 or more a year. Those whose household income is $30,000 to $74,999 a year do not meaningfully differ from either group.

Do parents set time limits on their teen’s phone use?

There’s a nearly even split between parents who restrict their teen’s time on their phone and those who don’t. About half of parents (47%) say they limit the amount of time their teen can be on their phone, while a similar share (48%) don’t do this.

Parents’ views, by teen’s age

Parents of younger teens are far more likely to regulate their child’s screen time. While 62% of parents of 13- to 14-year-olds say they limit how much time their teen can be on their phone, that share drops to 37% among those with a 15- to 17-year-old.

How difficult is it for parents to keep up with their teen’s phone use?

Managing screen time can feel like an uphill battle for some parents. About four-in-ten say it’s hard to manage how much time their teen spends on their phone. A smaller share (26%) says this is easy to do. 

Another 26% of parents fall in the middle – saying it’s neither easy nor hard.

Higher-income parents are more likely to find it difficult to manage their teen’s phone time. Roughly half (47%) of parents living in households earning $75,000 or more a year say managing the amount of time their teen is on their phone is hard. These shares are smaller among parents whose annual household income falls below $30,000 (38%) or is between $30,000 and $74,999 (32%).

Parents’ own struggles with device distractions, screen time

Teens aren’t the only ones who can be glued to their phones. Parents, too, can find themselves in an endless cycle of checking emails , text messages and social media.

With that in mind, we asked parents to think about their own screen time – both the time they spend on their phone, and if it ever gets in the way of connecting with their teen.

Do parents think they spend too much time on their phone?

Like teens, parents are far more likely to say they spend too much rather than not enough time on their phone. About half of parents (47%) say they spend too much time on their smartphone. Just 5% think they spend too little time on it. And 45% believe they spend the right amount of time on their phone.

Parents’ views differ by:

• Household Income: 50% of parents with annual household incomes of $75,000 or more say they spend too much time on their phone. This share drops to 41% among those living in households earning $30,000 to $74,999 a year and 38% among those earning under $30,000.
• Race and ethnicity: 57% of White parents believe they spend too much time on their phone, compared with 38% of Black parents and 34% of Hispanic parents.

How often are parents distracted by their phone when talking with their teen?

When it comes to distracted parenting, parents paint a rosier picture than teens. Nearly half of teens (46%) say their parent is at least sometimes distracted by their phone when they’re trying to talk to them, including 8% who say this happens often.

But when parents were asked to assess their own behavior, fewer – 31% – say this happens regularly.

• Throughout this report, “teens” refers to those ages 13 to 17 and “parents” refers to those with a child ages 13 to 17. ↩
• A  2018 Center survey  also asked U.S teens some of the same questions about experiences and views related to smartphone and social media. Direct comparisons cannot be made across the two surveys due to mode, sampling and recruitment differences. Please read the Methodology section  to learn more about how the current survey was conducted. ↩
• There were not enough Asian respondents in the sample to be broken out into a separate analysis. As always, their responses are incorporated into the general population figures throughout the report. ↩

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Social Networking Sites and Addiction: Ten Lessons Learned

Online social networking sites (SNSs) have gained increasing popularity in the last decade, with individuals engaging in SNSs to connect with others who share similar interests. The perceived need to be online may result in compulsive use of SNSs, which in extreme cases may result in symptoms and consequences traditionally associated with substance-related addictions. In order to present new insights into online social networking and addiction, in this paper, 10 lessons learned concerning online social networking sites and addiction based on the insights derived from recent empirical research will be presented. These are: (i) social networking and social media use are not the same; (ii) social networking is eclectic; (iii) social networking is a way of being; (iv) individuals can become addicted to using social networking sites; (v) Facebook addiction is only one example of SNS addiction; (vi) fear of missing out (FOMO) may be part of SNS addiction; (vii) smartphone addiction may be part of SNS addiction; (viii) nomophobia may be part of SNS addiction; (ix) there are sociodemographic differences in SNS addiction; and (x) there are methodological problems with research to date. These are discussed in turn. Recommendations for research and clinical applications are provided.

1. Introduction

The history of social networking sites (SNSs) dates back to 1997, when the first SNS SixDegrees emerged as a result of the idea that individuals are linked via six degrees of separation [ 1 ], and is conceived as “the small world problem” in which society is viewed as becoming increasingly inter-connected [ 2 ]. In 2004, Facebook , was launched as an online community for students at Harvard University and has since become the world’s most popular SNS [ 3 ]. In 2016, there were 2.34 billion social network users worldwide [ 4 ]. In the same year, 22.9% of the world population used Facebook [ 5 ]. In 2015, the average social media user spent 1.7 h per day on social media in the USA and 1.5 h in the UK, with social media users in the Philippines having the highest daily use at 3.7 h [ 6 ]. This suggests social media use has become an important leisure activity for many, allowing individuals to connect with one another online irrespective of time and space limitations.

It is this kind of connecting or the self-perceived constant need to connect that has been viewed critically by media scholars. Following decades of researching technology-mediated and online behaviors, Turkle [ 7 ] claims overreliance on technology has led to an impoverishment of social skills, leaving individuals unable to engage in meaningful conversations because such skills are being sacrificed for constant connection, resulting in short-term attention and a decreased ability to retain information. Individuals have come to be described as “alone together”: always connected via technology, but in fact isolated [ 8 ]. The perceived need to be online may lead to compulsive use of SNSs, which in extreme cases may result in symptoms and consequences traditionally associated with substance-related addictions. Since the publication of the first ever literature review of the empirical studies concerning SNS addiction in 2011 [ 3 ], the research field has moved forward at an increasingly rapid pace. This hints at the scientific community’s increasing interest in problematic and potentially addictive social networking use. In order to present new insights into online social networking and addiction, in this paper, 10 lessons learned concerning online social networking sites and addiction based on the insights derived from recent empirical research will be presented. These are: (i) social networking and social media use are not the same; (ii) social networking is eclectic; (iii) social networking is a way of being; (iv) individuals can become addicted to using social networking sites; (v) Facebook addiction is only one example of SNS addiction; (vi) fear of missing out (FOMO) may be part of SNS addiction; (vii) smartphone addiction may be part of SNS addiction; (viii) nomophobia may be part of SNS addiction; (ix) there are sociodemographic differences in SNS addiction; and (x) there are methodological problems with research to date. These are discussed in turn.

2. 10 Lessons Learned from Recent Empirical Literature

2.1. social networking and social media use are not the same.

Social networking and social media use have often been used interchangeably in the scientific literature. However, they are not the same. Social media refers to the web 2.0 capabilities of producing, sharing, and collaborating on content online (i.e., user-generated content, implying a social element). Accordingly, social media use includes a wide range of social applications, such as collaborative projects, weblogs, content communities, social networking sites, virtual game worlds, and virtual social worlds [ 9 ], each of which will be addressed below.

Collaborative projects can be shared and worked on jointly and simultaneously using cloud-based computing. Two different types can be distinguished: Wikis allow for creating, removing and modifying online content (e.g., Wikipedia ). Social bookmarking applications, on the other hand, allow for numbers of people to accumulate and appraise websites (e.g., Delicious ). Taken together, collaborative projects may produce a superior end result in comparison to individual projects [ 9 ], which can be linked to the concept of collective intelligence, whereby the intelligence in the group is greater than the sum of its parts [ 10 ].

Weblogs (or “blogs”) can also be considered social media. Blogs allow individuals to share personal online diaries and information (sometimes in the form of images and videos), which may or may not be commented upon by other internet users. Next, there are content communities and video-sharing sites (e.g., YouTube ). Content may include videos, but also text (e.g., BookCrossing ), photographs (e.g., Instagram ), and PowerPoint presentations (e.g., Slideshare ), and in most cases, there is no a need for individuals to have personal profiles, and if they do, these tend to include limited personal information. Virtual game worlds allow users to create an online alter ego in the form of an avatar and to play with other players in large gaming universes (and the next section covers gaming in more detail). Kaplan and Haenlein [ 9 ] differentiate these from virtual social worlds from virtual game worlds, whereby the former allow individuals to create online characters which live in an alternative virtual world that is similar to their real life environments on the one hand, but defies physical laws. Arguably the best example of these virtual social worlds is Second Life , populated by human-like avatars, who engage in activities users engage in on an everyday basis, such as furnishing houses, going shopping, and meeting friends.

Finally, there are social networking sites, which we have previously defined as “virtual communities where users can create individual public profiles, interact with real-life friends, and meet other people based on shared interests” ([ 3 ]; p. 3529). Social networking is particularly focused on connecting people, which does not apply to a number of the other social media applications outlined above. Engaging in social networking comprises a specific type of social media use, therefore they are not synonymous. Consequently, studies that have examined social media addiction and social networking addiction may also be using the terms interchangeably, suggesting nosological imprecision.

2.2. Social Networking Is Eclectic

Despite social networking being one type of social media use (as outlined in the previous section), the behavior is inherently eclectic because it includes a variety of apps and services that can be engaged in. For instance, social networking can be the use of traditional social networking sites, such as Facebook. Facebook can be considered an ‘egocentric’ SNS (rather than the previously more common virtual communities that focused on shared interests between members) because it allows individuals to represent themselves using individual profiles and wall posts. These can contain text and audiovisual content, whilst connecting to friends who often appear as real life friends and acquaintances given the main motivation of individuals to use SNSs such as Facebook is to maintain their connections [ 3 ].

In 2016, the most popular social networking site was Facebook with 1712 million active users [ 5 ]. Facebook has long established its supremacy in terms of active members, with membership numbers steadily increasing by 17%–20% annually [ 11 ]. Facebook is a very active network. Every minute, 510,000 comments are posted; 293,000 statuses are updated; and 136,000 photos are uploaded, whilst the average user spends approximately 20 min daily on the site [ 11 ].

Over the past few years, new networks have emerged that have gradually risen in popularity, particularly amongst younger generations. Instagram was launched in 2010 as a picture sharing SNS, claiming to “allow you to experience moments in your friends’ lives through pictures as they happen” [ 12 ]. In 2016, Instagram had 500 m active users [ 5 ]. Snapchat was launched in 2011 [ 13 ] as an SNS that allows users to message and connect with others using a smartphone and to send texts, videos, and make calls. Snapchat is different from other networks in that it has an inherently ephemeral nature, whereby any messages are automatically deleted shortly after the receiver has viewed them, allowing an increased experience of perceived privacy and safety online [ 14 ]. However, teenagers are especially aware of the transitory nature of Snapchat messages and therefore take screenshots and keep them stored on their mobile phones or in the cloud, simply to have proof of conversations and visuals spread on this medium. The privacy advantage of the medium is thereby countered. Snapchat had 200 million users in 2016 [ 5 ]. In the same year, Snapchat was the most popular SNS among 13–24 year-old adolescents and adults in the USA, with 72% of this group using them, followed by 68% Facebook users, and 66% Instagram users [ 15 ]. The popularity of Snapchat —particularly among young users—suggests the SNS landscape is changing in this particular demographic, with users being more aware of potential privacy risks, enjoying the lack of social pressure on Snapchat as well as the increased amount of control over who is viewing their ephemeral messages. However, it could also be the case that this may lead to the complete opposite by increasing the pressure to be online all the time because individuals risk missing the connecting thread in a continuing stream of messages within an online group. This may be especially the case in Snapchat groups/rooms created for adolescents in school or other contexts. This can lead to decreasing concentration during preparation tasks for school at home, and may lead to constant distraction because of the pressure to follow what is going on as well as the fear of missing out. From a business point of view, Snapchat has been particularly successful due to its novel impermanent approach to messaging, with Facebook founder Mark Zuckerberg offering $3 billion to buy the SNS, which has been declined by Evan Spiegel, Snapchat’s CEO and co-founder [ 13 ]. These facts suggest the world of traditional SNS is changing.

Social networking can be instant messaging. The most popular messaging services to date are WhatsApp and Facebook Messenger with 1000 million active users each [ 5 ]. WhatsApp is a mobile messaging site that allows users to connect to one another via messages and calls using their internet connection and mobile data (rather than minutes and texts on their phones), and was bought by Facebook in 2014 for $22 billion [ 16 ], leading to controversies about Facebook’s data sharing practices (i.e., Whatsapp phone numbers being linked with Facebook profiles), resulting in the European Commission fining Facebook [ 17 ]. In addition to WhatsApp , Facebook owns their own messaging system, which is arguably the best example of the convergence between traditional SNS use and messaging, and which functions as an app on smartphones separate from the actual Facebook application.

Social networking can be microblogging. Microblogging is a form of more traditional blogging, which could be considered a personal online diary. Alternatively, microblogging can also be viewed as an amalgamation of blogging and messaging, in such a way that messages are short and intended to be shared with the writer’s audience (typically consisting of ‘followers’ rather than ‘friends’ found on Facebook and similar SNSs). A popular example of a microblogging site is Twitter , which allows 140 characters per Tweet only. In 2016, Twitter had 313 million active users [ 5 ], making it the most successful microblogging site to date. Twitter has become particularly used as political tool with examples including its important role in the Arab Spring anti-government protests [ 18 ], as well as extensive use by American President Donald Trump during and following his presidential campaign [ 19 ]. In addition to microblogging politics, research has also assessed the microblogging of health issues [ 20 ].

Social networking can be gaming. Gaming can arguably be considered an element of social networking if the gaming involves connecting with people (i.e., via playing together and communicating using game-inherent channels). It has been argued that large-scale internet-enabled games (i.e., Massively Multiplayer Role-Playing Games [MMORPGs]), such as the popular World of Warcraft , are inherently social games situated in enormous virtual worlds populated by thousands of gamers [ 21 , 22 ], providing gamers various channels of communication and interaction, and allowing for the building of relationships which may extend beyond the game worlds [ 23 ]. By their very nature, games such as MMORPGs are “particularly good at simultaneously tapping into what is typically formulated as game/not game, social/instrumental, real/virtual. And this mix is exactly what is evocative and hooks many people. The innovations they produce there are a result of MMOGs as vibrant sites of culture” [ 24 ]. Not only do these games offer the possibility of communication, but they provide a basis for strong bonds between individuals when they unite through shared activities and goals, and have been shown to facilitate and increase intimacy and relationship quality in couples [ 25 ] and online gamers [ 22 , 23 ]. In addition to inherently social MMORPGs, Facebook -enabled games—such as Farmville or Texas Hold “Em Poker ”—can be subsumed under the social networking umbrella if they are being used in order to connect with others (rather than for solitary gaming purposes) [ 26 , 27 ].

Social networking can be online dating. Presently, there are many online dating websites available, which offer their members the opportunity to become part of virtual communities, and they have been especially designed to meet the members’ romantic and relationship-related needs and desires [ 28 ]. On these sites, individuals are encouraged to create individual public profiles, to interact and communicate with other members with the shared interest of finding a ‘date’ and/or long-term relationships, therewith meeting the present authors’ definition of SNS. In that way, online dating sites can be considered social networking sites. However, these profiles are often semi-public, with access granted only to other members of these networks and/or subscribers to the said online dating services. According to the US think tank Pew Research Center’s Internet Project [ 29 ], 38% of singles in the USA have made use of online dating sites or mobile dating applications. Moreover, nearly 60% of internet users think that online dating is a good way to meet people, and the percentage of individuals who have met their romantic partners online has seen a two-fold increase over the last years [ 29 ]. These data suggest online dating is becoming increasingly popular, contributing to the appeal of online social networking sites for many users across the generations. However, it can also be argued that online dating sites such as Tinder may be less a medium for ‘long-term relationships’, given that Tinder use can lead to sexual engagement. This suggests the uses and gratifications perspective underlying Tinder use points more in the direction of other motives, such as physical and sexual aspirations and needs, rather than purely romance.

Taken together, this section has argued that social networking activities can comprise a wide variety of usage motivations and needs, ranging from friendly connection over gaming to romantic endeavors, further strengthening SNS’ natural embeddedness in many aspects of the everyday life of users. From a social networking addiction perspective, this may be similar to the literature on Internet addiction which often delineates between addictions to specific applications on the Internet (e.g., gaming, gambling, shopping, sex) and more generalized Internet addiction (e.g., concerning problematic over-use of the Internet comprising many different applications) [ 30 , 31 ].

2.3. Social Networking Is a Way of Being

In the present day and age, individuals have come to live increasingly mediated lives. Nowadays, social networking does not necessarily refer to what we do, but who we are and how we relate to one another. Social networking can arguably be considered a way of being and relating, and this is supported by empirical research. A younger generation of scholars has grown up in a world that has been reliant on technology as integral part of their lives, making it impossible to imagine life without being connected. This has been referred to as an ‘always on’ lifestyle: “It’s no longer about on or off really. It’s about living in a world where being networked to people and information wherever and whenever you need it is just assumed” [ 32 ]. This has two important implications. First, being ‘on’ has become the status quo. Second, there appears to be an inherent understanding or requirement in today’s technology-loving culture that one needs to engage in online social networking in order not to miss out, to stay up to date, and to connect. Boyd [ 32 ] herself refers to needing to go on a “digital sabbatical” in order not be on, to take a vacation from connecting, with the caveat that this means still engaging with social media, but deciding which messages to respond to.

In addition to this, teenagers particularly appear to have subscribed to the cultural norm of continual online networking. They create virtual spaces which serve their need to belong, as there appear to be increasingly limited options of analogous physical spaces due to parents’ safety concerns [ 33 ]. Being online is viewed as safer than roaming the streets and parents often assume using technology in the home is normal and healthy, as stated by a psychotherapist treating adolescents presenting with the problem of Internet addiction: “Use of digital media is the culture of the household and kids are growing up that way more and more” [ 34 ]. Interestingly, recent research has demonstrated that sharing information on social media increases life satisfaction and loneliness for younger adult users, whereas the opposite was true for older adult users [ 35 ], suggesting that social media use and social networking are used and perceived very differently across generations. This has implications for social networking addiction because the context of excessive social networking is critical in defining someone as an addict, and habitual use by teenagers might be pathologized using current screening instruments when in fact the activity—while excessive—does not result in significant detriment to the individual’s life [ 36 ].

SNS use is also driven by a number of other motivations. From a uses and gratifications perspective, these include information seeking (i.e., searching for specific information using SNS), identity formation (i.e., as a means of presenting oneself online, often more favorably than offline) [ 37 ], and entertainment (i.e., for the purpose of experiencing fun and pleasure) [ 38 ]. In addition to this, there are the motivations such as voyeurism [ 39 ] and cyberstalking [ 40 ] that could have potentially detrimental impacts on individuals’ health and wellbeing as well as their relationships.

It has also been claimed that social networking meets basic human needs as initially described in Maslow’s hierarchy of needs [ 41 ]. According to this theory, social networking meets the needs of safety, association, estimation, and self-realization [ 42 ]. Safety needs are met by social networking being customizable with regards to privacy, allowing the users to control who to share information with. Associative needs are fulfilled through the connecting function of SNSs, allowing users to ‘friend’ and ‘follow’ like-minded individuals. The need to estimate is met by users being able to ‘gather’ friends and ‘likes’, and compare oneself to others, and is therefore related to Maslow’s need of esteem. Finally, the need for self-realization, the highest attainable goal that only a small minority of individuals are able to achieve, can be reached by presenting oneself in a way one wants to present oneself, and by supporting ‘friends’ on those SNSs who require help. Accordingly, social networking taps into very fundamental human needs by offering the possibilities of social support and self-expression [ 42 ]. This may offer an explanation for the popularity of and relatively high engagement with SNSs in today’s society. However, the downside is that high engagement and being always ‘on’ or engaged with technology has been considered problematic and potentially addictive in the past [ 43 ], but if being ‘always on’ can be considered the status quo and most individuals are ‘on’ most of the time, where does this leave problematic use or addiction? The next section considers this question.

2.4. Individuals Can Become Addicted to Using Social Networking Sites

There is a growing scientific evidence base to suggest excessive SNS use may lead to symptoms traditionally associated with substance-related addictions [ 3 , 44 ]. These symptoms have been described as salience, mood modification, tolerance, withdrawal, relapse, and conflict with regards to behavioral addictions [ 45 ], and have been validated in the context of the Internet addiction components model [ 46 ]. For a small minority of individuals, their use of social networking sites may become the single most important activity that they engage in, leading to a preoccupation with SNS use (salience). The activities on these sites are then being used in order to induce mood alterations, pleasurable feelings or a numbing effect (mood modification). Increased amounts of time and energy are required to be put into engaging with SNS activities in order to achieve the same feelings and state of mind that occurred in the initial phases of usage (tolerance). When SNS use is discontinued, addicted individuals will experience negative psychological and sometimes physiological symptoms (withdrawal), often leading to a reinstatement of the problematic behavior (relapse). Problems arise as a consequence of the engagement in the problematic behavior, leading to intrapsychic (conflicts within the individual often including a subjective loss of control) and interpersonal conflicts (i.e., problems with the immediate social environment including relationship problems and work and/or education being compromised).

Whilst referring to an ‘addiction’ terminology in this paper, it needs to be noted that there is much controversy within the research field concerning both the possible overpathologising of everyday life [ 47 , 48 ] as well as the most appropriate term for the phenomenon. On the one hand, current behavioral addiction research tends to be correlational and confirmatory in nature and is often based on population studies rather than clinical samples in which psychological impairments are observed [ 47 ]. Additional methodological problems are outlined below ( Section 2.10 ). On the other hand, in the present paper, the present authors do not discriminate between the label addiction, compulsion, problematic SNS use, or other similar labels used because these terms are being used interchangeably by authors in the field. Nevertheless, when referring to ‘addiction’, the present authors refer to the presence of the above stated criteria, as these appear to hold across both substance-related as well as behavioral addictions [ 45 ] and indicate the requirement of significant impairment and distress on behalf of the individual experiencing it in order to qualify for using clinical terminology [ 49 ], such as the ‘addiction’ label.

The question then arises as what it is that individuals become addicted to. Is it the technology or is it more what the technology allows them to do? It has been argued previously [ 34 , 50 ] that the technology is but a medium or a tool that allows individuals to engage in particular behaviors, such as social networking and gaming, rather than being addictive per se . This view is supported by media scholars: “To an outsider, wanting to be always-on may seem pathological. All too often it’s labelled an addiction. The assumption is that we’re addicted to the technology. The technology doesn’t matter. It’s all about the people and information” [ 32 ]. Following this thinking, one could claim that it is not an addiction to the technology, but to connecting with people, and the good feelings that ‘likes’ and positive comments of appreciation can produce. Given that connection is the key function of social networking sites as indicated above, it appears that ‘social networking addiction’ may be considered an appropriate denomination of this potential mental health problem.

There are a numbers of models which offer explanations as to the development of SNS addiction [ 51 ]. According to the cognitive-behavioral model, excessive social networking is the consequence of maladaptive cognitions and is exacerbated through a number of external issues, resulting in addictive use. The social skill model suggests individuals use SNSs excessively as a consequence of low self-presentation skills and preference for online social interaction over face-to-face communication, resulting in addictive SNS use [ 51 ]. With respect to the socio-cognitive model, excessive social networking develops as a consequence of positive outcome expectations, Internet self-efficacy, and limited Internet self-regulation, leading to addictive SNS use [ 51 ]. It has furthermore been suggested that SNS use may become problematic when individuals use it in order to cope with everyday problems and stressors, including loneliness and depression [ 52 ]. Moreover, it has been contended that excessive SNS users find it difficult to communicate face-to-face, and social media use offers a variety of immediate rewards, such as self-efficacy and satisfaction, resulting in continued and increased use, with the consequence of exacerbating problems, including neglecting offline relationships, and problems in professional contexts. The resultant depressed moods are then dealt with by continued engagement in SNSs, leading to a vicious cycle of addiction [ 53 ]. Cross-cultural research including 10,930 adolescents from six European countries (Greece, Spain, Poland, the Netherlands, Romania, and Iceland) furthermore showed that using SNS for two or more hours a day was related to internalizing problems and decreased academic performance and activity [ 54 ]. In addition, a study using a sample of 920 secondary school students in China indicated neuroticism and extraversion predicted SNS addiction, clearly differentiating individuals who experience problems as a consequence of their excessive SNS use from those individuals who used games or the Internet in general excessively [ 55 ], further contributing to the contention that SNS addiction appears to be a behavioral problem separate from the more commonly researched gaming addiction. In a study using a relatively small representative sample of the Belgian population (n = 1000), results suggested 6.5% were using SNSs compulsively, with this group having lower scores on measures of emotional stability and agreeableness, conscientiousness, perceived control and self-esteem, and higher scores on loneliness and depressive feelings [ 56 ].

2.5. Facebook Addiction Is Only One Example of SNS Addiction

Over the past few years, research in the SNS addiction field has largely focused on a potential addiction to using Facebook specifically, rather than other SNSs (see e.g., [ 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 ]). However, recent research suggests individuals may develop addiction-related problems as a consequence of using other SNSs, such as Instagram [ 66 ]. It has been claimed that users may experience gratification through sharing photos on Instagram , similar to the gratification they experience when using Facebook , suggesting that the motivation to share photos can be explained by uses and gratifications theory [ 66 , 67 ]. This may also be the reason for why individuals have been found to be less likely to experience addiction-related symptoms when using Twitter in contrast to Instagram [ 66 ]. In addition to the gratification received through photo sharing, these websites also allow to explore new identities [ 68 ], which may be considered to contribute to gratification, as supported by previous research [ 69 ]. Research has also suggested that Instagram use in particular appears to be potentially addictive in young UK adults [ 66 ], offering further support for the contention that Facebook addiction is only one example of SNS addiction.

Other than the presence and possible addictive qualities of SNSs other than Facebook , it has been contended that the respective activities which take place on these websites need to be considered when studying addiction [ 70 ]. For instance, Facebook users can play games such as Farmville [ 36 ], gamble online [ 71 ], watch videos, share photos, update their profiles, and message their friends [ 3 ]. Other researchers have moved beyond the actual website use that is referred to in these types of addictions, and specifically focused on the main activities individuals engage in, referring to constructs such as ‘e-communication addiction’ [ 72 ]. It has also been claimed the term ‘ Facebook addiction’ is already obsolete as there are different types of SNSs that can be engaged in and different activities that can take place on these SNSs [ 70 ]. Following this justified criticism, researchers who had previously studied Facebook addiction specifically [ 58 ] have now turned to studying SNS addiction more generally instead [ 73 ], demonstrating the changing definitional parameters of social networking in this evolving field of research.

2.6. Fear of Missing Out (FOMO) May Be Part of SNS Addiction

Recent research [ 74 , 75 ] has suggested that high engagement in social networking is partially due to what has been named the ‘fear of missing out’ (FOMO). FOMO is “a pervasive apprehension that others might be having rewarding experiences from which one is absent” [ 76 ]. Higher levels of FOMO have been associated with greater engagement with Facebook , lower general mood, lower wellbeing, and lower life satisfaction, mixed feelings when using social media, as well as inappropriate and dangerous SNS use (i.e., in university lectures, and or whilst driving) [ 76 ]. In addition to this, research [ 77 ] suggests that FOMO predicts problematic SNS use and is associated with social media addiction [ 78 ], as measured with a scale adapted from the Internet Addiction Test [ 79 ]. It has been debated whether FOMO is a specific construct, or simply a component of relational insecurity, as observed for example with the attachment dimension of preoccupation with relationships in research into problematic Internet use [ 80 ].

In one study using 5280 social media users from several Spanish-speaking Latin-American countries [ 74 ] it was found that FOMO predicts negative consequences of maladaptive SNS use. In addition, this study also found that the relationship between psychopathology (as operationalized by anxiety and depression symptoms and assessed via the Hospital Anxiety and Depression Scale) and negative consequences of SNS use were mediated by FOMO, emphasizing the importance of FOMO in the self-perceived consequences of high SNS engagement. Moreover, other research [ 75 ] using 506 UK Facebook users has found that FOMO mediates the relationship between high SNS use and decreased self-esteem. Research with psychotherapists working with clients seeking help for their Internet use-related behaviors also suggested that young clients “fear the sort of relentlessness of on-going messaging (…). But concurrently with that is an absolute terror of exclusion” [ 34 ]. Taken together, these findings suggest FOMO may be a significant predictor or possible component of potential SNS addiction, a contention that requires further consideration in future research. Further work is needed into the origins of FOMO (both theoretically and empirically), as well as research into why do some SNS users are prone to FOMO and develop signs of addictions compared to those who do not.

2.7. Smartphone Addiction May Be Part of SNS Addiction

Over the last decade, research assessing problematic and possibly addictive mobile phone use (including smartphones) has proliferated [ 81 ], suggesting some individuals may develop addiction-related problems as a consequence of their mobile phone use. Recent research has suggested problematic mobile phone use is a multi-faceted condition, with dependent use being one of four possible pathways, in addition to dangerous, prohibited, and financially problematic use [ 82 ]. According to the pathway model, an addictive pattern of mobile phone use is characterized by the use of specific applications, including calls, instant messaging, and the use of social networks. This suggests that rather than being an addictive medium per se , mobile technologies including smartphones and tablets are media that enable the engagement in potentially addictive activities, including SNS use. Put another way, it could be argued that mobile phone addicts are no more addicted to their phones than alcoholics are addicted to bottles.

Similarly, it has been argued previously that individuals do not become addicted to the Internet per se , but to the activities they engage in on the Internet, such as gaming [ 50 ] or SNS use [ 3 ]. With the advent and ubiquity of mobile technologies, this supposition is more pertinent than ever. Using social networking sites is a particularly popular activity on smartphones, with around 80% of social media used via mobile technologies [ 83 ]. For instance, approximately 75% of Facebook users access the SNS via their mobile phones [ 84 ]. Therefore, it can be suggested that smartphone addiction may be part of SNS addiction. Previous research [ 73 ] supported this supposition by specifically indicating that social networking is often engaged in via phones, which may contribute to its addictive potential. Accordingly, it is necessary to move towards nosological precision, for the benefit of both individuals seeking help in professional settings, as well as research that will aid developing effective treatment approaches for those in need.

2.8. Nomophobia May Be Part of SNS Addiction

Related to both FOMO and mobile phone addiction is the construct of nomophobia. Nomophobia has been defined as “no mobile phone phobia”, i.e., the fear of being without one’s mobile phone [ 85 ]. Researchers have called for nomophobia to be included in the DSM-5, and the following criteria have been outlined to contribute to this problem constellation: regular and time-consuming use, feelings of anxiety when the phone is not available, “ringxiety” (i.e., repeatedly checking one’s phone for messages, sometimes leading to phantom ring tones), constant availability, preference for mobile communication over face to face communication, and financial problems as a consequence of use [ 85 ]. Nomophobia is inherently related to a fear of not being able to engage in social connections, and a preference for online social interaction (which is the key usage motivation for SNSs [ 3 ]), and has been linked to problematic Internet use and negative consequences of technology use [ 86 ], further pointing to a strong association between nomophobia and SNS addiction symptoms.

Using mobile phones is understood as leading to alterations in everyday life habits and perceptions of reality, which can be associated with negative outcomes, such as impaired social interactions, social isolation, as well as both somatic and mental health problems, including anxiety, depression, and stress [ 85 , 87 ]. Accordingly, nomophobia can lead to using the mobile phone in an impulsive way [ 85 ], and may thus be a contributing factor to SNS addiction as it can facilitate and enhance the repeated use of social networking sites, forming habits that may increase the general vulnerability for the experience of addiction-related symptoms as a consequence of problematic SNS use.

2.9. There Are Sociodemographic Differences in SNS Addiction

Research suggests there are sociodemographic differences among those addicted to social networking. In terms of gender, psychotherapists treating technology-use related addictions suggest SNS addiction may be more common in female rather than male patients, and describe this difference based on usage motivations:

(…) girls don’t play role-playing games primarily, but use social forums excessively, in order to experience social interaction with other girls and above all to feel understood in their very individual problem constellations, very different from boys, who want to experience narcissistic gratification via games. This means the girls want direct interaction. They want to feel understood. They want to be able to express themselves. (…) we’re getting girls with clinical pictures that are so pronounced that we have to admit them into inpatient treatment. (…) we have to develop strategies to specifically target girls much better because there appears a huge gap. Epidemiologically, they are a very important group, but we’re not getting them into consultation and treatment. [ 34 ]

This quote highlights two important findings. First, in the age group of 14–16 years, girls appear to show a higher prevalence of addictions to the Internet and SNSs, as found in a representative German sample [ 88 ], and second, teenage girls may be underrepresented in clinical samples. Moreover, another study on a representative sample demonstrated that the distribution of addiction criteria varies between genders and that extraversion is a personality trait differentiating between intensive and addictive use [ 89 ].

Cross-sectional research is less conclusive as regards the contribution of gender as a risk factor for SNS addiction. A higher prevalence of Facebook addiction was found in a sample of 423 females in Norway using the Facebook Addiction Scale [ 58 ]. Among Turkish teacher candidates, the trend was reversed, suggesting males were significantly more likely to be addicted to using Facebook [ 90 ] as assessed via an adapted version of Young’s Internet Addiction Test [ 79 ].

In other studies, no relationship between gender and addiction was found. For instance, using a version of Young’s Internet Addiction Test modified for SNS addiction in 277 young Chinese smartphone users, gender did not predict SNS addiction [ 91 ]. Similarly, another study assessing SNS dependence in 194 SNS users did not find a relationship between gender and SNS dependence [ 51 ]. In a study of 447 university students in Turkey, Facebook addiction was assessed using the Facebook Addiction Scale, but did not find a predictive relationship between gender and Facebook addiction [ 62 ].

Furthermore, the relationships between gender and SNS addiction may be further complicated by other variables. For instance, recent research by Oberst et al. [ 74 ] found that only for females, anxiety and depression symptoms significantly predicted negative consequences of SNS use. The researchers explained this difference by suggesting that anxiety and depression experience in girls may result in higher SNS usage, implicating cyclical relationships in that psychopathological symptom experience may exacerbate negative consequences due to SNS use, which may then negatively impact upon perceived anxiety and depression symptoms.

In terms of age, studies indicate that younger individuals may be more likely to develop problems as a consequence of their excessive engagement with online social networking sites [ 92 ]. Moreover, research suggests perceptions as to the extent of possible addiction appear to differ across generations. A recent study by [ 72 ] found that parents view their adolescents’ online communication as more addictive than the adolescents themselves perceive it to be. This suggests that younger generations significantly differ from older generations in how they use technology, what place it has in their lives, and how problematic they may experience their behaviors to be. It also suggests that external accounts (such as those from parents in the case of children and adolescents) may be useful for clinicians and researchers in assessing the extent of a possible problem as adolescents may not be aware of the potential negative consequences that may arise as a result of their excessive online communication use. Interestingly, research also found that mothers are more likely to view their adolescents’ behavior as potentially more addictive relative to fathers, whose perception tended to be that of online communication use being less of a problem [ 72 ]. Taken together, although there appear differences in SNS addiction with regards to sociodemographic characteristics of the samples studied, such as gender, future research is required in order to clearly indicate where these differences lie specifically, given that much of current research appears somewhat inconclusive.

2.10. There Are Methodological Problems with Research to Date

Given that the research field is relatively young, studies investigating social networking site addiction unsurprisingly suffer from a number of methodological problems. Currently, there are few estimations of the prevalence of social networking addiction with most studies comprising small and unrepresentative samples [ 3 ]. As far as the authors are aware, only one study (in Hungary) has used a nationally representative sample. The study by Bányai and colleagues [ 93 ] reported that 4.5% of 5961 adolescents (mean age 16 years old) were categorized as ‘at-risk’ of social networking addiction using the Bergen Social Media Addiction Scale. However, most studies investigating social networking addiction use various assessment tools, different diagnostic criteria as well as varying cut-off points, making generalizations and study cross-comparisons difficult [ 53 ].

Studies have made use of several different psychometric scales and six of these are briefly described below. The Addictive Tendencies Scale (ATS) [ 94 ] is based on addiction theory and uses three items, salience, loss of control, and withdrawal, whilst viewing SNS addiction as dimensional construct. The Bergen Facebook Addiction Scale (BFAS) [ 58 ] is based on Griffiths’ [ 45 ] addiction components, using a polythetic scoring method (scoring 3 out of 4 on each criterion on a minimum of four of the six criteria) and has been shown to have good psychometric properties. The Bergen Social Media Addiction Scale is similar to the BFAS in that ‘ Facebook ’ is replaced with ‘Social Media’ [ 95 ]. The E-Communication Addiction Scale [ 72 ] includes 22 questions with four subscales scored on a five-point Likert scale—addressing issues such as lack of self-control (cognitive), e-communication use in extraordinary places, worries, and control difficulty (behavioral)—and it has been found to have a high internal consistency, measuring e-communication addiction across different severity levels, ranging from very low to very high.

The Facebook Dependence Questionnaire (FDQ) [ 96 ] uses eight items based on the Internet Addiction Scale [ 97 ], with the endorsement of five out of eight criteria signifying addiction to using Facebook . The Social Networking Addiction Scale (SNWAS) [ 51 ] is a five-item scale which uses Charlton and Danforth’s engagement vs. addiction questionnaire [ 98 , 99 ] as a basis, viewing SNS addiction as a dimensional construct. This is by no means an exhaustive list, but those assessment tools highlighted here simply demonstrate that the current social networking addiction scales are based on different theoretical frameworks and use various cut-offs, and this precludes researchers from making cross-study comparisons, and severely limits the reliability of current SNS epidemiological addiction research.

Taken together, the use of different conceptualizations, assessment instruments, and cut-off points decreases the reliability of prevalence estimates because it hampers comparisons across studies, and it also questions the construct validity of SNS addiction. Accordingly, researchers are advised to develop appropriate criteria that are clinically sensitive to identify individuals who present with SNS addiction specifically, whilst clinicians will benefit from a reliable and valid diagnosis in terms of treatment development and delivery.

3. Discussion

In this paper, lessons learned from the recent empirical literature on social networking and addiction have been presented, following on from earlier work [ 3 ] when research investigating SNS addiction was in its infancy. The research presented suggests SNSs have become a way of being, with millions of people around the world regularly accessing SNSs using a variety of devices, including technologies on the go (i.e., tablets, smartphones), which appear to be particularly popular for using SNSs. The activity of social networking itself appears to be specifically eclectic and constantly changing, ranging from using traditional sites such as Facebook to more socially-based online gaming platforms and dating platforms, all allowing users to connect based on shared interests. Research has shown that there is a fine line between frequent non-problematic habitual use and problematic and possibly addictive use of SNSs, suggesting that users who experience symptoms and consequences traditionally associated with substance-related addictions (i.e., salience, mood modification, tolerance, withdrawal, relapse, and conflict) may be addicted to using SNSs. Research has also indicated that a fear of missing out (FOMO) may contribute to SNS addiction, because individuals who worry about being unable to connect to their networks may develop impulsive checking habits that over time may develop into an addiction. The same thing appears to hold true for mobile phone use and a fear of being without one’s mobile phone (i.e., nomophobia), which may be viewed as a medium that enables the engagement in SNSs (rather than being addictive per se ). Given that engaging in social networking is a key activity engaged in using mobile technologies, FOMO, nomophobia, and mobile phone addiction appear to be associated with SNS addiction, with possible implications for assessment and future research.

In addition to this, the lessons learned from current research suggest there are sociodemographic differences in SNS addiction. The lack of consistent findings regarding a relationship with gender may be due to different sampling techniques and various assessment instruments used, as well as the presence of extraneous variables that may contribute to the relationships found. All of these factors highlight possible methodological problems of current SNS addiction research (e.g., lack of cross-comparisons due to differences in sampling and classification, lack of control of confounding variables), which need to be addressed in future empirical research. In addition to this, research suggests younger generations may be more at risk for developing addictive symptoms as a consequence of their SNS use, whilst perceptions of SNS addiction appear to differ across generations. Younger individuals tend to view their SNS use as less problematic than their parents might, further contributing to the contention that SNS use has become a way of being and is contextual, which must be separated from the experience of actual psychopathological symptoms. The ultimate aim of research must be not to overpathologize everyday behaviors, but to carry out better quality research as this will help facilitate treatment efforts in order to provide support for those who may need it.

Based on the 10 lessons learned from recent SNS addiction research, the following recommendations are provided. First, researchers are recommended to consider including an assessment of FOMO and/or nomophobia in SNS addiction screening instruments because both constructs appear related to SNS addiction. Second, it is recommended that social networking site use is measured across different technologies with which it can be accessed, including mobile and smartphones. It is of fundamental importance to study what kinds of activities are being engaged in online (social networking, gaming, etc.), rather than the medium through which these activities are engaged in (i.e., desktop computer, tablet, mobile/smartphone). Third, risk factors associated with problematic social networking need to be assessed longitudinally to provide a clearer indication of developmental etiology, and to allow for the design of targeted prevention approaches. Fourth, clinical samples need to be included in research in order to ensure the sensitivity and specificity of the screening instruments developed. Fifth, in terms of treatment, unlike treating substance-related addictions, the main treatment goal should be control rather than abstinence. Arguably, abstinence cannot realistically be achieved in the context of SNS addiction because the Internet and social networking have become integral elements of our lives [ 3 , 8 , 33 ]. Rather than discontinuing social networking completely, therapy should focus on establishing controlled SNS use and media awareness [ 53 ].

4. Conclusions

This paper has outlined ten lessons learned from recent empirical literature on online social networking and addiction. Based on the presented evidence, the way forward in the emerging research field of social networking addiction requires the establishment of consensual nosological precision, so that both researchers and clinical practitioners can work together and establish productive communication between the involved parties that enable reliable and valid assessments of SNS addiction and associated behaviors (e.g., problematic mobile phone use), and the development of targeted and specific treatment approaches to ameliorate the negative consequences of such disorders.

Acknowledgments

This work did not receive any funding.

Author Contributions

The first author wrote the first complete draft of the paper based on an idea by the second author. The authors then worked collaboratively and iteratively on subsequent drafts of the paper.

Conflicts of Interest

The authors declare no conflict of interest.