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• Published: 08 May 2024

Exploring the dynamics of consumer engagement in social media influencer marketing: from the self-determination theory perspective

• Chenyu Gu   ORCID: orcid.org/0000-0001-6059-0573 1 &
• Qiuting Duan 2  

Humanities and Social Sciences Communications volume  11 , Article number:  587 ( 2024 ) Cite this article

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• Business and management
• Cultural and media studies

Influencer advertising has emerged as an integral part of social media marketing. Within this realm, consumer engagement is a critical indicator for gauging the impact of influencer advertisements, as it encompasses the proactive involvement of consumers in spreading advertisements and creating value. Therefore, investigating the mechanisms behind consumer engagement holds significant relevance for formulating effective influencer advertising strategies. The current study, grounded in self-determination theory and employing a stimulus-organism-response framework, constructs a general model to assess the impact of influencer factors, advertisement information, and social factors on consumer engagement. Analyzing data from 522 samples using structural equation modeling, the findings reveal: (1) Social media influencers are effective at generating initial online traffic but have limited influence on deeper levels of consumer engagement, cautioning advertisers against overestimating their impact; (2) The essence of higher-level engagement lies in the ad information factor, affirming that in the new media era, content remains ‘king’; (3) Interpersonal factors should also be given importance, as influencing the surrounding social groups of consumers is one of the effective ways to enhance the impact of advertising. Theoretically, current research broadens the scope of both social media and advertising effectiveness studies, forming a bridge between influencer marketing and consumer engagement. Practically, the findings offer macro-level strategic insights for influencer marketing.

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Introduction.

Recent studies have highlighted an escalating aversion among audiences towards traditional online ads, leading to a diminishing effectiveness of traditional online advertising methods (Lou et al., 2019 ). In an effort to overcome these challenges, an increasing number of brands are turning to influencers as their spokespersons for advertising. Utilizing influencers not only capitalizes on their significant influence over their fan base but also allows for the dissemination of advertising messages in a more native and organic manner. Consequently, influencer-endorsed advertising has become a pivotal component and a growing trend in social media advertising (Gräve & Bartsch, 2022 ). Although the topic of influencer-endorsed advertising has garnered increasing attention from scholars, the field is still in its infancy, offering ample opportunities for in-depth research and exploration (Barta et al., 2023 ).

Presently, social media influencers—individuals with substantial follower bases—have emerged as the new vanguard in advertising (Hudders & Lou, 2023 ). Their tweets and videos possess the remarkable potential to sway the purchasing decisions of thousands if not millions. This influence largely hinges on consumer engagement behaviors, implying that the impact of advertising can proliferate throughout a consumer’s entire social network (Abbasi et al., 2023 ). Consequently, exploring ways to enhance consumer engagement is of paramount theoretical and practical significance for advertising effectiveness research (Xiao et al., 2023 ). This necessitates researchers to delve deeper into the exploration of the stimulating factors and psychological mechanisms influencing consumer engagement behaviors (Vander Schee et al., 2020 ), which is the gap this study seeks to address.

The Stimulus-Organism-Response (S-O-R) framework has been extensively applied in the study of consumer engagement behaviors (Tak & Gupta, 2021 ) and has been shown to integrate effectively with self-determination theory (Yang et al., 2019 ). Therefore, employing the S-O-R framework to investigate consumer engagement behaviors in the context of influencer advertising is considered a rational approach. The current study embarks on an in-depth analysis of the transformation process from three distinct dimensions. In the Stimulus (S) phase, we focus on how influencer factors, advertising message factors, and social influence factors act as external stimuli. This phase scrutinizes the external environment’s role in triggering consumer reactions. During the Organism (O) phase, the research explores the intrinsic psychological motivations affecting individual behavior as posited in self-determination theory. This includes the willingness for self-disclosure, the desire for innovation, and trust in advertising messages. The investigation in this phase aims to understand how these internal motivations shape consumer attitudes and perceptions in the context of influencer marketing. Finally, in the Response (R) phase, the study examines how these psychological factors influence consumer engagement behavior. This part of the research seeks to understand the transition from internal psychological states to actual consumer behavior, particularly how these states drive the consumers’ deep integration and interaction with the influencer content.

Despite the inherent limitations of cross-sectional analysis in capturing the full temporal dynamics of consumer engagement, this study seeks to unveil the dynamic interplay between consumers’ psychological needs—autonomy, competence, and relatedness—and their varying engagement levels in social media influencer marketing, grounded in self-determination theory. Through this lens, by analyzing factors related to influencers, content, and social context, we aim to infer potential dynamic shifts in engagement behaviors as psychological needs evolve. This approach allows us to offer a snapshot of the complex, multi-dimensional nature of consumer engagement dynamics, providing valuable insights for both theoretical exploration and practical application in the constantly evolving domain of social media marketing. Moreover, the current study underscores the significance of adapting to the dynamic digital environment and highlights the evolving nature of consumer engagement in the realm of digital marketing.

Literature review

Stimulus-organism-response (s-o-r) model.

The Stimulus-Response (S-R) model, originating from behaviorist psychology and introduced by psychologist Watson ( 1917 ), posits that individual behaviors are directly induced by external environmental stimuli. However, this model overlooks internal personal factors, complicating the explanation of psychological states. Mehrabian and Russell ( 1974 ) expanded this by incorporating the individual’s cognitive component (organism) into the model, creating the Stimulus-Organism-Response (S-O-R) framework. This model has become a crucial theoretical framework in consumer psychology as it interprets internal psychological cognitions as mediators between stimuli and responses. Integrating with psychological theories, the S-O-R model effectively analyzes and explains the significant impact of internal psychological factors on behavior (Koay et al., 2020 ; Zhang et al., 2021 ), and is extensively applied in investigating user behavior on social media platforms (Hewei & Youngsook, 2022 ). This study combines the S-O-R framework with self-determination theory to examine consumer engagement behaviors in the context of social media influencer advertising, a logic also supported by some studies (Yang et al., 2021 ).

Self-determination theory

Self-determination theory, proposed by Richard and Edward (2000), is a theoretical framework exploring human behavioral motivation and personality. The theory emphasizes motivational processes, positing that individual behaviors are developed based on factors satisfying their psychological needs. It suggests that individual behavioral tendencies are influenced by the needs for competence, relatedness, and autonomy. Furthermore, self-determination theory, along with organic integration theory, indicates that individual behavioral tendencies are also affected by internal psychological motivations and external situational factors.

Self-determination theory has been validated by scholars in the study of online user behaviors. For example, Sweet applied the theory to the investigation of community building in online networks, analyzing knowledge-sharing behaviors among online community members (Sweet et al., 2020 ). Further literature review reveals the applicability of self-determination theory to consumer engagement behaviors, particularly in the context of influencer marketing advertisements. Firstly, self-determination theory is widely applied in studying the psychological motivations behind online behaviors, suggesting that the internal and external motivations outlined within the theory might also apply to exploring consumer behaviors in influencer marketing scenarios (Itani et al., 2022 ). Secondly, although research on consumer engagement in the social media influencer advertising context is still in its early stages, some studies have utilized SDT to explore behaviors such as information sharing and electronic word-of-mouth dissemination (Astuti & Hariyawan, 2021 ). These behaviors, which are part of the content contribution and creation dimensions of consumer engagement, may share similarities in the underlying psychological motivational mechanisms. Thus, this study will build upon these foundations to construct the Organism (O) component of the S-O-R model, integrating insights from SDT to further understand consumer engagement in influencer marketing.

Consumer engagement

Although scholars generally agree at a macro level to define consumer engagement as the creation of additional value by consumers or customers beyond purchasing products, the specific categorization of consumer engagement varies in different studies. For instance, Simon and Tossan interpret consumer engagement as a psychological willingness to interact with influencers (Simon & Tossan, 2018 ). However, such a broad definition lacks precision in describing various levels of engagement. Other scholars directly use tangible metrics on social media platforms, such as likes, saves, comments, and shares, to represent consumer engagement (Lee et al., 2018 ). While this quantitative approach is not flawed and can be highly effective in practical applications, it overlooks the content aspect of engagement, contradicting the “content is king” principle of advertising and marketing. We advocate for combining consumer engagement with the content aspect, as content engagement not only generates more traces of consumer online behavior (Oestreicher-Singer & Zalmanson, 2013 ) but, more importantly, content contribution and creation are central to social media advertising and marketing, going beyond mere content consumption (Qiu & Kumar, 2017 ). Meanwhile, we also need to emphasize that engagement is not a fixed state but a fluctuating process influenced by ongoing interactions between consumers and influencers, mediated by the evolving nature of social media platforms and the shifting sands of consumer preferences (Pradhan et al., 2023 ). Consumer engagement in digital environments undergoes continuous change, reflecting a journey rather than a destination (Viswanathan et al., 2017 ).

The current study adopts a widely accepted definition of consumer engagement from existing research, offering operational feasibility and aligning well with the research objectives of this paper. Consumer engagement behaviors in the context of this study encompass three dimensions: content consumption, content contribution, and content creation (Muntinga et al., 2011 ). These dimensions reflect a spectrum of digital engagement behaviors ranging from low to high levels (Schivinski et al., 2016 ). Specifically, content consumption on social media platforms represents a lower level of engagement, where consumers merely click and read the information but do not actively contribute or create user-generated content. Some studies consider this level of engagement as less significant for in-depth exploration because content consumption, compared to other forms, generates fewer visible traces of consumer behavior (Brodie et al., 2013 ). Even in a study by Qiu and Kumar, it was noted that the conversion rate of content consumption is low, contributing minimally to the success of social media marketing (Qiu & Kumar, 2017 ).

On the other hand, content contribution, especially content creation, is central to social media marketing. When consumers comment on influencer content or share information with their network nodes, it is termed content contribution, representing a medium level of online consumer engagement (Piehler et al., 2019 ). Furthermore, when consumers actively upload and post brand-related content on social media, this higher level of behavior is referred to as content creation. Content creation represents the highest level of consumer engagement (Cheung et al., 2021 ). Although medium and high levels of consumer engagement are more valuable for social media advertising and marketing, this exploratory study still retains the content consumption dimension of consumer engagement behaviors.

Theoretical framework

Internal organism factors: self-disclosure willingness, innovativeness, and information trust.

In existing research based on self-determination theory that focuses on online behavior, competence, relatedness, and autonomy are commonly considered as internal factors influencing users’ online behaviors. However, this approach sometimes strays from the context of online consumption. Therefore, in studies related to online consumption, scholars often use self-disclosure willingness as an overt representation of autonomy, innovativeness as a representation of competence, and trust as a representation of relatedness (Mahmood et al., 2019 ).

The use of these overt variables can be logically explained as follows: According to self-determination theory, individuals with a higher level of self-determination are more likely to adopt compensatory mechanisms to facilitate behavior compared to those with lower self-determination (Wehmeyer, 1999 ). Self-disclosure, a voluntary act of sharing personal information with others, is considered a key behavior in the development of interpersonal relationships. In social environments, self-disclosure can effectively alleviate stress and build social connections, while also seeking societal validation of personal ideas (Altman & Taylor, 1973 ). Social networks, as para-social entities, possess the interactive attributes of real societies and are likely to exhibit similar mechanisms. In consumer contexts, personal disclosures can include voluntary sharing of product interests, consumption experiences, and future purchase intentions (Robertshaw & Marr, 2006 ). While material incentives can prompt personal information disclosure, many consumers disclose personal information online voluntarily, which can be traced back to an intrinsic need for autonomy (Stutzman et al., 2011 ). Thus, in this study, we consider the self-disclosure willingness as a representation of high autonomy.

Innovativeness refers to an individual’s internal level of seeking novelty and represents their personality and tendency for novelty (Okazaki, 2009 ). Often used in consumer research, innovative consumers are inclined to try new technologies and possess an intrinsic motivation to use new products. Previous studies have shown that consumers with high innovativeness are more likely to search for information on new products and share their experiences and expertise with others, reflecting a recognition of their own competence (Kaushik & Rahman, 2014 ). Therefore, in consumer contexts, innovativeness is often regarded as the competence dimension within the intrinsic factors of self-determination (Wang et al., 2016 ), with external motivations like information novelty enhancing this intrinsic motivation (Lee et al., 2015 ).

Trust refers to an individual’s willingness to rely on the opinions of others they believe in. From a social psychological perspective, trust indicates the willingness to assume the risk of being harmed by another party (McAllister, 1995 ). Widely applied in social media contexts for relational marketing, information trust has been proven to positively influence the exchange and dissemination of consumer information, representing a close and advanced relationship between consumers and businesses, brands, or advertising endorsers (Steinhoff et al., 2019 ). Consumers who trust brands or social media influencers are more willing to share information without fear of exploitation (Pop et al., 2022 ), making trust a commonly used representation of the relatedness dimension in self-determination within consumer contexts.

Construction of the path from organism to response: self-determination internal factors and consumer engagement behavior

Following the logic outlined above, the current study represents the internal factors of self-determination theory through three variables: self-disclosure willingness, innovativeness, and information trust. Next, the study explores the association between these self-determination internal factors and consumer engagement behavior, thereby constructing the link between Organism (O) and Response (R).

Self-disclosure willingness and consumer engagement behavior

In the realm of social sciences, the concept of self-disclosure willingness has been thoroughly examined from diverse disciplinary perspectives, encompassing communication studies, sociology, and psychology. Viewing from the lens of social interaction dynamics, self-disclosure is acknowledged as a fundamental precondition for the initiation and development of online social relationships and interactive engagements (Luo & Hancock, 2020 ). It constitutes an indispensable component within the spectrum of interactive behaviors and the evolution of interpersonal connections. Voluntary self-disclosure is characterized by individuals divulging information about themselves, which typically remains unknown to others and is inaccessible through alternative sources. This concept aligns with the tenets of uncertainty reduction theory, which argues that during interpersonal engagements, individuals seek information about their counterparts as a means to mitigate uncertainties inherent in social interactions (Lee et al., 2008 ). Self-disclosure allows others to gain more personal information, thereby helping to reduce the uncertainty in interpersonal relationships. Such disclosure is voluntary rather than coerced, and this sharing of information can facilitate the development of relationships between individuals (Towner et al., 2022 ). Furthermore, individuals who actively engage in social media interactions (such as liking, sharing, and commenting on others’ content) often exhibit higher levels of self-disclosure (Chu et al., 2023 ); additional research indicates a positive correlation between self-disclosure and online engagement behaviors (Lee et al., 2023 ). Taking the context of the current study, the autonomous self-disclosure willingness can incline social media users to read advertising content more attentively and share information with others, and even create evaluative content. Therefore, this paper proposes the following research hypothesis:

H1a: The self-disclosure willingness is positively correlated with content consumption in consumer engagement behavior.

H1b: The self-disclosure willingness is positively correlated with content contribution in consumer engagement behavior.

H1c: The self-disclosure willingness is positively correlated with content creation in consumer engagement behavior.

Innovativeness and consumer engagement behavior

Innovativeness represents an individual’s propensity to favor new technologies and the motivation to use new products, associated with the cognitive perception of one’s self-competence. Individuals with a need for self-competence recognition often exhibit higher innovativeness (Kelley & Alden, 2016 ). Existing research indicates that users with higher levels of innovativeness are more inclined to accept new product information and share their experiences and discoveries with others in their social networks (Yusuf & Busalim, 2018 ). Similarly, in the context of this study, individuals, as followers of influencers, signify an endorsement of the influencer. Driven by innovativeness, they may be more eager to actively receive information from influencers. If they find the information valuable, they are likely to share it and even engage in active content re-creation to meet their expectations of self-image. Therefore, this paper proposes the following research hypotheses:

H2a: The innovativeness of social media users is positively correlated with content consumption in consumer engagement behavior.

H2b: The innovativeness of social media users is positively correlated with content contribution in consumer engagement behavior.

H2c: The innovativeness of social media users is positively correlated with content creation in consumer engagement behavior.

Information trust and consumer engagement

Trust refers to an individual’s willingness to rely on the statements and opinions of a target object (Moorman et al., 1993 ). Extensive research indicates that trust positively impacts information dissemination and content sharing in interpersonal communication environments (Majerczak & Strzelecki, 2022 ); when trust is established, individuals are more willing to share their resources and less suspicious of being exploited. Trust has also been shown to influence consumers’ participation in community building and content sharing on social media, demonstrating cross-cultural universality (Anaya-Sánchez et al., 2020 ).

Trust in influencer advertising information is also a key predictor of consumers’ information exchange online. With many social media users now operating under real-name policies, there is an increased inclination to trust information shared on social media over that posted by corporate accounts or anonymously. Additionally, as users’ social networks partially overlap with their real-life interpersonal networks, extensive research shows that more consumers increasingly rely on information posted and shared on social networks when making purchase decisions (Wang et al., 2016 ). This aligns with the effectiveness goals of influencer marketing advertisements and the characteristics of consumer engagement. Trust in the content posted by influencers is considered a manifestation of a strong relationship between fans and influencers, central to relationship marketing (Kim & Kim, 2021 ). Based on trust in the influencer, which then extends to trust in their content, people are more inclined to browse information posted by influencers, share this information with others, and even create their own content without fear of exploitation or negative consequences. Therefore, this paper proposes the following research hypotheses:

H3a: Information trust is positively correlated with content consumption in consumer engagement behavior.

H3b: Information trust is positively correlated with content contribution in consumer engagement behavior.

H3c: Information trust is positively correlated with content creation in consumer engagement behavior.

Construction of the path from stimulus to organism: influencer factors, advertising information factors, social factors, and self-determination internal factors

Having established the logical connection from Organism (O) to Response (R), we further construct the influence path from Stimulus (S) to Organism (O). Revisiting the definition of influencer advertising in social media, companies, and brands leverage influencers on social media platforms to disseminate advertising content, utilizing the influencers’ relationships and influence over consumers for marketing purposes. In addition to consumer’s internal factors, elements such as companies, brands, influencers, and the advertisements themselves also impact consumer behavior. Although factors like the brand image perception of companies may influence consumer behavior, considering that in influencer marketing, companies and brands do not directly interact with consumers, this study prioritizes the dimensions of influencers and advertisements. Furthermore, the impact of social factors on individual cognition and behavior is significant, thus, the current study integrates influencers, advertisements, and social dimensions as the Stimulus (S) component.

Influencer factors: parasocial identification

Self-determination theory posits that relationships are one of the key motivators influencing individual behavior. In the context of social media research, users anticipate establishing a parasocial relationship with influencers, resembling real-life relationships. Hence, we consider the parasocial identification arising from users’ parasocial interactions with influencers as the relational motivator. Parasocial interaction refers to the one-sided personal relationship that individuals develop with media characters (Donald & Richard, 1956 ). During this process, individuals believe that the media character is directly communicating with them, creating a sense of positive intimacy (Giles, 2002 ). Over time, through repeated unilateral interactions with media characters, individuals develop a parasocial relationship, leading to parasocial identification. However, parasocial identification should not be directly equated with the concept of social identification in social identity theory. Social identification occurs when individuals psychologically de-individualize themselves, perceiving the characteristics of their social group as their own, upon identifying themselves as part of that group. In contrast, parasocial identification refers to the one-sided interactional identification with media characters (such as celebrities or influencers) over time (Chen et al., 2021 ). Particularly when individuals’ needs for interpersonal interaction are not met in their daily lives, they turn to parasocial interactions to fulfill these needs (Shan et al., 2020 ). Especially on social media, which is characterized by its high visibility and interactivity, users can easily develop a strong parasocial identification with the influencers they follow (Wei et al., 2022 ).

Parasocial identification and self-disclosure willingness

Theories like uncertainty reduction, personal construct, and social exchange are often applied to explain the emergence of parasocial identification. Social media, with its convenient and interactive modes of information dissemination, enables consumers to easily follow influencers on media platforms. They can perceive the personality of influencers through their online content, viewing them as familiar individuals or even friends. Once parasocial identification develops, this pleasurable experience can significantly influence consumers’ cognitions and thus their behavioral responses. Research has explored the impact of parasocial identification on consumer behavior. For instance, Bond et al. found that on Twitter, the intensity of users’ parasocial identification with influencers positively correlates with their continuous monitoring of these influencers’ activities (Bond, 2016 ). Analogous to real life, where we tend to pay more attention to our friends in our social networks, a similar phenomenon occurs in the relationship between consumers and brands. This type of parasocial identification not only makes consumers willing to follow brand pages but also more inclined to voluntarily provide personal information (Chen et al., 2021 ). Based on this logic, we speculate that a similar relationship may exist between social media influencers and their fans. Fans develop parasocial identification with influencers through social media interactions, making them more willing to disclose their information, opinions, and views in the comment sections of the influencers they follow, engaging in more frequent social interactions (Chung & Cho, 2017 ), even if the content at times may be brand or company-embedded marketing advertisements. In other words, in the presence of influencers with whom they have established parasocial relationships, they are more inclined to disclose personal information, thereby promoting consumer engagement behavior. Therefore, we propose the following research hypotheses:

H4: Parasocial identification is positively correlated with consumer self-disclosure willingness.

H4a: Self-disclosure willingness mediates the impact of parasocial identification on content consumption in consumer engagement behavior.

H4b: Self-disclosure willingness mediates the impact of parasocial identification on content contribution in consumer engagement behavior.

H4c: Self-disclosure willingness mediates the impact of parasocial identification on content creation in consumer engagement behavior.

Parasocial identification and information trust

Information Trust refers to consumers’ willingness to trust the information contained in advertisements and to place themselves at risk. These risks include purchasing products inconsistent with the advertised information and the negative social consequences of erroneously spreading this information to others, leading to unpleasant consumption experiences (Minton, 2015 ). In advertising marketing, gaining consumers’ trust in advertising information is crucial. In the context of influencer marketing on social media, companies, and brands leverage the social connection between influencers and their fans. According to cognitive empathy theory, consumers project their trust in influencers onto the products endorsed, explaining the phenomenon of ‘loving the house for the crow on its roof.’ Research indicates that parasocial identification with influencers is a necessary condition for trust development. Consumers engage in parasocial interactions with influencers on social media, leading to parasocial identification (Jin et al., 2021 ). Consumers tend to reduce their cognitive load and simplify their decision-making processes, thus naturally adopting a positive attitude and trust towards advertising information disseminated by influencers with whom they have established parasocial identification. This forms the core logic behind the success of influencer marketing advertisements (Breves et al., 2021 ); furthermore, as mentioned earlier, because consumers trust these advertisements, they are also willing to share this information with friends and family and even engage in content re-creation. Therefore, we propose the following research hypotheses:

H5: Parasocial identification is positively correlated with information trust.

H5a: Information trust mediates the impact of parasocial identification on content consumption in consumer engagement behavior.

H5b: Information trust mediates the impact of parasocial identification on content contribution in consumer engagement behavior.

H5c: Information trust mediates the impact of parasocial identification on content creation in consumer engagement behavior.

Influencer factors: source credibility

Source credibility refers to the degree of trust consumers place in the influencer as a source, based on the influencer’s reliability and expertise. Numerous studies have validated the effectiveness of the endorsement effect in advertising (Schouten et al., 2021 ). The Source Credibility Model, proposed by the renowned American communication scholar Hovland and the “Yale School,” posits that in the process of information dissemination, the credibility of the source can influence the audience’s decision to accept the information. The credibility of the information is determined by two aspects of the source: reliability and expertise. Reliability refers to the audience’s trust in the “communicator’s objective and honest approach to providing information,” while expertise refers to the audience’s trust in the “communicator being perceived as an effective source of information” (Hovland et al., 1953 ). Hovland’s definitions reveal that the interpretation of source credibility is not about the inherent traits of the source itself but rather the audience’s perception of the source (Jang et al., 2021 ). This differs from trust and serves as a precursor to the development of trust. Specifically, reliability and expertise are based on the audience’s perception; thus, this aligns closely with the audience’s perception of influencers (Kim & Kim, 2021 ). This credibility is a cognitive statement about the source of information.

Source credibility and self-disclosure willingness

Some studies have confirmed the positive impact of an influencer’s self-disclosure on their credibility as a source (Leite & Baptista, 2022 ). However, few have explored the impact of an influencer’s credibility, as a source, on consumers’ self-disclosure willingness. Undoubtedly, an impact exists; self-disclosure is considered a method to attempt to increase intimacy with others (Leite et al., 2022 ). According to social exchange theory, people promote relationships through the exchange of information in interpersonal communication to gain benefits (Cropanzano & Mitchell, 2005 ). Credibility, deriving from an influencer’s expertise and reliability, means that a highly credible influencer may provide more valuable information to consumers. Therefore, based on the social exchange theory’s logic of reciprocal benefits, consumers might be more willing to disclose their information to trustworthy influencers, potentially even expanding social interactions through further consumer engagement behaviors. Thus, we propose the following research hypotheses:

H6: Source credibility is positively correlated with self-disclosure willingness.

H6a: Self-disclosure willingness mediates the impact of Source credibility on content consumption in consumer engagement behavior.

H6b: Self-disclosure willingness mediates the impact of Source credibility on content contribution in consumer engagement behavior.

H6c: Self-disclosure willingness mediates the impact of Source credibility on content creation in consumer engagement behavior.

Source credibility and information trust

Based on the Source Credibility Model, the credibility of an endorser as an information source can significantly influence consumers’ acceptance of the information (Shan et al., 2020 ). Existing research has demonstrated the positive impact of source credibility on consumers. Djafarova, in a study based on Instagram, noted through in-depth interviews with 18 users that an influencer’s credibility significantly affects respondents’ trust in the information they post. This credibility is composed of expertise and relevance to consumers, and influencers on social media are considered more trustworthy than traditional celebrities (Djafarova & Rushworth, 2017 ). Subsequently, Bao and colleagues validated in the Chinese consumer context, based on the ELM model and commitment-trust theory, that the credibility of brand pages on Weibo effectively fosters consumer trust in the brand, encouraging participation in marketing activities (Bao & Wang, 2021 ). Moreover, Hsieh et al. found that in e-commerce contexts, the credibility of the source is a significant factor influencing consumers’ trust in advertising information (Hsieh & Li, 2020 ). In summary, existing research has proven that the credibility of the source can promote consumer trust. Influencer credibility is a significant antecedent affecting consumers’ trust in the advertised content they publish. In brand communities, trust can foster consumer engagement behaviors (Habibi et al., 2014 ). Specifically, consumers are more likely to trust the advertising content published by influencers with higher credibility (more expertise and reliability), and as previously mentioned, consumer engagement behavior is more likely to occur. Based on this, the study proposes the following research hypotheses:

H7: Source credibility is positively correlated with information trust.

H7a: Information trust mediates the impact of source credibility on content consumption in consumer engagement behavior.

H7b: Information trust mediates the impact of source credibility on content contribution in consumer engagement behavior.

H7c: Information trust mediates the impact of source credibility on content creation in consumer engagement behavior.

Advertising information factors: informative value

Advertising value refers to “the relative utility value of advertising information to consumers and is a subjective evaluation by consumers.” In his research, Ducoffe pointed out that in the context of online advertising, the informative value of advertising is a significant component of advertising value (Ducoffe, 1995 ). Subsequent studies have proven that consumers’ perception of advertising value can effectively promote their behavioral response to advertisements (Van-Tien Dao et al., 2014 ). Informative value of advertising refers to “the information about products needed by consumers provided by the advertisement and its ability to enhance consumer purchase satisfaction.” From the perspective of information dissemination, valuable advertising information should help consumers make better purchasing decisions and reduce the effort spent searching for product information. The informational aspect of advertising has been proven to effectively influence consumers’ cognition and, in turn, their behavior (Haida & Rahim, 2015 ).

Informative value and innovativeness

As previously discussed, consumers’ innovativeness refers to their psychological trait of favoring new things. Studies have shown that consumers with high innovativeness prefer novel and valuable product information, as it satisfies their need for newness and information about new products, making it an important factor in social media advertising engagement (Shi, 2018 ). This paper also hypothesizes that advertisements with high informative value can activate consumers’ innovativeness, as the novelty of information is one of the measures of informative value (León et al., 2009 ). Acquiring valuable information can make individuals feel good about themselves and fulfill their perception of a “novel image.” According to social exchange theory, consumers can gain social capital in interpersonal interactions (such as social recognition) by sharing information about these new products they perceive as valuable. Therefore, the current study proposes the following research hypothesis:

H8: Informative value is positively correlated with innovativeness.

H8a: Innovativeness mediates the impact of informative value on content consumption in consumer engagement behavior.

H8b: Innovativeness mediates the impact of informative value on content contribution in consumer engagement behavior.

H8c: Innovativeness mediates the impact of informative value on content creation in consumer engagement behavior.

Informative value and information trust

Trust is a multi-layered concept explored across various disciplines, including communication, marketing, sociology, and psychology. For the purposes of this paper, a deep analysis of different levels of trust is not undertaken. Here, trust specifically refers to the trust in influencer advertising information within the context of social media marketing, denoting consumers’ belief in and reliance on the advertising information endorsed by influencers. Racherla et al. investigated the factors influencing consumers’ trust in online reviews, suggesting that information quality and value contribute to increasing trust (Racherla et al., 2012 ). Similarly, Luo and Yuan, in a study based on social media marketing, also confirmed that the value of advertising information posted on brand pages can foster consumer trust in the content (Lou & Yuan, 2019 ). Therefore, by analogy, this paper posits that the informative value of influencer-endorsed advertising can also promote consumer trust in that advertising information. The relationship between trust in advertising information and consumer engagement behavior has been discussed earlier. Thus, the current study proposes the following research hypotheses:

H9: Informative value is positively correlated with information trust.

H9a: Information trust mediates the impact of informative value on content consumption in consumer engagement behavior.

H9b: Information trust mediates the impact of informative value on content contribution in consumer engagement behavior.

H9c: Information trust mediates the impact of informative value on content creation in consumer engagement behavior.

Advertising information factors: ad targeting accuracy

Ad targeting accuracy refers to the degree of match between the substantive information contained in advertising content and consumer needs. Advertisements containing precise information often yield good advertising outcomes. In marketing practice, advertisers frequently use information technology to analyze the characteristics of different consumer groups in the target market and then target their advertisements accordingly to achieve precise dissemination and, consequently, effective advertising results. The utility of ad targeting accuracy has been confirmed by many studies. For instance, in the research by Qiu and Chen, using a modified UTAUT model, it was demonstrated that the accuracy of advertising effectively promotes consumer acceptance of advertisements in WeChat Moments (Qiu & Chen, 2018 ). Although some studies on targeted advertising also indicate that overly precise ads may raise concerns about personal privacy (Zhang et al., 2019 ), overall, the accuracy of advertising information is effective in enhancing advertising outcomes and is a key element in the success of targeted advertising.

Ad targeting accuracy and information trust

In influencer marketing advertisements, due to the special relationship recognition between consumers and influencers, the privacy concerns associated with ad targeting accuracy are alleviated (Vrontis et al., 2021 ). Meanwhile, the informative value brought by targeting accuracy is highlighted. More precise advertising content implies higher informative value and also signifies that the advertising content is more worthy of consumer trust (Della Vigna, Gentzkow, 2010 ). As previously discussed, people are more inclined to read and engage with advertising content they trust and recognize. Therefore, the current study proposes the following research hypotheses:

H10: Ad targeting accuracy is positively correlated with information trust.

H10a: Information trust mediates the impact of ad targeting accuracy on content consumption in consumer engagement behavior.

H10b: Information trust mediates the impact of ad targeting accuracy on content contribution in consumer engagement behavior.

H10c: Information trust mediates the impact of ad targeting accuracy on content creation in consumer engagement behavior.

Social factors: subjective norm

The Theory of Planned Behavior, proposed by Ajzen ( 1991 ), suggests that individuals’ actions are preceded by conscious choices and are underlain by plans. TPB has been widely used by scholars in studying personal online behaviors, these studies collectively validate the applicability of TPB in the context of social media for researching online behaviors (Huang, 2023 ). Additionally, the self-determination theory, which underpins this chapter’s research, also supports the notion that individuals’ behavioral decisions are based on internal cognitions, aligning with TPB’s assertions. Therefore, this paper intends to select subjective norms from TPB as a factor of social influence. Subjective norm refers to an individual’s perception of the expectations of significant others in their social relationships regarding their behavior. Empirical research in the consumption field has demonstrated the significant impact of subjective norms on individual psychological cognition (Yang & Jolly, 2009 ). A meta-analysis by Hagger, Chatzisarantis ( 2009 ) even highlighted the statistically significant association between subjective norms and self-determination factors. Consequently, this study further explores its application in the context of influencer marketing advertisements on social media.

Subjective norm and self-disclosure willingness

In numerous studies on social media privacy, subjective norms significantly influence an individual’s self-disclosure willingness. Wirth et al. ( 2019 ) based on the privacy calculus theory, surveyed 1,466 participants and found that personal self-disclosure on social media is influenced by the behavioral expectations of other significant reference groups around them. Their research confirmed that subjective norms positively influence self-disclosure of information and highlighted that individuals’ cognitions and behaviors cannot ignore social and environmental factors. Heirman et al. ( 2013 ) in an experiment with Instagram users, also noted that subjective norms could promote positive consumer behavioral responses. Specifically, when important family members and friends highly regard social media influencers as trustworthy, we may also be more inclined to disclose our information to influencers and share this information with our surrounding family and friends without fear of disapproval. In our subjective norms, this is considered a positive and valuable interactive behavior, leading us to exhibit engagement behaviors. Based on this logic, we propose the following research hypotheses:

H11: Subjective norms are positively correlated with self-disclosure willingness.

H11a: Self-disclosure willingness mediates the impact of subjective norms on content consumption in consumer engagement behavior.

H11b: Self-disclosure willingness mediates the impact of subjective norms on content contribution in consumer engagement behavior.

H11c: Self-disclosure willingness mediates the impact of subjective norms on content creation in consumer engagement behavior.

Subjective norm and information trust

Numerous studies have indicated that subjective norms significantly influence trust (Roh et al., 2022 ). This can be explained by reference group theory, suggesting people tend to minimize the effort expended in decision-making processes, often looking to the behaviors or attitudes of others as a point of reference; for instance, subjective norms can foster acceptance of technology by enhancing trust (Gupta et al., 2021 ). Analogously, if a consumer’s social network generally holds positive attitudes toward influencer advertising, they are also more likely to trust the endorsed advertisement information, as it conserves the extensive effort required in gathering product information (Chetioui et al., 2020 ). Therefore, this paper proposes the following research hypotheses:

H12: Subjective norms are positively correlated with information trust.

H12a: Information trust mediates the impact of subjective norms on content consumption in consumer engagement behavior.

H12b: Information trust mediates the impact of subjective norms on content contribution in consumer engagement behavior.

H12c: Information trust mediates the impact of subjective norms on content creation in consumer engagement behavior.

Conceptual model

In summary, based on the Stimulus (S)-Organism (O)-Response (R) framework, this study constructs the external stimulus factors (S) from three dimensions: influencer factors (parasocial identification, source credibility), advertising information factors (informative value, Ad targeting accuracy), and social influence factors (subjective norms). This is grounded in social capital theory and the theory of planned behavior. drawing on self-determination theory, the current study constructs the individual psychological factors (O) using self-disclosure willingness, innovativeness, and information trust. Finally, the behavioral response (R) is constructed using consumer engagement, which includes content consumption, content contribution, and content creation, as illustrated in Fig. 1 .

Consumer engagement behavior impact model based on SOR framework.

Materials and methods

Participants and procedures.

The current study conducted a survey through the Wenjuanxing platform to collect data. Participants were recruited through social media platforms such as WeChat, Douyin, Weibo et al., as samples drawn from social media users better align with the research purpose of our research and ensure the validity of the sample. Before the survey commenced, all participants were explicitly informed about the purpose of this study, and it was made clear that volunteers could withdraw from the survey at any time. Initially, 600 questionnaires were collected, with 78 invalid responses excluded. The criteria for valid questionnaires were as follows: (1) Respondents must have answered “Yes” to the question, “Do you follow any influencers (internet celebrities) on social media platforms?” as samples not using social media or not following influencers do not meet the study’s objective, making this question a prerequisite for continuing the survey; (2) Respondents had to correctly answer two hidden screening questions within the questionnaire to ensure that they did not randomly select scores; (3) The total time taken to complete the questionnaire had to exceed one minute, ensuring that respondents had sufficient time to understand and thoughtfully answer each question; (4) Respondents were not allowed to choose the same score for eight consecutive questions. Ultimately, 522 valid questionnaires were obtained, with an effective rate of 87.00%, meeting the basic sample size requirements for research models (Gefen et al., 2011 ). Detailed demographic information of the study participants is presented in Table 1 .

Measurements

To ensure the validity and reliability of the data analysis results in this study, the measurement tools and scales used in this chapter were designed with reference to existing established research. The main variables in the survey questionnaire include parasocial identification, source credibility, informative value, ad targeting accuracy, subjective norms, self-disclosure willingness, innovativeness, information trust, content consumption, content contribution, and content creation. The measurement scale for parasocial identification was adapted from the research of Schramm and Hartmann, comprising 6 items (Schramm & Hartmann, 2008 ). The source credibility scale was combined from the studies of Cheung et al. and Luo & Yuan’s research in the context of social media influencer marketing, including 4 items (Cheung et al., 2009 ; Lou & Yuan, 2019 ). The scale for informative value was modified based on Voss et al.‘s research, consisting of 4 items (Voss et al., 2003 ). The ad targeting accuracy scale was derived from the research by Qiu Aimei et al., 2018 ) including 3 items. The subjective norm scale was adapted from Ajzen’s original scale, comprising 3 items (Ajzen, 2002 ). The self-disclosure willingness scale was developed based on Chu and Kim’s research, including 3 items (Chu & Kim, 2011 ). The innovativeness scale was formulated following the study by Sun et al., comprising 4 items (Sun et al., 2006 ). The information trust scale was created in reference to Chu and Choi’s research, including 3 items (Chu & Choi, 2011 ). The scales for the three components of social media consumer engagement—content consumption, content contribution, and content creation—were sourced from the research by Buzeta et al., encompassing 8 items in total (Buzeta et al., 2020 ).

All scales were appropriately revised for the context of social media influencer marketing. To avoid issues with scoring neutral attitudes, a uniform Likert seven-point scale was used for each measurement item (ranging from 1 to 7, representing a spectrum from ‘strongly disagree’ to ‘strongly agree’). After the overall design of the questionnaire was completed, a pre-test was conducted with 30 social media users to ensure that potential respondents could clearly understand the meaning of each question and that there were no obstacles to answering. This pre-test aimed to prevent any difficulties or misunderstandings in the questionnaire items. The final version of the questionnaire is presented in Table 2 .

Data analysis

Since the model framework of the current study is derived from theoretical deductions of existing research and, while logically constructed, does not originate from an existing research model, this study still falls under the category of exploratory research. According to the analysis suggestions of Hair and other scholars, in cases of exploratory research model frameworks, it is more appropriate to choose Smart PLS for Partial Least Squares Path Analysis (PLS) to conduct data analysis and testing of the research model (Hair et al., 2012 ).

Measurement of model

In this study, careful data collection and management resulted in no missing values in the dataset. This ensured the integrity and reliability of the subsequent data analysis. As shown in Table 3 , after deleting measurement items with factor loadings below 0.5, the final factor loadings of the measurement items in this study range from 0.730 to 0.964. This indicates that all measurement items meet the retention criteria. Additionally, the Cronbach’s α values of the latent variables range from 0.805 to 0.924, and all latent variables have Composite Reliability (CR) values greater than the acceptable value of 0.7, demonstrating that the scales of this study have passed the reliability test requirements (Hair et al., 2019 ). All latent variables in this study have Average Variance Extracted (AVE) values greater than the standard acceptance value of 0.5, indicating that the convergent validity of the variables also meets the standard (Fornell & Larcker, 1981 ). Furthermore, the results show that the Variance Inflation Factor (VIF) values for each factor are below 10, indicating that there are no multicollinearity issues with the scales in this study (Hair, 2009 ).

The current study then further verified the discriminant validity of the variables, with specific results shown in Table 4 . The square roots of the average variance extracted (AVE) values for all variables (bolded on the diagonal) are greater than the Pearson correlation coefficients between the variables, indicating that the discriminant validity of the scales in this study meets the required standards (Fornell & Larcker, 1981 ). Additionally, a single-factor test method was employed to examine common method bias in the data. The first unrotated factor accounted for 29.71% of the variance, which is less than the critical threshold of 40%. Therefore, the study passed the test and did not exhibit serious common method bias (Podsakoff et al., 2003 ).

To ensure the robustness and appropriateness of our structural equation model, we also conducted a thorough evaluation of the model fit. Initially, through PLS Algorithm calculations, the R 2 values of each variable were greater than the standard acceptance value of 0.1, indicating good predictive accuracy of the model. Subsequently, Blindfolding calculations were performed, and the results showed that the Stone-Geisser Q 2 values of each variable were greater than 0, demonstrating that the model of this study effectively predicts the relationships between variables (Dijkstra & Henseler, 2015 ). In addition, through CFA, we also obtained some indicator values, specifically, χ 2 /df = 2.528 < 0.3, RMSEA = 0.059 < 0.06, SRMR = 0.055 < 0.08. Given its sensitivity to sample size, we primarily focused on the CFI, TLI, and NFI values, CFI = 0.953 > 0.9, TLI = 0.942 > 0.9, and NFI = 0.923 > 0.9 indicating a good fit. Additionally, RMSEA values below 0.06 and SRMR values below 0.08 were considered indicative of a good model fit. These indices collectively suggested that our model demonstrates a satisfactory fit with the data, thereby reinforcing the validity of our findings.

Research hypothesis testing

The current study employed a Bootstrapping test with a sample size of 5000 on the collected raw data to explore the coefficients and significance of the paths in the research model. The final test data results of this study’s model are presented in Table 5 .

The current study employs S-O-R model as the framework, grounded in theories such as self-determination theory and theory of planned behavior, to construct an influence model of consumer engagement behavior in the context of social media influencer marketing. It examines how influencer factors, advertisement information factors, and social influence factors affect consumer engagement behavior by impacting consumers’ psychological cognitions. Using structural equation modeling to analyze collected data ( N  = 522), it was found that self-disclosure willingness, innovativeness, and information trust positively influence consumer engagement behavior, with innovativeness having the largest impact on higher levels of engagement. Influencer factors, advertisement information factors, and social factors serve as effective external stimuli, influencing psychological motivators and, consequently, consumer engagement behavior. The specific research results are illustrated in Fig. 2 .

Tested structural model of consumer engagement behavior.

The impact of psychological motivators on different levels of consumer engagement: self-disclosure willingness, innovativeness, and information trust

The research analysis indicates that self-disclosure willingness and information trust are key drivers for content consumption (H1a, H2a validated). This aligns with previous findings that individuals with a higher willingness to disclose themselves show greater levels of engagement behavior (Chu et al., 2023 ); likewise, individuals who trust advertisement information are more inclined to engage with advertisement content (Kim, Kim, 2021 ). Moreover, our study finds that information trust has a stronger impact on content consumption, underscoring the importance of trust in the dissemination of advertisement information. However, no significant association was found between individual innovativeness and content consumption (H3a not validated).

Regarding the dimension of content contribution in consumer engagement, self-disclosure willingness, information trust, and innovativeness all positively impact it (H1b, H2b, and H3b all validated). This is consistent with earlier research findings that individuals with higher self-disclosure willingness are more likely to like, comment on, or share content posted by influencers on social media platforms (Towner et al., 2022 ); the conclusions of this paper also support that innovativeness is an important psychological driver for active participation in social media interactions (Kamboj & Sharma, 2023 ). However, at the level of consumer engagement in content contribution, while information trust also exerts a positive effect, its impact is the weakest, although information trust has the strongest impact on content consumption.

In social media advertising, the ideal outcome is the highest level of consumer engagement, i.e., content creation, meaning consumers actively join in brand content creation, seeing themselves as co-creators with the brand (Nadeem et al., 2021 ). Our findings reveal that self-disclosure willingness, innovativeness, and information trust all positively influence content creation (H1c, H2c, and H3c all validated). The analysis found that similar to the impact on content contribution, innovativeness has the most significant effect on encouraging individual content creation, followed by self-disclosure willingness, with information trust having the least impact.

In summary, while some previous studies have shown that self-disclosure willingness, innovativeness, and information trust are important factors in promoting consumer engagement (Chu et al., 2023 ; Nadeem et al., 2021 ; Geng et al., 2021 ), this study goes further by integrating and comparing all three within the same research framework. It was found that to trigger higher levels of consumer engagement behavior, trust is not the most crucial psychological motivator; rather, the most effective method is to stimulate consumers’ innovativeness, thus complementing previous research. Subsequently, this study further explores the impact of different stimulus factors on various psychological motivators.

The influence of external stimulus factors on psychological motivators: influencer factors, advertisement information factors, and social factors

The current findings indicate that influencer factors, such as parasocial identification and source credibility, effectively enhance consumer engagement by influencing self-disclosure willingness and information trust. This aligns with prior research highlighting the significance of parasocial identification (Shan et al., 2020 ). Studies suggest parasocial identification positively impacts consumer engagement by boosting self-disclosure willingness and information trust (validated H4a, H4b, H4c, and H5a), but not content contribution or creation through information trust (H5b, H5c not validated). Source credibility’s influence on self-disclosure willingness was not significant (H6 not validated), thus negating the mediating effect of self-disclosure willingness (H6a, H6b, H6c not validated). Influencer credibility mainly affects engagement through information trust (H7a, H7b, H7c validated), supporting previous findings (Shan et al., 2020 ).

Advertisement factors (informative value and ad targeting accuracy) promote engagement through innovativeness and information trust. Informative value significantly impacts higher-level content contribution and creation through innovativeness (H8b, H8c validated), while ad targeting accuracy influences consumer engagement at all levels mainly through information trust (H10a, H10b, H10c validated).

Social factors (subjective norms) enhance self-disclosure willingness and information trust, consistent with previous research (Wirth et al., 2019 ; Gupta et al., 2021 ), and further promote consumer engagement across all levels (H11a, H11b, H11c, H12a, H12b, and H12c all validated).

In summary, influencer, advertisement, and social factors impact consumer engagement behavior by influencing psychological motivators, with influencer factors having the greatest effect on content consumption, advertisement content factors significantly raising higher-level consumer engagement through innovativeness, and social factors also influencing engagement through self-disclosure willingness and information trust.

Implication

From a theoretical perspective, current research presents a comprehensive model of consumer engagement within the context of influencer advertising on social media. This model not only expands the research horizon in the fields of social media influencer advertising and consumer engagement but also serves as a bridge between two crucial themes in new media advertising studies. Influencer advertising has become an integral part of social media advertising, and the construction of a macro model aids researchers in understanding consumer psychological processes and behavioral patterns. It also assists advertisers in formulating more effective strategies. Consumer engagement, focusing on the active role of consumers in disseminating information and the long-term impact on advertising effectiveness, aligns more closely with the advertising effectiveness measures in the new media context than traditional advertising metrics. However, the intersection of these two vital themes lacks comprehensive research and a universal model. This study constructs a model that elucidates the effects of various stimuli on consumer psychology and engagement behaviors, exploring the connections and mechanisms through different mediating pathways. By differentiating levels of engagement, the study offers more nuanced conclusions for diverse advertising objectives. Furthermore, this research validates the applicability of self-determination theory in the context of influencer advertising effectiveness. While this psychological theory has been utilized in communication behavior research, its effectiveness in the field of advertising requires further exploration. The current study introduces self-determination theory into the realm of influencer advertising and consumer engagement, thereby expanding its application in the field of advertising communication. It also responds to the call from the advertising and marketing academic community to incorporate more psychological theories to explain the ‘black box’ of consumer psychology. The inclusion of this theory re-emphasizes the people-centric approach of this research and highlights the primary role of individuals in advertising communication studies.

From a practical perspective, this study provides significant insights for adapting marketing strategies to the evolving media landscape and the empowered role of audiences. Firstly, in the face of changes in the communication environment and the empowerment of audience communication capabilities, traditional marketing approaches are becoming inadequate for new media advertising needs. Traditional advertising focuses on direct, point-to-point effects, whereas social media advertising aims for broader, point-to-mass communication, leveraging audience proactivity to facilitate the viral spread of content across online social networks. Secondly, for brands, the general influence model proposed in this study offers guidance for influencer advertising strategy. If the goal is to maximize reach and brand recognition with a substantial advertising budget, partnering with top influencers who have a large following can be an effective strategy. However, if the objective is to maximize cost-effectiveness with a limited budget by leveraging consumer initiative for secondary spread, the focus should be on designing advertising content that stimulates consumer creativity and willingness to innovate. Thirdly, influencers are advised to remain true to their followers. In influencer marketing, influencers attract advertisers through their influence over followers, converting this influence into commercial gain. This influence stems from the trust followers place in the influencer, thus influencers should maintain professional integrity and prioritize the quality of information they share, even when presented with advertising opportunities. Lastly, influencers should assert more control over their relationships with advertisers. In traditional advertising, companies and brands often exert significant control over the content. However, in the social media era, influencers should negotiate more creative freedom in their advertising partnerships, asserting a more equal relationship with advertisers. This approach ensures that content quality remains high, maintaining the trust influencers have built with their followers.

Limitations and future directions

while this study offers valuable insights into the dynamics of influencer marketing and consumer engagement on social media, several limitations should be acknowledged: Firstly, constrained by the research objectives and scope, this study’s proposed general impact model covers three dimensions: influencers, advertisement information, and social factors. However, these dimensions are not limited to the five variables discussed in this paper. Therefore, we call for future research to supplement and explore more crucial factors. Secondly, in the actual communication environment, there may be differences in the impact of communication effectiveness across various social media platforms. Thus, future research could also involve comparative studies and explorations between different social media platforms. Thirdly, the current study primarily examines the direct effects of various factors on consumer engagement. However, the potential interaction effects between these variables (e.g., how influencers’ credibility might interact with advertisement information quality) are not extensively explored. Future research could investigate these complex interrelationships for a more holistic understanding. Lastly, our study, being cross-sectional, offers preliminary insights into the complex and dynamic nature of engagement between social media influencers and consumers, yet it does not incorporate the temporal dimension. The diverse impacts of psychological needs on engagement behaviors hint at an underlying dynamism that merits further investigation. Future research should consider employing longitudinal designs to directly observe how these dynamics evolve over time.

The findings of the current study not only theoretically validate the applicability of self-determination theory in the field of social media influencer marketing advertising research but also broaden the scope of advertising effectiveness research from the perspective of consumer engagement. Moreover, the research framework offers strategic guidance and reference for influencer marketing strategies. The main conclusions of this study can be summarized as follows.

Innovativeness is the key factor in high-level consumer engagement behavior. Content contribution represents a higher level of consumer engagement compared to content consumption, as it not only requires consumers to dedicate attention to viewing advertising content but also to share this information across adjacent nodes within their social networks. This dissemination of information is a pivotal factor in the success of influencer marketing advertisements. Hence, companies and brands prioritize consumers’ content contribution over mere viewing of advertising content (Qiu & Kumar, 2017 ). Compared to content consumption and contribution, content creation is considered the highest level of consumer engagement, where consumers actively create and upload brand-related content, and it represents the most advanced outcome sought by enterprises and brands in advertising campaigns (Cheung et al., 2021 ). The current study posits that to pursue better outcomes in social media influencer advertising marketing, enhancing consumers’ willingness for self-disclosure, innovativeness, and trust in advertising information are effective strategies. However, the crux lies in leveraging the consumer’s subjective initiative, particularly in boosting their innovativeness. If the goal is simply to achieve content consumption rather than higher levels of consumer engagement, the focus should be on fostering trust in advertising information. There is no hierarchy in the efficacy of different strategies; they should align with varying marketing contexts and advertising objectives.

The greatest role of social media influencers lies in attracting online traffic. information trust is the core element driving content consumption, and influencer factors mainly affect consumer engagement behaviors through information trust. Therefore, this study suggests that the primary role of influencers in social media advertising is to attract online traffic, i.e., increase consumer behavior regarding ad content consumption (reducing avoidance of ad content), and help brands achieve the initial goal of making consumers “see and complete ads.” However, their impact on further high-level consumer engagement behaviors is limited. This mechanism serves as a reminder to advertisers not to overestimate the effects of influencers in marketing. Currently, top influencers command a significant portion of the ad budget, which could squeeze the budget for other aspects of advertising, potentially affecting the overall effectiveness of the campaign. Businesses and brands should consider deeper strategic implications when planning their advertising campaigns.

Valuing Advertising Information Factors, Content Remains King. Our study posits that in the social media influencer marketing context, the key to enhancing consumer contribution and creation of advertising content lies primarily in the advertising information factors. In other words, while content consumption is important, advertisers should objectively assess the role influencers play in advertising. In the era of social media, content remains ‘king’ in advertising. This view indirectly echoes the points made in the previous paragraph: influencers effectively perform initial ‘online traffic generation’ tasks in social media, but this role should not be overly romanticized or exaggerated. Whether it’s companies, brands, or influencers, providing consumers with advertisements rich in informational value is crucial to achieving better advertising outcomes and potentially converting consumers into stakeholders.

Subjective norm is an unignorable social influence factor. Social media is characterized by its network structure of information dissemination, where a node’s information is visible to adjacent nodes. For instance, if user A likes a piece of content C from influencer I, A’s follower B, who may not follow influencer I, can still see content C via user A’s page. The aim of marketing in the social media era is to influence a node and then spread the information to adjacent nodes, either secondarily or multiple times (Kumar & Panda, 2020 ). According to the Theory of Planned Behavior, an individual’s actions are influenced by significant others in their lives, such as family and friends. Previous studies have proven the effectiveness of the Theory of Planned Behavior in influencing attitudes toward social media advertising (Ranjbarian et al., 2012 ). Current research further confirms that subjective norms also influence consumer engagement behaviors in influencer marketing on social media. Therefore, in advertising practice, brands should not only focus on individual consumers but also invest efforts in groups that can influence consumer decisions. Changing consumer behavior in the era of social media marketing doesn’t solely rely on the company’s efforts.

As communication technology advances, media platforms will further empower individual communicative capabilities, moving beyond the era of the “magic bullet” theory. The distinction between being a recipient and a transmitter of information is increasingly blurred. In an era where everyone is both an audience and an influencer, research confined to the role of the ‘recipient’ falls short of addressing the dynamics of ‘transmission’. Future research in marketing and advertising should thus focus more on the power of individual transmission. Furthermore, as Marshall McLuhan famously said, “the medium is the extension of man.” The evolution of media technology remains human-centric. Accordingly, future marketing research, while paying heed to media transformations, should emphasize the centrality of the ‘human’ element.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to privacy issues. Making the full data set publicly available could potentially breach the privacy that was promised to participants when they agreed to take part, and may breach the ethics approval for the study. The data are available from the corresponding author on reasonable request.

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The authors thank all the participants of this study. The participants were all informed about the purpose and content of the study and voluntarily agreed to participate. The participants were able to stop participating at any time without penalty. Funding for this study was provided by Minjiang University Research Start-up Funds (No. 324-32404314).

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Conceptualization: CG; methodology: CG and QD; software: CG and QD; validation: CG; formal analysis: CG and QD; investigation: CG and QD; resources: CG; data curation: CG and QD; writing—original draft preparation: CG; writing—review and editing: CG; visualization: CG; project administration: CG. All authors have read and agreed to the published version of the manuscript.

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Gu, C., Duan, Q. Exploring the dynamics of consumer engagement in social media influencer marketing: from the self-determination theory perspective. Humanit Soc Sci Commun 11 , 587 (2024). https://doi.org/10.1057/s41599-024-03127-w

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Research Article

Social impact in social media: A new method to evaluate the social impact of research

Roles Investigation, Writing – original draft

* E-mail: [email protected]

Affiliation Department of Journalism and Communication Studies, Universitat Autonoma de Barcelona, Barcelona, Spain

Affiliation Department of Psychology and Sociology, Universidad de Zaragoza, Zaragoza, Spain

Roles Conceptualization, Investigation, Methodology, Supervision, Writing – review & editing

Affiliation Department of Sociology, Universitat Autonoma de Barcelona, Barcelona, Spain

Affiliation Department of Sociology, Universitat de Barcelona (UB), Barcelona, Spain

• Cristina M. Pulido, 
• Gisela Redondo-Sama, 
• Teresa Sordé-Martí, 
• Ramon Flecha

• Published: August 29, 2018
• https://doi.org/10.1371/journal.pone.0203117
• Reader Comments

The social impact of research has usually been analysed through the scientific outcomes produced under the auspices of the research. The growth of scholarly content in social media and the use of altmetrics by researchers to track their work facilitate the advancement in evaluating the impact of research. However, there is a gap in the identification of evidence of the social impact in terms of what citizens are sharing on their social media platforms. This article applies a social impact in social media methodology (SISM) to identify quantitative and qualitative evidence of the potential or real social impact of research shared on social media, specifically on Twitter and Facebook. We define the social impact coverage ratio (SICOR) to identify the percentage of tweets and Facebook posts providing information about potential or actual social impact in relation to the total amount of social media data found related to specific research projects. We selected 10 projects in different fields of knowledge to calculate the SICOR, and the results indicate that 0.43% of the tweets and Facebook posts collected provide linkages with information about social impact. However, our analysis indicates that some projects have a high percentage (4.98%) and others have no evidence of social impact shared in social media. Examples of quantitative and qualitative evidence of social impact are provided to illustrate these results. A general finding is that novel evidences of social impact of research can be found in social media, becoming relevant platforms for scientists to spread quantitative and qualitative evidence of social impact in social media to capture the interest of citizens. Thus, social media users are showed to be intermediaries making visible and assessing evidence of social impact.

Citation: Pulido CM, Redondo-Sama G, Sordé-Martí T, Flecha R (2018) Social impact in social media: A new method to evaluate the social impact of research. PLoS ONE 13(8): e0203117. https://doi.org/10.1371/journal.pone.0203117

Editor: Sergi Lozano, Institut Català de Paleoecologia Humana i Evolució Social (IPHES), SPAIN

Received: November 8, 2017; Accepted: August 15, 2018; Published: August 29, 2018

Copyright: © 2018 Pulido et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The research leading to these results has received funding from the 7th Framework Programme of the European Commission under the Grant Agreement n° 613202 P.I. Ramon Flecha, https://ec.europa.eu/research/fp7/index_en.cfm . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The social impact of research is at the core of some of the debates influencing how scientists develop their studies and how useful results for citizens and societies may be obtained. Concrete strategies to achieve social impact in particular research projects are related to a broader understanding of the role of science in contemporary society. There is a need to explore dialogues between science and society not only to communicate and disseminate science but also to achieve social improvements generated by science. Thus, the social impact of research emerges as an increasing concern within the scientific community [ 1 ]. As Bornmann [ 2 ] said, the assessment of this type of impact is badly needed and is more difficult than the measurement of scientific impact; for this reason, it is urgent to advance in the methodologies and approaches to measuring the social impact of research.

Several authors have approached the conceptualization of social impact, observing a lack of generally accepted conceptual and instrumental frameworks [ 3 ]. It is common to find a wide range of topics included in the contributions about social impact. In their analysis of the policies affecting land use, Hemling et al. [ 4 ] considered various domains in social impact, for instance, agricultural employment or health risk. Moving to the field of flora and fauna, Wilder and Walpole [ 5 ] studied the social impact of conservation projects, focusing on qualitative stories that provided information about changes in attitudes, behaviour, wellbeing and livelihoods. In an extensive study by Godin and Dore [ 6 ], the authors provided an overview and framework for the assessment of the contribution of science to society. They identified indicators of the impact of science, mentioning some of the most relevant weaknesses and developing a typology of impact that includes eleven dimensions, with one of them being the impact on society. The subdimensions of the impact of science on society focus on individuals (wellbeing and quality of life, social implication and practices) and organizations (speeches, interventions and actions). For the authors, social impact “refers to the impact knowledge has on welfare, and on the behaviours, practices and activities of people and groups” (p. 7).

In addition, the terms “social impact” and “societal impact” are sometimes used interchangeably. For instance, Bornmann [ 2 ] said that due to the difficulty of distinguishing social benefits from the superior term of societal benefits, “in much literature the term ‘social impact’ is used instead of ‘societal impact’”(p. 218). However, in other cases, the distinction is made [ 3 ], as in the present research. Similar to the definition used by the European Commission [ 7 ], social impact is used to refer to economic impact, societal impact, environmental impact and, additionally, human rights impact. Therefore, we use the term social impact as the broader concept that includes social improvements in all the above mentioned areas obtained from the transference of research results and representing positive steps towards the fulfilment of those officially defined social goals, including the UN Sustainable Development Goals, the EU 2020 Agenda, or similar official targets. For instance, the Europe 2020 strategy defines five priority targets with concrete indicators (employment, research and development, climate change and energy, education and poverty and social exclusion) [ 8 ], and we consider the targets addressed by objectives defined in the specific call that funds the research project.

This understanding of the social impact of research is connected to the creation of the Social Impact Open Repository (SIOR), which constitutes the first open repository worldwide that displays, cites and stores the social impact of research results [ 9 ]. The SIOR has linked to ORCID and Wikipedia to allow the synergies of spreading information about the social impact of research through diverse channels and audiences. It is relevant to mention that currently, SIOR includes evidence of real social impact, which implies that the research results have led to actual improvements in society. However, it is common to find evidence of potential social impact in research projects. The potential social impact implies that in the development of the research, there has been some evidence of the effectiveness of the research results in terms of social impact, but the results have not yet been transferred.

Additionally, a common confusion is found among the uses of dissemination, transference (policy impact) and social impact. While dissemination means to disseminate the knowledge created by research to citizens, companies and institutions, transference refers to the use of this knowledge by these different actors (or others), and finally, as already mentioned, social impact refers to the actual improvements resulting from the use of this knowledge in relation to the goals motivating the research project (such as the United Nations Sustainable Development Goals). In the present research [ 3 ], it is argued that “social impact can be understood as the culmination of the prior three stages of the research” (p.3). Therefore, this study builds on previous contributions measuring the dissemination and transference of research and goes beyond to propose a novel methodological approach to track social impact evidences.

In fact, the contribution that we develop in this article is based on the creation of a new method to evaluate the evidence of social impact shared in social media. The evaluation proposed is to measure the social impact coverage ratio (SICOR), focusing on the presence of evidence of social impact shared in social media. Then, the article first presents some of the contributions from the literature review focused on the research on social media as a source for obtaining key data for monitoring or evaluating different research purposes. Second, the SISM (social impact through social media) methodology[ 10 ] developed is introduced in detail. This methodology identifies quantitative and qualitative evidence of the social impact of the research shared on social media, specifically on Twitter and Facebook, and defines the SICOR, the social impact coverage ratio. Next, the results are discussed, and lastly, the main conclusions and further steps are presented.

Literature review

Social media research includes the analysis of citizens’ voices on a wide range of topics [ 11 ]. According to quantitative data from April 2017 published by Statista [ 12 ], Twitter and Facebook are included in the top ten leading social networks worldwide, as ranked by the number of active users. Facebook is at the top of the list, with 1,968 million active users, and Twitter ranks 10 th , with 319 million active users. Between them are the following social networks: WhatsApp, YouTube, Facebook Messenger, WeChat, QQ, Instagram,Qzone and Tumblr. If we look at altmetrics, the tracking of social networks for mentions of research outputs includes Facebook, Twitter, Google+,LinkedIn, Sina Weibo and Pinterest. The social networks common to both sources are Facebook and Twitter. These are also popular platforms that have a relevant coverage of scientific content and easy access to data, and therefore, the research projects selected here for application of the SISM methodology were chosen on these platforms.

Chew and Eysenbach [ 13 ] studied the presence of selected keywords in Twitter related to public health issues, particularly during the 2009 H1N1 pandemic, identifying the potential for health authorities to use social media to respond to the concerns and needs of society. Crooks et al.[ 14 ] investigated Twitter activity in the context of a 5.8 magnitude earthquake in 2011 on the East Coast of the United States, concluding that social media content can be useful for event monitoring and can complement other sources of data to improve the understanding of people’s responses to such events. Conversations among young Canadians posted on Facebook and analysed by Martinello and Donelle [ 15 ] revealed housing and transportation as main environmental concerns, and the project FoodRisc examined the role of social media to illustrate consumers’ quick responses during food crisis situations [ 16 ]. These types of contributions illustrate that social media research implies the understanding of citizens’ concerns in different fields, including in relation to science.

Research on the synergies between science and citizens has increased over the years, according to Fresco [ 17 ], and there is a growing interest among researchers and funding agencies in how to facilitate communication channels to spread scientific results. For instance, in 1998, Lubchenco [ 18 ] advocated for a social contract that “represents a commitment on the part of all scientists to devote their energies and talents to the most pressing problems of the day, in proportion to their importance, in exchange for public funding”(p.491).

In this framework, the recent debates on how to increase the impact of research have acquired relevance in all fields of knowledge, and major developments address the methods for measuring it. As highlighted by Feng Xia et al. [ 19 ], social media constitute an emerging approach to evaluating the impact of scholarly publications, and it is relevant to consider the influence of the journal, discipline, publication year and user type. The authors revealed that people’s concerns differ by discipline and observed more interest in papers related to everyday life, biology, and earth and environmental sciences. In the field of biomedical sciences, Haustein et al. [ 20 ] analysed the dissemination of journal articles on Twitter to explore the correlations between tweets and citations and proposed a framework to evaluate social media-based metrics. In fact, different studies address the relationship between the presence of articles on social networks and citations [ 21 ]. Bornmann [ 22 ] conducted a case study using a sample of 1,082 PLOS journal articles recommended in F1000 to explore the usefulness of altmetrics for measuring the broader impact of research. The author presents evidence about Facebook and Twitter as social networks that may indicate which papers in the biomedical sciences can be of interest to broader audiences, not just to specialists in the area. One aspect of particular interest resulting from this contribution is the potential to use altmetrics to measure the broader impacts of research, including the societal impact. However, most of the studies investigating social or societal impact lack a conceptualization underlying its measurement.

To the best of our knowledge, the assessment of social impact in social media (SISM) has developed according to this gap. At the core of this study, we present and discuss the results obtained through the application of the SICOR (social impact coverage ratio) with examples of evidence of social impact shared in social media, particularly on Twitter and Facebook, and the implications for further research.

Following these previous contributions, our research questions were as follows: Is there evidence of social impact of research shared by citizens in social media? If so, is there quantitative or qualitative evidence? How can social media contribute to identifying the social impact of research?

Methods and data presentation

A group of new methodologies related to the analysis of online data has recently emerged. One of these emerging methodologies is social media analytics [ 23 ], which was initially used most in the marketing research field but also came to be used in other domains due to the multiple possibilities opened up by the availability and richness of the data for different research purposes. Likewise, the concern of how to evaluate the social impact of research as well as the development of methodologies for addressing this concern has occupied central attention. The development of SISM (Social Impact in Social Media) and the application of the SICOR (Social Impact Coverage Ratio) is a contribution to advancement in the evaluation of the social impact of research through the analysis of the social media selected (in this case, Twitter and Facebook). Thus, SISM is novel in both social media analytics and among the methodologies used to evaluate the social impact of research. This development has been made under IMPACT-EV, a research project funded under the Framework Program FP7 of the Directorate-General for Research and Innovation of the European Commission. The main difference from other methodologies for measuring the social impact of research is the disentanglement between dissemination and social impact. While altmetrics is aimed at measuring research results disseminated beyond academic and specialized spheres, SISM contribute to advancing this measurement by shedding light on to what extent evidence of the social impact of research is found in social media data. This involves the need to differentiate between tweets or Facebook posts (Fb/posts) used to disseminate research findings from those used to share the social impact of research. We focus on the latter, investigating whether there is evidence of social impact, including both potential and real social impact. In fact, the question is whether research contributes and/or has the potential to contribute to improve the society or living conditions considering one of these goals defined. What is the evidence? Next, we detail the application of the methodology.

Data collection

To develop this study, the first step was to select research projects with social media data to be analysed. The selection of research projects for application of the SISM methodology was performed according to three criteria.

Criteria 1. Selection of success projects in FP7. The projects were success stories of the 7 th Framework Programme (FP7) highlighted by the European Commission [ 24 ] in the fields of knowledge of medicine, public health, biology and genomics. The FP7 published calls for project proposals from 2007 to 2013. This implies that most of the projects funded in the last period of the FP7 (2012 and 2013) are finalized or in the last phase of implementation.

Criteria 2. Period of implementation. We selected projects in the 2012–2013 period because they combine recent research results with higher possibilities of having Twitter and Facebook accounts compared with projects of previous years, as the presence of social accounts in research increased over this period.

Criteria 3. Twitter and Facebook accounts. It was crucial that the selected projects had active Twitter and Facebook accounts.

Table 1 summarizes the criteria and the final number of projects identified. As shown, 10 projects met the defined criteria. Projects in medical research and public health had higher presence.

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https://doi.org/10.1371/journal.pone.0203117.t001

After the selection of projects, we defined the timeframe of social media data extraction on Twitter and Facebook from the starting date of the project until the day of the search, as presented in Table 2 .

https://doi.org/10.1371/journal.pone.0203117.t002

The second step was to define the search strategies for extracting social media data related to the research projects selected. In this line, we defined three search strategies.

Strategy 1. To extract messages published on the Twitter account and the Facebook page of the selected projects. We listed the Twitter accounts and Facebook pages related to each project in order to look at the available information. In this case, it is important to clarify that the tweets published under the corresponding Twitter project account are original tweets or retweets made from this account. It is relevant to mention that in one case, the Twitter account and Facebook page were linked to the website of the research group leading the project. In this case, we selected tweets and Facebook posts related to the project. For instance, in the case of the Twitter account, the research group created a specific hashtag to publish messages related to the project; therefore, we selected only the tweets published under this hashtag. In the analysis, we prioritized the analysis of the tweets and Facebook posts that received some type of interaction (likes, retweets or shares) because such interaction is a proxy for citizens’ interest. In doing so, we used the R program and NVivoto extract the data and proceed with the analysis. Once we obtained the data from Twitter and Facebook, we were able to have an overview of the information to be further analysed, as shown in Table 3 .

https://doi.org/10.1371/journal.pone.0203117.t003

We focused the second and third strategies on Twitter data. In both strategies, we extracted Twitter data directly from the Twitter Advanced Search tool, as the API connected to NVivo and the R program covers only a specific period of time limited to 7/9 days. Therefore, the use of the Twitter Advanced Search tool made it possible to obtain historic data without a period limitation. We downloaded the results in PDF and then uploaded them to NVivo.

Strategy 2. To use the project acronym combined with other keywords, such as FP7 or EU. This strategy made it possible to obtain tweets mentioning the project. Table 4 presents the number of tweets obtained with this strategy.

https://doi.org/10.1371/journal.pone.0203117.t004

Strategy 3. To use searchable research results of projects to obtain Twitter data. We defined a list of research results, one for each project, and converted them into keywords. We selected one searchable keyword for each project from its website or other relevant sources, for instance, the brief presentations prepared by the European Commission and published in CORDIS. Once we had the searchable research results, we used the Twitter Advanced Search tool to obtain tweets, as presented in Table 5 .

https://doi.org/10.1371/journal.pone.0203117.t005

The sum of the data obtained from these three strategies allowed us to obtain a total of 3,425 tweets and 1,925 posts on public Facebook pages. Table 6 presents a summary of the results.

https://doi.org/10.1371/journal.pone.0203117.t006

We imported the data obtained from the three search strategies into NVivo to analyse. Next, we select tweets and Facebook posts providing linkages with quantitative or qualitative evidence of social impact, and we complied with the terms of service for the social media from which the data were collected. By quantitative and qualitative evidence, we mean data or information that shows how the implementation of research results has led to improvements towards the fulfilment of the objectives defined in the EU2020 strategy of the European Commission or other official targets. For instance, in the case of quantitative evidence, we searched tweets and Facebook posts providing linkages with quantitative information about improvements obtained through the implementation of the research results of the project. In relation to qualitative evidence, for example, we searched for testimonies that show a positive evaluation of the improvement due to the implementation of research results. In relation to this step, it is important to highlight that social media users are intermediaries making visible evidence of social impact. Users often share evidence, sometimes sharing a link to an external resource (e.g., a video, an official report, a scientific article, news published on media). We identified evidence of social impact in these sources.

Data analysis

γ i is the total number of messages obtained about project i with evidence of social impact on social media platforms (Twitter, Facebook, Instagram, etc.);

T i is the total number of messages from project i on social media platforms (Twitter, Facebook, Instagram, etc.); and

n is the number of projects selected.

Analytical categories and codebook

The researchers who carried out the analysis of the social media data collected are specialists in the social impact of research and research on social media. Before conducting the full analysis, two aspects were guaranteed. First, how to identify evidence of social impact relating to the targets defined by the EU2020 strategy or to specific goals defined by the call addressed was clarified. Second, we held a pilot to test the methodology with one research project that we know has led to considerable social impact, which allowed us to clarify whether or not it was possible to detect evidence of social impact shared in social media. Once the pilot showed positive results, the next step was to extend the analysis to another set of projects and finally to the whole sample. The construction of the analytical categories was defined a priori, revised accordingly and lastly applied to the full sample.

Different observations should be made. First, in this previous analysis, we found that the tweets and Facebook users play a key role as “intermediaries,” serving as bridges between the larger public and the evidence of social impact. Social media users usually share a quote or paragraph introducing evidence of social impact and/or link to an external resource, for instance, a video, official report, scientific article, news story published on media, etc., where evidence of the social impact is available. This fact has implications for our study, as our unit of analysis is all the information included in the tweets or Facebook posts. This means that our analysis reaches the external resources linked to find evidence of social impact, and for this reason, we defined tweets or Facebook posts providing linkages with information about social impact.

Second, the other important aspect is the analysis of the users’ profile descriptions, which requires much more development in future research given the existing limitations. For instance, some profiles are users’ restricted due to privacy reasons, so the information is not available; other accounts have only the name of the user with no description of their profile available. Therefore, we gave priority to the identification of evidence of social impact including whether a post obtained interaction (retweets, likes or shares) or was published on accounts other than that of the research project itself. In the case of the profile analysis, we added only an exploratory preliminary result because this requires further development. Considering all these previous details, the codebook (see Table 7 ) that we present as follows is a result of this previous research.

https://doi.org/10.1371/journal.pone.0203117.t007

How to analyse Twitter and Facebook data

To illustrate how we analysed data from Twitter and Facebook, we provide one example of each type of evidence of social impact defined, considering both real and potential social impact, with the type of interaction obtained and the profiles of those who have interacted.

QUANESISM. Tweet by ZeroHunger Challenge @ZeroHunger published on 3 May 2016. Text: How re-using food waste for animal feed cuts carbon emissions.-NOSHAN project hubs.ly/H02SmrP0. 7 retweets and 5 likes.

The unit of analysis is all the content of the tweet, including the external link. If we limited our analysis to the tweet itself, it would not be evidence. Examining the external link is necessary to find whether there is evidence of social impact. The aim of this project was to investigate the process and technologies needed to use food waste for feed production at low cost, with low energy consumption and with a maximal evaluation of the starting wastes. This tweet provides a link to news published in the PHYS.org portal [ 25 ], which specializes in science news. The news story includes an interview with the main researcher that provides the following quotation with quantitative evidence:

'Our results demonstrated that with a NOSHAN 10 percent mix diet, for every kilogram of broiler chicken feed, carbon dioxide emissions were reduced by 0.3 kg compared to a non-food waste diet,' explains Montse Jorba, NOSHAN project coordinator. 'If 1 percent of total chicken broiler feed in Europe was switched to the 10 percent NOSHAN mix diet, the total amount of CO2 emissions avoided would be 0.62 million tons each year.'[ 25 ]

This quantitative evidence “a NOSHAN 10 percent mix diet, for every kilogram of broiler chicken feed, carbon dioxide emissions carbon dioxide emissions were reduced by 0.3 kg to a non-food waste diet” is linked directly with the Europe 2020 target of Climate Change & Energy, specifically with the target of reducing greenhouse gas emissions by 20% compared to the levels in 1990 [ 8 ]. The illustrative extrapolation the coordinator mentioned in the news is also an example of quantitative evidence, although is an extrapolation based on the specific research result.

This tweet was captured by the Acronym search strategy. It is a message tweeted by an account that is not related to the research project. The twitter account is that of the Zero Hunger Challenge movement, which supports the goals of the UN. The interaction obtained is 7 retweets and 5 likes. Regarding the profiles of those who retweeted and clicked “like”, there were activists, a journalist, an eco-friendly citizen, a global news service, restricted profiles (no information is available on those who have retweeted) and one account with no information in its profile.

The following example illustrates the analysis of QUALESISM: Tweet by @eurofitFP7 published on4 October 2016. Text: See our great new EuroFIT video on youtube! https://t.co/TocQwMiW3c 9 retweets and 5 likes.

The aim of this project is to improve health through the implementation of two novel technologies to achieve a healthier lifestyle. The tweet provides a link to a video on YouTube on the project’s results. In this video, we found qualitative evidence from people who tested the EuroFit programme; there are quotes from men who said that they have experienced improved health results using this method and that they are more aware of how to manage their health:

One end-user said: I have really amazing results from the start, because I managed to change a lot of things in my life. And other one: I was more conscious of what I ate, I was more conscious of taking more steps throughout the day and also standing up a little more. [ 26 ]

The research applies the well researched scientific evidence to the management of health issues in daily life. The video presents the research but also includes a section where end-users talk about the health improvements they experienced. The quotes extracted are some examples of the testimonies collected. All agree that they have improved their health and learned healthy habits for their daily lives. These are examples of qualitative evidence linked with the target of the call HEALTH.2013.3.3–1—Social innovation for health promotion [ 27 ] that has the objectives of reducing sedentary habits in the population and promoting healthy habits. This research contributes to this target, as we see in the video testimonies. Regarding the interaction obtained, this tweet achieved 9 retweets and 5 likes. In this case, the profiles of the interacting citizens show involvement in sport issues, including sport trainers, sport enthusiasts and some researchers.

To summarize the analysis, in Table 8 below, we provide a summary with examples illustrating the evidence found.

https://doi.org/10.1371/journal.pone.0203117.t008

Quantitative evidence of social impact in social media

There is a greater presence of tweets/Fb posts with quantitative evidence (14) than with qualitative evidence (9) in the total number of tweets/Fb posts identified with evidence of social impact. Most of the tweets/Fb posts with quantitative evidence of social impact are from scientific articles published in peer-reviewed international journals and show potential social impact. In Table 8 , we introduce 3 examples of this type of tweets/Fb posts with quantitative evidence:

The first tweet with quantitative social impact selected is from project 7. The aim of this project was to provide high-quality scientific evidence for preventing vitamin D deficiency in European citizens. The tweet highlighted the main contribution of the published study, that is, “Weekly consumption of 7 vitamin D-enhanced eggs has an important impact on winter vitamin D status in adults” [ 28 ]. The quantitative evidence shared in social media was extracted from a news publication in a blog on health news. This blog collects scientific articles of research results. In this case, the blog disseminated the research result focused on how vitamin D-enhanced eggs improve vitamin D deficiency in wintertime, with the published results obtained by the research team of the project selected. The quantitative evidence illustrates that the group of adults who consumed vitamin D-enhanced eggs did not suffer from vitamin D deficiency, as opposed to the control group, which showed a significant decrease in vitamin D over the winter. The specific evidence is the following extracted from the article [ 28 ]:

With the use of a within-group analysis, it was shown that, although serum 25(OH) D in the control group significantly decreased over winter (mean ± SD: -6.4 ± 6.7 nmol/L; P = 0.001), there was no change in the 2 groups who consumed vitamin D-enhanced eggs (P>0.1 for both. (p. 629)

This evidence contributes to achievement of the target defined in the call addressed that is KBBE.2013.2.2–03—Food-based solutions for the eradication of vitamin D deficiency and health promotion throughout the life cycle [ 29 ]. The quantitative evidence shows how the consumption of vitamin D-enhanced eggs reduces vitamin D deficiency.

The second example of this table corresponds to the example of quantitative evidence of social impact provided in the previous section.

The third example is a Facebook post from project 3 that is also tweeted. Therefore, this evidence was published in both social media sources analysed. The aim of this project was to measure a range of chemical and physical environmental hazards in food, consumer products, water, air, noise, and the built environment in the pre- and postnatal early-life periods. This Facebook post and tweet links directly to a scientific article [ 30 ] that shows the precision of the spectroscopic platform:

Using 1H NMR spectroscopy we characterized short-term variability in urinary metabolites measured from 20 children aged 8–9 years old. Daily spot morning, night-time and pooled (50:50 morning and night-time) urine samples across six days (18 samples per child) were analysed, and 44 metabolites quantified. Intraclass correlation coefficients (ICC) and mixed effect models were applied to assess the reproducibility and biological variance of metabolic phenotypes. Excellent analytical reproducibility and precision was demonstrated for the 1H NMR spectroscopic platform (median CV 7.2%) . (p.1)

This evidence is linked to the target defined in the call “ENV.2012.6.4–3—Integrating environmental and health data to advance knowledge of the role of environment in human health and well-being in support of a European exposome initiative” [ 31 ]. The evidence provided shows how the project’s results have contributed to building technology for improving the data collection to advance in the knowledge of the role of the environment in human health, especially in early life. The interaction obtained is one retweet from a citizen from Nigeria interested in health issues, according to the information available in his profile.

Qualitative evidence of social impact in social media

We found qualitative evidence of the social impact of different projects, as shown in Table 9 . Similarly to the quantitative evidence, the qualitative cases also demonstrate potential social impact. The three examples provided have in common that they are tweets or Facebook posts that link to videos where the end users of the research project explain their improvements once they have implemented the research results.

https://doi.org/10.1371/journal.pone.0203117.t009

The first tweet with qualitative evidence selected is from project 4. The aim of this project is to produce a system that helps in the prevention of obesity and eating disorders, targeting young people and adults [ 32 ]. The twitter account that published this tweet is that of the Future and Emerging Technologies Programme of the European Commission, and a link to a Euronews video is provided. This video shows how the patients using the technology developed in the research achieved control of their eating disorders, through the testimonies of patients commenting on the positive results they have obtained. These testimonies are included in the news article that complements the video. An example of these testimonies is as follows:

Pierre Vial has lost 43 kilos over the past nine and a half months. He and other patients at the eating disorder clinic explain the effects obesity and anorexia have had on their lives. Another patient, Karin Borell, still has some months to go at the clinic but, after decades of battling anorexia, is beginning to be able to visualise life without the illness: “On a good day I see myself living a normal life without an eating disorder, without problems with food. That’s really all I wish right now”.[ 32 ]

This qualitative evidence shows how the research results contribute to the achievement of the target goals of the call addressed:“ICT-2013.5.1—Personalised health, active ageing, and independent living”. [ 33 ] In this case, the results are robust, particularly for people suffering chronic diseases and desiring to improve their health; people who have applied the research findings are improving their eating disorders and better managing their health. The value of this evidence is the inclusion of the patients’ voices stating the impact of the research results on their health.

The second example is a Facebook post from project 9, which provides a link to a Euronews video. The aim of this project is to bring some tools from the lab to the farm in order to guarantee a better management of the farm and animal welfare. In this video [ 34 ], there are quotes from farmers using the new system developed through the research results of the project. These quotes show how use of the new system is improving the management of the farm and the health of the animals; some examples are provided:

Cameras and microphones help me detect in real time when the animals are stressed for whatever reason,” explained farmer Twan Colberts. “So I can find solutions faster and in more efficient ways, without me being constantly here, checking each animal.”

This evidence shows how the research results contribute to addressing the objectives specified in the call “KBBE.2012.1.1–02—Animal and farm-centric approach to precision livestock farming in Europe” [ 29 ], particularly, to improve the precision of livestock farming in Europe. The interaction obtained is composed of6 likes and 1 share. The profiles are diverse, but some of them do not disclose personal information; others have not added a profile description, and only their name and photo are available.

Interrater reliability (kappa)

The analysis of tweets and Facebook posts providing linkages with information about social impact was conducted following a content analysis method in which reliability was based on a peer review process. This sample is composed of 3,425 tweets and 1,925 Fb/posts. Each tweet and Facebook post was analysed to identify whether or not it contains evidence of social impact. Each researcher has the codebook a priori. We used interrater reliability in examining the agreement between the two raters on the assignment of the categories defined through Cohen’s kappa. We used SPSS to calculate this coefficient. We exported an excel sheet with the sample coded by the two researchers being 1 (is evidence of social impact, either potential or real) and 0 (is not evidence of social impact) to SPSS. The cases where agreement was not achieved were not considered as containing evidence of social impact. The result obtained is 0.979; considering the interpretation of this number according to Landis & Koch [ 35 ], our level of agreement is almost perfect, and thus, our analysis is reliable. To sum up the data analysis, the description of the steps followed is explained:

Step 1. Data analysis I. We included all data collected in an excel sheet to proceed with the analysis. Prior to the analysis, researchers read the codebook to keep in mind the information that should be identified.

Step 2. Each researcher involved reviewed case by case the tweets and Facebook posts to identify whether they provide links with evidence of social impact or not. If the researcher considers there to be evidence of social impact, he or she introduces the value of 1into the column, and if not, the value of 0.

Step 3. Once all the researchers have finished this step, the next step is to export the excel sheet to SPSS to extract the kappa coefficient.

Step 4. Data Analysis II. The following step was to analyse case by case the tweets and Facebook posts identified as providing linkages with information of social impact and classify them as quantitative or qualitative evidence of social impact.

Step 5. The interaction received was analysed because this determines to which extent this evidence of social impact has captured the attention of citizens (in the form of how many likes, shares, or retweets the post has).

Step 6. Finally, if available, the profile descriptions of the citizens interacting through retweeting or sharing the Facebook post were considered.

Step 7. SICOR was calculated. It could be applied to the complete sample (all data projects) or to each project, as we will see in the next section.

The total number of tweets and Fb/posts collected from the 10 projects is 5,350. After the content analysis, we identified 23 tweets and Facebook posts providing linkages to information about social impact. To respond to the research question, which considered whether there is evidence of social impact shared by citizens in social media, the answer was affirmative, although the coverage ratio is low. Both Twitter and Facebook users retweeted or shared evidence of social impact, and therefore, these two social media networks are valid sources for expanding knowledge on the assessment of social impact. Table 10 shows the social impact coverage ratio in relation to the total number of messages analysed.

https://doi.org/10.1371/journal.pone.0203117.t010

The analysis of each of the projects selected revealed some results to consider. Of the 10 projects, 7 had evidence, but those projects did not necessarily have more Tweets and Facebook posts. In fact, some projects with fewer than 70 tweets and 50 Facebook posts have more evidence of social impact than other projects with more than 400 tweets and 400 Facebook posts. This result indicates that the number of tweets and Facebook posts does not determine the existence of evidence of social impact in social media. For example, project 2 has 403 tweets and 423 Facebooks posts, but it has no evidence of social impact on social media. In contrast, project 9 has 62 tweets, 43 Facebook posts, and 2 pieces of evidence of social impact in social media, as shown in Table 11 .

https://doi.org/10.1371/journal.pone.0203117.t011

The ratio of tweets/Fb posts to evidence is 0.43%, and it differs depending on the project, as shown below in Table 12 . There is one project (P7) with a ratio of 4.98%, which is a social impact coverage ratio higher than that of the other projects. Next, a group of projects (P3, P9, P10) has a social impact coverage ratio between 1.41% and 2,99%.The next slot has three projects (P1, P4, P5), with a ratio between 0.13% and 0.46%. Finally, there are three projects (P2, P6, P8) without any tweets/Fb posts evidence of social impact.

https://doi.org/10.1371/journal.pone.0203117.t012

Considering the three strategies for obtaining data, each is related differently to the evidence of social impact. In terms of the social impact coverage ratio, as shown in Table 13 , the most successful strategy is number 3 (searchable research results), as it has a relation of 17.86%, which is much higher than the ratios for the other 2 strategies. The second strategy (acronym search) is more effective than the first (profile accounts),with 1.77% for the former as opposed to 0.27% for the latter.

https://doi.org/10.1371/journal.pone.0203117.t013

Once tweets and Facebook posts providing linkages with information about social impact(ESISM)were identified, we classified them in terms of quantitative (QUANESISM) or qualitative evidence (QUALESISM)to determine which type of evidence was shared in social media. Table 14 indicates the amount of quantitative and qualitative evidence identified for each search strategy.

https://doi.org/10.1371/journal.pone.0203117.t014

First, the results obtained indicated that the SISM methodology aids in calculating the social impact coverage ratio of the research projects selected and evaluating whether the social impact of the corresponding research is shared by citizens in social media. The social impact coverage ratio applied to the sample selected is low, but when we analyse the SICOR of each project separately, we can observe that some projects have a higher social impact coverage ratio than others. Complementary to altmetrics measuring the extent to which research results reach out society, the SICOR considers the question whether this process includes evidence of potential or real social impact. In this sense, the overall methodology of SISM contributes to advancement in the evaluation of the social impact of research by providing a more precise approach to what we are evaluating.

This contribution complements current evaluation methodologies of social impact that consider which improvements are shared by citizens in social media. Exploring the results in more depth, it is relevant to highlight that of the ten projects selected, there is one research project with a social impact coverage ratio higher than those of the others, which include projects without any tweets or Facebook posts with evidence of social impact. This project has a higher ratio of evidence than the others because evidence of its social impact is shared more than is that of other projects. This also means that the researchers produced evidence of social impact and shared it during the project. Another relevant result is that the quantity of tweets and Fb/posts collected did not determine the number of tweets and Fb/posts found with evidence of social impact. Moreover, the analysis of the research projects selected showed that there are projects with less social media interaction but with more tweets and Fb/posts containing evidence of social media impact. Thus, the number of tweets and Fb/posts with evidence of social impact is not determined by the number of publication messages collected; it is determined by the type of messages published and shared, that is, whether they contain evidence of social impact or not.

The second main finding is related to the effectiveness of the search strategies defined. Related to the strategies carried out under this methodology, one of the results found is that the most effective search strategy is the searchable research results, which reveals a higher percentage of evidence of social impact than the own account and acronym search strategies. However, the use of these three search strategies is highly recommended because the combination of all of them makes it possible to identify more tweets and Facebook posts with evidence of social impact.

Another result is related to the type of evidence of social impact found. There is both quantitative and qualitative evidence. Both types are useful for understanding the type of social impact achieved by the corresponding research project. In this sense, quantitative evidence allows us to understand the improvements obtained by the implementation of the research results and capture their impact. In contrast, qualitative evidence allows us to deeply understand how the resultant improvements obtained from the implementation of the research results are evaluated by the end users by capturing their corresponding direct quotes. The social impact includes the identification of both real and potential social impact.

Conclusions

After discussing the main results obtained, we conclude with the following points. Our study indicates that there is incipient evidence of social impact, both potential and real, in social media. This demonstrates that researchers from different fields, in the present case involved in medical research, public health, animal welfare and genomics, are sharing the improvements generated by their research and opening up new venues for citizens to interact with their work. This would imply that scientists are promoting not only the dissemination of their research results but also the evidence on how their results may lead to the improvement of societies. Considering the increasing relevance and presence of the dissemination of research, the results indicate that scientists still need to include in their dissemination and communication strategies the aim of sharing the social impact of their results. This implies the publication of concrete qualitative or quantitative evidence of the social impact obtained. Because of the inclusion of this strategy, citizens will pay more attention to the content published in social media because they are interested in knowing how science can contribute to improving their living conditions and in accessing crucial information. Sharing social impact in social media facilitates access to citizens of different ages, genders, cultural backgrounds and education levels. However, what is most relevant for our argument here is how citizens should also be able to participate in the evaluation of the social impact of research, with social media a great source to reinforce this democratization process. This contributes not only to greatly improving the social impact assessment, as in addition to experts, policy makers and scientific publications, citizens through social media contribute to making this assessment much more accurate. Thus, citizens’ contribution to the dissemination of evidence of the social impact of research yields access to more diverse sectors of society and information that might be unknown by the research or political community. Two future steps are opened here. On the one hand, it is necessary to further examine the profiles of users who interact with this evidence of social impact considering the limitations of the privacy and availability of profile information. A second future task is to advance in the articulation of the role played by citizens’ participation in social impact assessment, as citizens can contribute to current worldwide efforts by shedding new light on this process of social impact assessment and contributing to making science more relevant and useful for the most urgent and poignant social needs.

Supporting information

S1 file. interrater reliability (kappa) result..

This file contains the SPSS file with the result of the calculation of Cohen’s Kappa regards the interrater reliability. The word document exported with the obtained result is also included.

https://doi.org/10.1371/journal.pone.0203117.s001

S2 File. Data collected and SICOR calculation.

This excel contains four sheets, the first one titled “data collected” contains the number of tweets and Facebook posts collected through the three defined search strategies; the second sheet titled “sample” contains the sample classified by project indicating the ID of the message or code assigned, the type of message (tweet or Facebook post) and the codification done by researchers being 1 (is evidence of social impact, either potential or real) and 0 (is not evidence of social impact); the third sheet titled “evidence found” contains the number of type of evidences of social impact founded by project (ESISM-QUANESIM or ESISM-QUALESIM), search strategy and type of message (tweet or Facebook posts); and the last sheet titled “SICOR” contains the Social Impact Coverage Ratio calculation by projects in one table and type of search strategy done in another one.

https://doi.org/10.1371/journal.pone.0203117.s002

Acknowledgments

The research leading to these results received funding from the 7 th Framework Programme of the European Commission under Grant Agreement n° 613202. The extraction of available data using the list of searchable keywords on Twitter and Facebook followed the ethical guidelines for social media research supported by the Economic and Social Research Council (UK) [ 36 ] and the University of Aberdeen [ 37 ]. Furthermore, the research results have already been published and made public, and hence, there are no ethical issues.

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Analyzing social media data: A mixed-methods framework combining computational and qualitative text analysis

• Published: 02 April 2019
• Volume 51 , pages 1766–1781, ( 2019 )

Cite this article

• Matthew Andreotta 1 , 2 ,
• Robertus Nugroho 2 , 3 ,
• Mark J. Hurlstone 1 ,
• Fabio Boschetti 4 ,
• Simon Farrell 1 ,
• Iain Walker 5 &
• Cecile Paris 2  

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To qualitative researchers, social media offers a novel opportunity to harvest a massive and diverse range of content without the need for intrusive or intensive data collection procedures. However, performing a qualitative analysis across a massive social media data set is cumbersome and impractical. Instead, researchers often extract a subset of content to analyze, but a framework to facilitate this process is currently lacking. We present a four-phased framework for improving this extraction process, which blends the capacities of data science techniques to compress large data sets into smaller spaces, with the capabilities of qualitative analysis to address research questions. We demonstrate this framework by investigating the topics of Australian Twitter commentary on climate change, using quantitative (non-negative matrix inter-joint factorization; topic alignment) and qualitative (thematic analysis) techniques. Our approach is useful for researchers seeking to perform qualitative analyses of social media, or researchers wanting to supplement their quantitative work with a qualitative analysis of broader social context and meaning.

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Introduction

Social scientists use qualitative modes of inquiry to explore the detailed descriptions of the world that people see and experience (Pistrang & Barker, 2012 ). To collect the voices of people, researchers can elicit textual descriptions of the world through interview or survey methodologies. However, with the popularity of the Internet and social media technologies, new avenues for data collection are possible. Social media platforms allow users to create content (e.g., Weinberg & Pehlivan, 2011 ), and interact with other users (e.g., Correa, Hinsley, & de Zùñiga, 2011 ; Kietzmann, Hermkens, McCarthy, & Silvestre, 2010 ), in settings where “Anyone can say Anything about Any topic” ( AAA slogan , Allemang & Hendler, 2011 , pg. 6). Combined with the high rate of content production, social media platforms can offer researchers massive and diverse dynamic data sets (Yin & Kaynak, 2015 ; Gudivada et al., 2015 ). With technologies increasingly capable of harvesting, storing, processing, and analyzing this data, researchers can now explore data sets that would be infeasible to collect through more traditional qualitative methods.

Many social media platforms can be considered as textual corpora, willingly and spontaneously authored by millions of users. Researchers can compile a corpus using automated tools and conduct qualitative inquiries of content or focused analyses on specific users (Marwick, 2014 ). In this paper, we outline some of the opportunities and challenges of applying qualitative textual analyses to the big data of social media. Specifically, we present a conceptual and pragmatic justification for combining qualitative textual analyses with data science text-mining tools. This process allows us to both embrace and cope with the volume and diversity of commentary over social media. We then demonstrate this approach in a case study investigating Australian commentary on climate change, using content from the social media platform: Twitter.

Opportunities and challenges for qualitative researchers using social media data

Through social media, qualitative researchers gain access to a massive and diverse range of individuals, and the content they generate. Researchers can identify voices which may not be otherwise heard through more traditional approaches, such as semi-structured interviews and Internet surveys with open-ended questions. This can be done through diagnostic queries to capture the activity of specific peoples, places, events, times, or topics. Diagnostic queries may specify geotagged content, the time of content creation, textual content of user activity, and the online profile of users. For example, Freelon et al., ( 2018 ) identified the Twitter activity of three separate communities (‘Black Twitter’, ‘Asian-American Twitter’, ‘Feminist Twitter’) through the use of hashtags Footnote 1 in tweets from 2015 to 2016. A similar process can be used to capture specific events or moments (Procter et al., 2013 ; Denef et al., 2013 ), places (Lewis et al., 2013 ), and specific topics (Hoppe, 2009 ; Sharma et al., 2017 ).

Collecting social media data may be more scalable than traditional approaches. Once equipped with the resources to access and process data, researchers can potentially scale data harvesting without expending a great deal of resources. This differs from interviews and surveys, where collecting data can require an effortful and time-consuming contribution from participants and researchers.

Social media analyses may also be more ecologically valid than traditional approaches. Unlike approaches where responses from participants are elicited in artificial social contexts (e.g., Internet surveys, laboratory-based interviews), social media data emerges from real-world social environments encompassing a large and diverse range of people, without any prompting from researchers. Thus, in comparison with traditional methodologies (Onwuegbuzie and Leech, 2007 ; Lietz & Zayas, 2010 ; McKechnie, 2008 ), participant behavior is relatively unconstrained if not entirely unconstrained, by the behaviors of researchers.

These opportunities also come up with challenges, because of the following attributes (Parker et al., 2011 ). Firstly, social media can be interactive : its content involves the interactions of users with other users (e.g., conversations), or even external websites (e.g., links to news websites). The ill-defined boundaries of user interaction have implications for determining the units of analysis of qualitative study. For example, conversations can be lengthy, with multiple users, without a clear structure or end-point. Interactivity thus blurs the boundaries between users, their content, and external content (Herring, 2009 ; Parker et al., 2011 ). Secondly, content can be ephemeral and dynamic . The users and content of their postings are transient (Parker et al., 2011 ; Boyd & Crawford, 2012 ; Weinberg & Pehlivan, 2011 ). This feature arises from the diversity of users, the dynamic socio-cultural context surrounding platform use, and the freedom users have to create, distribute, display, and dispose of their content (Marwick & Boyd, 2011 ). Lastly, social media content is massive in volume . The accumulated postings of users can lead to a large amount of data, and due to the diverse and dynamic content, postings may be largely unrelated and accumulate over a short period of time. Researchers hoping to harness the opportunities of social media data sets must therefore develop strategies for coping with these challenges.

A framework integrating computational and qualitative text analyses

Our framework—a mixed-method approach blending the capabilities of data science techniques with the capacities of qualitative analysis—is shown in Fig.  1 . We overcome the challenges of social media data by automating some aspects of the data collection and consolidation, so that the qualitative researcher is left with a manageable volume of data to synthesize and interpret. Broadly, our framework consists of the following four phases: (1) harvest social media data and compile a corpus, (2) use data science techniques to compress the corpus along a dimension of relevance, (3) extract a subset of data from the most relevant spaces of the corpus, and (4) perform a qualitative analysis on this subset of data.

Schematic overview of the four-phased framework

Phase 1: Harvest social media data and compile a corpus

Researchers can use automated tools to query records of social media data, extract this data, and compile it into a corpus. Researchers may query for content posted in a particular time frame (Procter et al., 2013 ), content containing specified terms (Sharma et al., 2017 ), content posted by users meeting particular characteristics (Denef et al., 2013 ; Lewis et al., 2013 ), and content pertaining to a specified location (Hoppe, 2009 ).

Phase 2: Use data science techniques to compress the corpus along a dimension of relevance

Although researchers may be interested in examining the entire data set, it is often more practical to focus on a subsample of data (McKenna et al., 2017 ). Specifically, we advocate dividing the corpus along a dimension of relevance, and sampling from spaces that are more likely to be useful for addressing the research questions under consideration. By relevance, we refer to an attribute of content that is both useful for addressing the research questions and usable for the planned qualitative analysis.

To organize the corpus along a dimension of relevance , researchers can use automated, computational algorithms. This process provides both formal and informal advantages for the subsequent qualitative analysis. Formally, algorithms can assist researchers in privileging an aspect of the corpus most relevant for the current inquiry. For example, topic modeling clusters massive content into semantic topics—a process that would be infeasible using human coders alone. A plethora of techniques exist for separating social media corpora on the basis of useful aspects, such as sentiment (e.g., Agarwal, Xie, Vovsha, Rambow, & Passonneau, 2010 ; Paris, Christensen, Batterham, & O’Dea, 2015 ; Pak & Paroubek, 2011 ) and influence (Weng et al., 2010 ).

Algorithms also produce an informal advantage for qualitative analysis. As mentioned, it is often infeasible for analysts to explore large data sets using qualitative techniques. Computational models of content can allow researchers to consider meaning at a corpus-level when interpreting individual datum or relationships between a subset of data. For example, in an inspection of 2.6 million tweets, Procter et al., ( 2013 ) used the output of an information flow analysis to derive rudimentary codes for inspecting individual tweets. Thus, algorithmic output can form a meaningful scaffold for qualitative analysis by providing analysts with summaries of potentially disjunct and multifaceted data (due to interactive, ephemeral, dynamic attributes of social media).

Phase 3: Extract a subset of data from the most relevant spaces of the corpus

Once the corpus is organized on the basis of relevance, researchers can extract data most relevant for answering their research questions. Researchers can extract a manageable amount of content to qualitatively analyze. For example, if the most relevant space of the corpus is too large for qualitative analysis, the researcher may choose to randomly sample from that space. If the most relevant space is small, the researcher may revisit Phase 2 and adopt a more lenient criteria of relevance.

Phase 4: Perform a qualitative analysis on this subset of data

The final phase involves performing the qualitative analysis to address the research question. As discussed above, researchers may draw on the computational models as a preliminary guide to the data.

Contextualizing the framework within previous qualitative social media studies

The proposed framework generalizes a number of previous approaches (Collins and Nerlich, 2015 ; McKenna et al., 2017 ) and individual studies (e.g., Lewis et al., 2013 ; Newman, 2016 ), in particular that of Marwick ( 2014 ). In Marwick’s general description of qualitative analysis of social media textual corpora, researchers: (1) harvest and compile a corpus, (2) extract a subset of the corpus, and (3) perform a qualitative analysis on the subset. As shown in Fig.  1 , our framework differs in that we introduce formal considerations of relevance, and the use of quantitative techniques to inform the extraction of a subset of data. Although researchers sometimes identify a subset of data most relevant to answering their research question, they seldom deploy data science techniques to identify it. Instead, researchers typically depend on more crude measures to isolate relevant data. For example, researchers have used the number of repostings of user content to quantify influence and recognition (e.g., Newman, 2016 ).

The steps in the framework may not be obvious without a concrete example. Next, we demonstrate our framework by applying it to Australian commentary regarding climate change on Twitter.

Application Example: Australian Commentary regarding Climate Change on Twitter

Social media platform of interest.

We chose to explore user commentary of climate change over Twitter. Twitter activity contains information about: the textual content generated by users (i.e., content of tweets), interactions between users, and the time of content creation (Veltri and Atanasova, 2017 ). This allows us to examine the content of user communication, taking into account the temporal and social contexts of their behavior. Twitter data is relatively easy for researchers to access. Many tweets reside within a public domain, and are accessible through free and accessible APIs.

The characteristics of Twitter’s platform are also favorable for data analysis. An established literature describes computational techniques and considerations for interpreting Twitter data. We used the approaches and findings from other empirical investigations to inform our approach. For example, we drew on past literature to inform the process of identifying which tweets were related to climate change.

Public discussion on climate change

Climate change is one of the greatest challenges facing humanity (Schneider, 2011 ). Steps to prevent and mitigate the damaging consequences of climate change require changes on different political, societal, and individual levels (Lorenzoni & Pidgeon, 2006 ). Insights into public commentary can inform decision making and communication of climate policy and science.

Traditionally, public perceptions are investigated through survey designs and qualitative work (Lorenzoni & Pidgeon, 2006 ). Inquiries into social media allow researchers to explore a large and diverse range of climate change-related dialogue (Auer et al., 2014 ). Yet, existing inquiries of Twitter activity are few in number and typically constrained to specific events related to climate change, such as the release of the Fifth Assessment Report by the Intergovernmental Panel on Climate Change (Newman et al., 2010 ; O’Neill et al., 2015 ; Pearce, 2014 ) and the 2015 United Nations Climate Change Conference, held in Paris (Pathak et al., 2017 ).

When longer time scales are explored, most researchers rely heavily upon computational methods to derive topics of commentary. For example, Kirilenko and Stepchenkova ( 2014 ) examined the topics of climate change tweets posted in 2012, as indicated by the most prevalent hashtags. Although hashtags can mark the topics of tweets, it is a crude measure as tweets with no hashtags are omitted from analysis, and not all topics are indicated via hashtags (e.g., Nugroho, Yang, Zhao, Paris, & Nepal, 2017 ). In a more sophisticated approach, Veltri and Atanasova ( 2017 ) examined the co-occurrence of terms using hierarchical clustering techniques to map the semantic space of climate change tweet content from the year 2013. They identified four themes: (1) “calls for action and increasing awareness”, (2) “discussions about the consequences of climate change”, (3) “policy debate about climate change and energy”, and (4) “local events associated with climate change” (p. 729).

Our research builds on the existing literature in two ways. Firstly, we explore a new data set—Australian tweets over the year 2016. Secondly, in comparison to existing research of Twitter data spanning long time periods, we use qualitative techniques to provide a more nuanced understanding of the topics of climate change. By applying our mixed-methods framework, we address our research question: what are the common topics of Australian’s tweets about climate change?

Outline of approach

We employed our four-phased framework as shown in Fig.  2 . Firstly, we harvested climate change tweets posted in Australia in 2016 and compiled a corpus (phase 1). We then utilized a topic modeling technique (Nugroho et al., 2017 ) to organize the diverse content of the corpus into a number of topics. We were interested in topics which commonly appeared throughout the time period of data collection, and less interested in more transitory topics. To identify enduring topics, we used a topic alignment algorithm (Chuang et al., 2015 ) to group similar topics occurring repeatedly throughout 2016 (phase 2). This process allowed us to identify the topics most relevant to our research question. From each of these, we extracted a manageable subset of data (phase 3). We then performed a qualitative thematic analysis (see Braun & Clarke, 2006 ) on this subset of data to inductively derive themes and answer our research question (phase 4). Footnote 2

Flowchart of application of a four-phased framework for conducting qualitative analyses using data science techniques. We were most interested in topics that frequently occurred throughout the period of data collection. To identify these, we organized the corpus chronologically, and divided the corpus into batches of content. Using computational techniques (shown in blue ), we uncovered topics in each batch and identified similar topics which repeatedly occurred across batches. When identifying topics in each batch, we generated three alternative representations of topics (5, 10, and 20 topics in each batch, shown in yellow ). In stages highlighted in green , we determined the quality of these representations, ultimately selecting the five topics per batch solution

Phase 1: Compiling a corpus

To search Australian’s Twitter data, we used CSIRO’s Emergency Situation Awareness (ESA) platform (CSIRO, 2018 ). The platform was originally built to detect, track, and report on unexpected incidences related to crisis situations (e.g., fires, floods; see Cameron, Power, Robinson, & Yin 2012 ). To do so, the ESA platform harvests tweets based on a location search that covers most of Australia and New Zealand.

The ESA platform archives the harvested tweets, which may be used for other CSIRO research projects. From this archive, we retrieved tweets satisfying three criteria: (1) tweets must be associated with an Australian location, (2) tweets must be harvested from the year 2016, and (3) the content of tweets must be related to climate change. We tested the viability of different markers of climate change tweets used in previous empirical work (Jang & Hart, 2015 ; Newman, 2016 ; Holmberg & Hellsten, 2016 ; O’Neill et al., 2015 ; Pearce et al., 2014 ; Sisco et al., 2017 ; Swain, 2017 ; Williams et al., 2015 ) by informally inspecting the content of tweets matching each criteria. Ultimately, we employed five terms (or combinations of terms) reliably associated with climate change: (1) “climate” AND “change”; (2) “#climatechange”; (3) “#climate”; (4) “global” AND “warming”; and (5) “#globalwarming”. This yielded a corpus of 201,506 tweets.

Phase 2: Using data science techniques to compress the corpus along a dimension of relevance

The next step was to organize the collection of tweets into distinct topics. A topic is an abstract representation of semantically related words and concepts. Each tweet belongs to a topic, and each topic may be represented as a list of keywords (i.e., prominent words of tweets belonging to the topic).

A vast literature surrounds the computational derivation of topics within textual corpora, and specifically within Twitter corpora (Ramage et al., 2010 ; Nugroho et al., 2017 ; Fang et al., 2016a ; Chuang et al., 2014 ). Popular methods for deriving topics include: probabilistic latent semantic analysis (Hofmann, 1999 ), non-negative matrix factorization (Lee & Seung, 2000 ), and latent Dirichlet allocation (Blei et al., 2003 ). These approaches use patterns of co-occurrence of terms within documents to derive topics. They work best on long documents. Tweets, however, are short, and thus only a few unique terms may co-occur between tweets. Consequently, approaches which rely upon patterns of term co-occurrence suffer within the Twitter environment. Moreover, these approaches ignore valuable social and temporal information (Nugroho et al., 2017 ). For example, consider a tweet t 1 and its reply t 2 . The reply feature of Twitter allows users to react to tweets and enter conversations. Therefore, it is likely t 1 and t 2 are related in topic, by virtue of the reply interaction.

To address sparsity concerns, we adopt the non-negative matrix inter-joint factorization (NMijF) of Nugroho et al., ( 2017 ). This process uses both tweet content (i.e., the patterns of co-occurrence of terms amongst tweets) and socio-temporal relationship between tweets (i.e., similarities in the users mentioned in tweets, whether the tweet is a reply to another tweet, whether tweets are posted at a similar time) to derive topics (see Supplementary Material ). The NMijF method has been demonstrated to outperform other topic modeling techniques on Twitter data (Nugroho et al., 2017 ).

Dividing the corpus into batches

Deriving many topics across a data set of thousands of tweets is prohibitively expensive in computational terms. Therefore, we divided the corpus into smaller batches and derived the topics of each batch. To keep the temporal relationships amongst tweets (e.g., timestamps of the tweets) the batches were organized chronologically. The data was partitioned into 41 disjoint batches (40 batches of 5000 tweets; one batch of 1506 tweets).

Generating topical representations for each batch

Following standard topic modeling practice, we removed features from each tweet which may compromise the quality of the topic derivation process. These features include: emoticons, punctuation, terms with fewer than three characters, stop-words (for list of stop-words, see MySQL, 2018 ), and phrases used to harvest the data (e.g., “#climatechange”). Footnote 3 Following this, the terms remaining in tweets were stemmed using the Natural Language Toolkit for Python (Bird et al., 2009 ). All stemmed terms were then tokenized for processing.

The NMijF topic derivation process requires three parameters (see Supplementary Material for more details). We set two of these parameters to the recommendations of Nugroho et al., ( 2017 ), based on empirical analysis. The final parameter—the number of topics derived from each batch—is difficult to estimate a priori , and must be made with some care. If k is too small, keywords and tweets belonging to a topic may be difficult to conceptualize as a singular, coherent, and meaningful topic. If k is too large, keywords and tweets belonging to a topic may be too specific and obscure. To determine a reasonable value of k , we ran the NMijF process on each batch with three different levels of the parameter—5, 10, and 20 topics per batch. This process generated three different representations of the corpus: 205, 410, and 820 topics. For each of these representations, each tweet was classified into one (and only one) topic. We represented each topic as a list of ten keywords most prevalent within the tweets of that topic.

Assessing the quality of topical representations

To select a topical representation for further analysis, we inspected the quality of each. Initially, we considered the use of a completely automatic process to assess or produce high quality topic derivations. However, our attempts to use completely automated techniques on tweets with a known topic structure failed to produce correct or reasonable solutions. Thus, we assessed quality using human assessment (see Table  1 ). The first stage involved inspecting each topical representation of the corpus (205, 410, and 820 topics), and manually flagging any topics that were clearly problematic. Specifically, we examined each topical representation to determine whether topics represented as separate were in fact distinguishable from one another. We discovered that the 820 topic representation (20 topics per batch) contained many closely related topics.

To quantify the distinctiveness between topics, we compared each topic to each other topic in the same batch in an automated process. If two topics shared three or more (of ten) keywords, these topics were deemed similar. We adopted this threshold from existing topic modeling work (Fang et al., 2016a , b ), and verified it through an informal inspection. We found that pairs of topics below this threshold were less similar than those equal to or above it. Using this threshold, the 820 topic representation was identified as less distinctive than other representations. Of the 41 batches, nine contained at least two similar topics for the 820 topic representation (cf., 0 batches for the 205 topic representation, two batches for the 410 topic representation). As a result, we chose to exclude the representation from further analysis.

The second stage of quality assessment involved inspecting the quality of individual topics. To achieve this, we adopted the pairwise topic preference task outlined by Fang et al. ( 2016a , b ). In this task, raters were shown pairs of two similar topics (represented as ten keywords), one from the 205 topic representation and the other from the 410 topic representation. To assist in their interpretation of topics, raters could also view three tweets belonging to each topic. For each pair of topics, raters indicated which topic they believed was superior, on the basis of coherency, meaning, interpretability, and the related tweets (see Table  1 ). Through aggregating responses, a relative measure of quality could be derived.

Initially, members of the research team assessed 24 pairs of topics. Results from the task did not indicate a marked preference for either topical representation. To confirm this impression more objectively, we recruited participants from the Australian community as raters. We used Qualtrics—an online survey platform and recruitment service—to recruit 154 Australian participants, matched with the general Australian population on age and gender. Each participant completed judgments on 12 pairs of similar topics (see Supplementary Material for further information).

Participants generally preferred the 410 topic representation over the 205 topic representation ( M = 6.45 of 12 judgments, S D = 1.87). Of 154 participants, 35 were classified as indifferent (selected both topic representations an equal number of times), 74 preferred the 410 topic representation (i.e., selected the 410 topic representation more often than the 205 topic representation), and 45 preferred the 205 topic representation (i.e., selected the 205 topic representation more often that the 410 topic representation). We conducted binomial tests to determine whether the proportion of participants of the three just described types differed reliably from chance levels (0.33). The proportion of indifferent participants (0.23) was reliably lower than chance ( p = 0.005), whereas the proportion of participants preferring the 205 topic solution (0.29) did not differ reliably from chance levels ( p = 0.305). Critically, the proportion of participants preferring the 410 topic solution (0.48) was reliably higher than expected by chance ( p < 0.001). Overall, this pattern indicates a participant preference for the 410 topic representation over the 205 topic representation.

In summary, no topical representation was unequivocally superior. On a batch level, the 410 topic representation contained more batches of non-distinct topic solutions than the 205 topic representation, indicating that the 205 topic representation contained topics which were more distinct. In contrast, on the level of individual topics, the 410 topic representation was preferred by human raters. We use this information, in conjunction with the utility of corresponding aligned topics (see below), to decide which representation is most suitable for our research purposes.

Grouping similar topics repeated in different batches

We were most interested in topics which occurred throughout the year (i.e., in multiple batches) to identify the most stable components of climate change commentary (phase 3). We grouped similar topics from different batches using a topical alignment algorithm (see Chuang et al. 2015 ). This process requires a similarity metric and a similarity threshold. The similarity metric represents the similarity between two topics, which we specified as the proportion of shared keywords (from 0, no keywords shared, to 1, all ten keywords shared). The similarity threshold is a value below which two topics were deemed dissimilar. As above, we set the threshold to 0.3 (three of ten keywords shared)—if two topics shared two or fewer keywords, the topics could not be justifiably classified as similar. To delineate important topics, groups of topics, and other concepts we have provided a glossary of terms in Table  2 .

The topic alignment algorithm is initialized by assigning each topic to its own group. The alignment algorithm iteratively merges the two most similar groups, where the similarity between groups is the maximum similarity between a topic belonging to one group and another topic belonging to the other. Only topics from different groups (by definition, topics from the same group are already grouped as similar) and different batches (by definition, topics from the same batch cannot be similar) can be grouped. This process continues, merging similar groups until no compatible groups remain. We found our initial implementation generated groups of largely dissimilar topics. To address this, we introduced an additional constraint—groups could only be merged if the mean similarity between pairs of topics (each belonging to the two groups in question) was greater than the similarity threshold. This process produced groups of similar topics. Functionally, this allowed us to detect topics repeated throughout the year.

We ran the topical alignment algorithm across both the 205 and 410 topic representations. For the 205 and 410 topic representation respectively, 22.47 and 31.60% of tweets were not associated with topics that aligned with others. This exemplifies the ephemeral and dynamic attributes of Twitter activity: over time, the content of tweets shifts, with some topics appearing only once throughout the year (i.e., in only one batch). In contrast, we identified 42 groups (69.77% of topics) and 101 groups (62.93% of topics) of related topics for the 205 and 410 topic representations respectively, occurring across different time periods (i.e., in more than one batch). Thus, both representations contained transient topics (isolated to one batch) and recurrent topics (present in more than one batch, belonging to a group of two or more topics).

Identifying topics most relevant for answering our research question

For the subsequent qualitative analyses, we were primarily interested in topics prevalent throughout the corpus. We operationalized prevalent topic groupings as any grouping of topics that spanned three or more batches. On this basis, 22 (57.50% of tweets) and 36 (35.14% of tweets) groupings of topics were identified as prevalent for the 205 and 410 topic representations, respectively (see Table  3 ). As an example, consider the prevalent topic groupings from the 205 topic representation, shown in Table  3 . Ten topics are united by commentary on the Great Barrier Reef (Group 2)—indicating this facet of climate change commentary was prevalent throughout the year. In contrast, some topics rarely occurred, such as a topic concerning a climate change comic (indicated by the keywords “xkcd” and “comic”) occurring once and twice in the 205 and 410 topic representation, respectively. Although such topics are meaningful and interesting, they are transient aspects of climate change commen tary and less relevant to our research question. In sum, topic modeling and grouping algorithms have allowed us to collate massive amounts of information, and identify components of the corpus most relevant to our qualitative inquiry.

Selecting the most favorable topical representation

At this stage, we have two complete and coherent representations of the corpus topics, and indications of which topics are most relevant to our research question. Although some evidence indicated that the 410 topic representation contains topics of higher quality, the 205 topic representation was more parsimonious on both the level of topics and groups of topics. Thus, we selected the 205 topic representation for further analysis.

Phase 3. Extract a subset of data

Extracting a subset of data from the selected topical representation.

Before qualitative analysis, researchers must extract a subset of data manageable in size. For this process, we concerned ourselves with only the content of prevalent topic groupings, seen in Table  3 . From each of the 22 prevalent topic groupings, we randomly sampled ten tweets. We selected ten tweets as a trade-off between comprehensiveness and feasibility. This thus reduced our data space for qualitative analysis from 201,423 tweets to 220.

Phase 4: Perform qualitative analysis

Perform thematic analysis.

In the final phase of our analysis, we performed a qualitative thematic analysis (TA; Braun & Clarke, 2006 ) on the subset of tweets sampled in phase 3. This analysis generated distinct themes, each of which answers our research question: what are the common topics of Australian’s tweets about climate change? As such, the themes generated through TA are topics. However, unlike the topics derived from the preceding computational approaches, these themes are informed by the human coder’s interpretation of content and are oriented towards our specific research question. This allows the incorporation of important diagnostic information, including the broader socio-political context of discussed events or terms, and an understanding (albeit, sometimes ambiguous) of the underlying latent meaning of tweets.

We selected TA as the approach allows for flexibility in assumptions and philosophical approaches to qualitative inquiries. Moreover, the approach is used to emphasize similarities and differences between units of analysis (i.e., between tweets) and is therefore useful for generating topics. However, TA is typically applied to lengthy interview transcripts or responses to open survey questions, rather than small units of analysis produced through Twitter activity. To ease the application of TA to small units of analysis, we modified the typical TA process (shown in Table  4 ) as follows.

Firstly, when performing phases 1 and 2 of TA, we initially read through each prevalent topic grouping’s tweets sequentially. By doing this, we took advantage of the relative homogeneity of content within topics. That is, tweets sharing the same topic will be more similar in content than tweets belonging to separate topics. When reading ambiguous tweets, we could use the tweet’s topic (and other related topics from the same group) to aid comprehension. Through the scaffold of topic representations, we facilitated the process of interpreting the data, generating initial codes, and deriving themes.

Secondly, the prevalent topic groupings were used to create initial codes and search for themes (TA phase 2 and 3). For example, the groups of topics indicate content of climate change action (group 1), the Great Barrier Reef (group 2), climate change deniers (group 3), and extreme weather (group 5). The keywords characterizing these topics were used as initial codes (e.g., “action”, “Great Barrier Reef”, “Paris Agreement”, “denial”). In sum, the algorithmic output provided us with an initial set of codes and an understanding of the topic structure that can indicate important features of the corpus.

A member of the research team performed this augmented TA to generate themes. A second rater outside of the research team applied the generated themes to the data, and inter-rater agreement was assessed. Following this, the two raters reached a consensus on the theme of each tweet.

Through TA, we inductively generated five distinct themes. We assigned each tweet to one (and only one) theme. A degree of ambiguity is involved in designating themes for tweets, and seven tweets were too ambiguous to subsume into our thematic framework. The remaining 213 tweets were assigned to one of five themes shown in Table  5 .

In an initial application of the coding scheme, the two raters agreed upon 161 (73.181%) of 220 tweets. Inter-rater reliability was satisfactory, Cohen’s κ = 0.648, p < 0.05. An assessment of agreement for each theme is presented in Table  5 . The proportion of agreement is the total proportion of observations where the two coders both agreed: (1) a tweet belonged to the theme, or (2) a tweet did not belong to the theme. The proportion of specific agreement is the conditional probability that a randomly selected rater will assign the theme to a tweet, given that the other rater did (see Supplementary Material for more information). Theme 3, theme 5, and the N/A categorization had lower levels of agreement than the remaining themes, possibly as tweets belonging to themes 3 and 5 often make references to content relevant to other themes.

Theme 1. Climate change action

The theme occurring most often was climate change action, whereby tweets were related to coping with, preparing for, or preventing climate change. Tweets comment on the action (and inaction) of politicians, political parties, and international cooperation between government, and to a lesser degree, industry, media, and the public. The theme encapsulated commentary on: prioritizing climate change action (“ Let’s start working together for real solutions on climate change ”); Footnote 4 relevant strategies and policies to provide such action (“ #OurOcean is absorbing the majority of #climatechange heat. We need #marinereserves to help build resilience. ”); and the undertaking (“ Labor will take action on climate change, cut pollution, secure investment & jobs in a growing renewables industry ”) or disregarding (“ act on Paris not just sign ”) of action.

Often, users were critical of current or anticipated action (or inaction) towards climate change, criticizing approaches by politicians and governments as ineffective (“ Malcolm Turnbull will never have a credible climate change policy ”), Footnote 5 and undesirable (“ Govt: how can we solve this vexed problem of climate change? Helpful bystander: u could not allow a gigantic coal mine. Govt: but srsly how? ”). Predominately, users characterized the government as unjustifiably paralyzed (“ If a foreign country did half the damage to our country as #climatechange we would declare war. ”), without a leadership focused on addressing climate change (“ an election that leaves Australia with no leadership on #climatechange - the issue of our time! ”).

Theme 2. Consequences of climate change

Users commented on the consequences and risks attributed to climate change. This theme may be further categorized into commentary of: physical systems, such as changes in climate, weather, sea ice, and ocean currents (“ Australia experiencing more extreme fire weather, hotter days as climate changes ”); biological systems, such as marine life (particularly, the Great Barrier Reef) and biodiversity (“ Reefs of the future could look like this if we continue to ignore #climatechange ”); human systems (“ You and your friends will die of old age & I’m going to die from climate change ”); and other miscellaneous consequences (“ The reality is, no matter who you supported, or who wins, climate change is going to destroy everything you love ”). Users specified a wide range of risks and impacts on human systems, such as health, cultural diversity, and insurance. Generally, the consequences of climate change were perceived as negative.

Theme 3. Conversations on climate change

Some commentary centered around discussions of climate change communication, debates, art, media, and podcasts. Frequently, these pertained to debates between politicians (“ not so gripping from No Principles Malcolm. Not one mention of climate change in his pitch. ”) and television panel discussions (“ Yes let’s all debate whether climate change is happening... #qanda ”). Footnote 6 Users condemned the climate change discussions of federal government (“ Turnbull gov echoes Stalinist Russia? Australia scrubbed from UN climate change report after government intervention ”), those skeptical of climate change (“ Trouble is climate change deniers use weather info to muddy debate. Careful???????????????? ”), and media (“ Will politicians & MSM hacks ever work out that they cannot spin our way out of the #climatechange crisis? ”). The term “climate change” was critiqued, both by users skeptical of the legitimacy of climate change (“ Weren’t we supposed to call it ‘climate change’ now? Are we back to ‘global warming’ again? What happened? Apart from summer? ”) and by users seeking action (“ Maybe governments will actually listen if we stop saying “extreme weather” & “climate change” & just say the atmosphere is being radicalized ”).

Theme 4. Climate change deniers

The fourth theme involved commentary on individuals or groups who were perceived to deny climate change. Generally, these were politicians and associated political parties, such as: Malcolm Roberts (a climate change skeptic, elected as an Australian Senator in 2016), Malcolm Turnbull, and Donald Trump. Commentary focused on the beliefs and legitimacy of those who deny the science of climate change (“ One Nation’s Malcolm Roberts is in denial about the facts of climate change ”) or support the denial of climate change science (“ Meanwhile in Australia... Malcolm Roberts, funded by climate change skeptic global groups loses the plot when nobody believes his findings ”). Some users advocated attempts to change the beliefs of those who deny climate change science (“ We have a president-elect who doesn’t believe in climate change. Millions of people are going to have to say: Mr. Trump, you are dead wrong ”), whereas others advocated disengaging from conversation entirely (“ You know I just don’t see any point engaging with climate change deniers like Roberts. Ignore him ”). In comparison to other themes, commentary revolved around individuals and their beliefs, rather than the phenomenon of climate change itself.

Theme 5. The legitimacy of climate change and climate science

Using our four-phased framework, we aimed to identify and qualitatively inspect the most enduring aspects of climate change commentary from Australian posts on Twitter in 2016. We achieved this by using computational techniques to model 205 topics of the corpus, and identify and group similar topics that repeatedly occurred throughout the year. From the most relevant topic groupings, we extracted a subsample of tweets and identified five themes with a thematic analysis: climate change action, consequences of climate change, conversations on climate change, climate change deniers, and the legitimacy of climate change and climate science. Overall, we demonstrated the process of using a mixed-methodology that blends qualitative analyses with data science methods to explore social media data.

Our workflow draws on the advantages of both quantitative and qualitative techniques. Without quantitative techniques, it would be impossible to derive topics that apply to the entire corpus. The derived topics are a preliminary map for understanding the corpus, serving as a scaffold upon which we could derive meaningful themes contextualized within the wider socio-political context of Australia in 2016. By incorporating quantitatively-derived topics into the qualitative process, we attempted to construct themes that would generalize to a larger, relevant component of the corpus. The robustness of these themes is corroborated by their association with computationally-derived topics, which repeatedly occurred throughout the year (i.e., prevalent topic groupings). Moreover, four of the five themes have been observed in existing data science analyses of Twitter climate change commentary. Within the literature, the themes of climate change action and consequences of climate change are common (Newman, 2016 ; O’Neill et al., 2015 ; Pathak et al., 2017 ; Pearce, 2014 ; Jang and Hart, 2015 ; Veltri & Atanasova, 2017 ). The themes of the legitimacy of climate change and climate science (Jang & Hart, 2015 ; Newman, 2016 ; O’Neill et al., 2015 ; Pearce, 2014 ) and climate change deniers (Pathak et al., 2017 ) have also been observed. The replication of these themes demonstrates the validity of our findings.

One of the five themes—conversations on climate change—has not been explicitly identified in existing data science analyses of tweets on climate change. Although not explicitly identifying the theme, Kirilenko and Stepchenkova ( 2014 ) found hashtags related to public conversations (e.g., “#qanda”, “#Debates”) were used frequently throughout the year 2012. Similar to the literature, few (if any) topics in our 205 topic solution could be construed as solely relating to the theme of “conversation”. However, as we progressed through the different phases of the framework, the theme became increasingly apparent. By the grouping stage, we identified a collection of topics unified by a keyword relating to debate. The subsequent thematic analysis clearly discerned this theme. The derivation of a theme previously undetected by other data science studies lends credence to the conclusions of Guetterman et al., ( 2018 ), who deduced that supplementing a quantitative approach with a qualitative technique can lead to the generation of more themes than a quantitative approach alone.

The uniqueness of a conversational theme can be accounted for by three potentially contributing factors. Firstly, tweets related to conversations on climate change often contained material pertinent to other themes. The overlap between this theme and others may hinder the capabilities of computational techniques to uniquely cluster these tweets, and undermine the ability of humans to reach agreement when coding content for this theme (indicated by the relatively low proportion of specific agreement in our thematic analysis). Secondly, a conversational theme may only be relevant in election years. Unlike other studies spanning long time periods (Jang and Hart, 2015 ; Veltri & Atanasova, 2017 ), Kirilenko and Stepchenkova ( 2014 ) and our study harvested data from US presidential election years (2012 and 2016, respectively). Moreover, an Australian federal election occurred in our year of observation. The occurrence of national elections and associated political debates may generate more discussion and criticisms of conversations on climate change. Alternatively, the emergence of a conversational theme may be attributable to the Australian panel discussion television program Q & A. The program regularly hosts politicians and other public figures to discuss political issues. Viewers are encouraged to participate by publishing tweets using the hashtag “#qanda”, perhaps prompting viewers to generate uniquely tagged content not otherwise observed in other countries. Importantly, in 2016, Q & A featured a debate on climate change between science communicator Professor Brian Cox and Senator Malcolm Roberts, a prominent climate science skeptic.

Although our four-phased framework capitalizes on both quantitative and qualitative techniques, it still has limitations. Namely, the sparse content relationships between data points (in our case, tweets) can jeopardize the quality and reproducibility of algorithmic results (e.g., Chuang et al., 2015 ). Moreover, computational techniques can require large computing resources. To a degree, our application mitigated these limitations. We adopted a topic modeling algorithm which uses additional dimensions of tweets (social and temporal) to address the influence of term-to-term sparsity (Nugroho et al., 2017 ). To circumvent concerns of computing resources, we partitioned the corpus into batches, modeled the topics in each batch, and grouped similar topics together using another computational technique (Chuang et al., 2015 ).

As a demonstration of our four-phased framework, our application is limited to a single example. For data collection, we were able to draw from the procedures of existing studies which had successfully used keywords to identify climate change tweets. Without an existing literature, identifying diagnostic terms can be difficult. Nevertheless, this demonstration of our four-phased framework exemplifies some of the critical decisions analysts must make when utilizing a mixed-method approach to social media data.

Both qualitative and quantitative researchers can benefit from our four-phased framework. For qualitative researchers, we provide a novel vehicle for addressing their research questions. The diversity and volume of content of social media data may be overwhelming for both the researcher and their method. Through computational techniques, the diversity and scale of data can be managed, allowing researchers to obtain a large volume of data and extract from it a relevant sample to conduct qualitative analyses. Additionally, computational techniques can help researchers explore and comprehend the nature of their data. For the quantitative researcher, our four-phased framework provides a strategy for formally documenting the qualitative interpretations. When applying algorithms, analysts must ultimately make qualitative assessments of the quality and meaning of output. In comparison to the mathematical machinery underpinning these techniques, the qualitative interpretations of algorithmic output are not well-documented. As these qualitative judgments are inseparable from data science, researchers should strive to formalize and document their decisions—our framework provides one means of achieving this goal.

Through the application of our four-phased framework, we contribute to an emerging literature on public perceptions of climate change by providing an in-depth examination of the structure of Australian social media discourse. This insight is useful for communicators and policy makers hoping to understand and engage the Australian online public. Our findings indicate that, within Australian commentary on climate change, a wide variety of messages and sentiment are present. A positive aspect of the commentary is that many users want action on climate change. The time is ripe it would seem for communicators to discuss Australia’s policy response to climate change—the public are listening and they want to be involved in the discussion. Consistent with this, we find some users discussing conversations about climate change as a topic. Yet, in some quarters there is still skepticism about the legitimacy of climate change and climate science, and so there remains a pressing need to implement strategies to persuade members of the Australian public of the reality and urgency of the climate change problem. At the same time, our analyses suggest that climate communicators must counter the sometimes held belief, expressed in our second theme on climate change consequences, that it is already too late to solve the climate problem. Members of the public need to be aware of the gravity of the climate change problem, but they also need powerful self efficacy promoting messages that convince them that we still have time to solve the problem, and that their individual actions matter.

On Twitter, users may precede a phrase with a hashtag (#). This allows users to signify and search for tweets related to a specific theme.

The analysis of this study was preregistered on the Open Science Framework: https://osf.io/mb8kh/ . See the Supplementary Material for a discussion of discrepancies. Analysis scripts and interim results from computational techniques can be found at: https://github.com/AndreottaM/TopicAlignment .

83 tweets were rendered empty and discarded from the corpus.

The content of tweet are reported verbatim. Sensitive information is redacted.

Malcolm Turnbull was the Prime Minister of Australia during the year 2016.

“ #qanda ” is a hashtag used to refer to Q & A, an Australian panel discussion television program.

Commonwealth Scientific and Industrial Research Organisation (CSIRO) is the national scientific research agency of Australia.

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Andreotta, M., Nugroho, R., Hurlstone, M.J. et al. Analyzing social media data: A mixed-methods framework combining computational and qualitative text analysis. Behav Res 51 , 1766–1781 (2019). https://doi.org/10.3758/s13428-019-01202-8

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8 ways to use social media for market research

Written by by Annette Chacko

Published on  May 30, 2023

Reading time  9 minutes

As marketers, it’s our job to know our target audience’s needs and preferences. It’s why we conduct market research every year to better understand what our customers want and uncover new business opportunities. But traditional market research is no longer enough now that consumer preferences and market dynamics can change overnight.

While focus groups and annual surveys are still useful touchpoints, they reveal little about current events or trending topics among consumers. Social media is overflowing with these in-the-moment insights—it’s market research on steroids. That’s why 93% of business leaders say social media data and insights will be their company’s primary source of business intelligence.

But this dynamic and rich data source hasn’t been fully harnessed. You need to use social media market research to uncover critical customer, competitor and industry insights to maintain an accurate pulse on your market while keeping costs low.

What is social media market research and why is it valuable?

Social media market research is the practice of gathering historical and real-time data from social media channels to improve your business. It gives you critical insights from qualitative data (comments and posts) and quantitative metrics (likes and shares).

While it can be done natively, intelligent tools driven by artificial intelligence, like Sprout, make the process of gathering social intelligence:

• Cost-effective: It’s more affordable than traditional methods like surveys and focus groups that can cost thousands of dollars depending on the size and complexity of the research project.
• Quick: Where traditional market research takes time, social media updates in real-time for immediate results, anytime you want.
• Comprehensive: With over 4.76 billion social media users in 2023 , gather vast and diverse social listening data to analyze conversations and extract more accurate insights about your brand and the entire industry.

Sprout enables you to research different social platforms at the same time and analyze the information in one centralized platform to inform data-driven marketing strategies across the organization.

The benefits of using social media for market research

According to The Harris Poll data in The 2023 State of Social Media report, 90% of business leaders agree that their company’s success will depend on how effectively it can use social media data and insights to inform business strategy.

Social media market research is the tool that will enable you to harness the data that will transform every part of your business , including:

Becoming more customer-centric

Our report also revealed that 91% of business leaders said social insights had an impact on gaining a better understanding of their customers. The granular insight social media marketing research provides into your consumer base helps you develop customer-centric strategies that build stronger relationships, increase retention and improve your growth rate.

Managing brand reputation

Social media research gives you data-driven insights into how your target audience perceives your brand. That’s why 94% of business leaders look at social media insights to build and manage brand reputation.

Keeping ahead of competitors

Ninety-two percent of business leaders say social media insights help improve their brand’s competitive positioning. Social data taps into deep competitor insights , such as what people like about competing brands and how customers respond to them. All of which guide how you can differentiate yourself from the pack.

Predicting future trends and filling market gaps

Market research through social listening helps you know where consumers are going next, per 89% of business leaders, who said that social insights impacted how they predict future trends. Social media market research gives you a holistic view of the market so you can identify emerging trends and plan long-term and short-term growth campaigns. And so, enabling you to take advantage of market gap opportunities and expand your market share in a focused way.

Optimizing your business with less spend

Seventy-six percent of business leaders say that social media insights have positively impacted moving their businesses forward with reduced budgets. With close to five billion people on social media, it provides a cost-effective way to magnify your brand, engage with customers and reach out to new audiences. It helps brands personalize campaigns and customer care more precisely and at scale.

8 insights you can glean from social media market research

Social listening data is a valuable source of market intelligence. But with all that raw data at your disposal, it can be challenging to sift through the noise to capture what is truly important and can make a real impact on your business strategy.

Here are eight actionable insights you should pull from your social media market research and how to find them.

1. Audience intent to know what customers want

Social media market research gives you valuable insights into customer intent when they mention you or your competitors. Deep dive into social listening data to get to know your audience on a personal level, from how they feel about certain topics to what influences their purchase decisions.

How to find it: Build out your community management strategy using social networks. Also, leverage platforms like Reddit to know what your audience is talking about and respond to threads directly. Capabilities like Sprout’s social media engagement tool help you capture customer sentiment from social listening across networks and seamlessly adjust your social strategy accordingly, in one place.

2. How customers use your products and services

Social media chatter gives you an insider’s look into how your customers are using your product and services and what new opportunities are out there for you.

By researching their brand and products through social listening, Lodge Cast Iron learned how customers actually use their cookware. They discovered a new segment of customers, vegans, which led them to create more vegan recipes to better engage with them and their interests.

How to find: Search for your brand and the name of your product on social platforms like Twitter or Instagram to learn how customers actually use your goods and services. Sites like Reddit are also great to tap into customer feedback, including conversations and communities relevant to your brand or products. With a tool like Sprout Social, you can easily monitor all this data including branded keywords and phrases to stay up to date with what customers have to say.

3. What existing customers like and dislike about your brand

Social media listening tools help you understand what people like and dislike about your product and services. In fact, business leaders surveyed by The Harris Poll said that 42% of their company’s product development is influenced by social media data insights.

Customer comments are often not as simple as “I like [brand]” or “I don’t like [brand]”. Some are indirect, don’t tag your handle or misspell your brand name. Analyze all comments through sentiment analysis with a smart social media intelligence platform like Sprout to get a clear idea of what customers expect from you and distinct insights on how to address them.

How to find: There are several ways you can find feedback about your brand. Search for branded keywords and phrases organically on social, or peruse the reviews section on platforms like Facebook, TripAdvisor and Google. Beyond these searches, your inbox is a great space to find customer feedback. It’s also a good idea to reach out to sales or customer service teams who hear directly from customers about their likes and dislikes.

4. Competitor intelligence to know where you stand

Insights from social media market research guide your competitor strategy for both short-term and long-term campaigns. In fact, 92% of marketers in The 2023 State of Social Media report say that social insights play a role in improving their brand’s competitive positioning.

Understand your competitors’ content and ad strategy, track how the market responds to them and know where you are in terms of audience segmentation.

How to find: Identify your competitors and monitor their social presence across their different social channels. With Sprout Social, you can set up a competitive analysis report across various social networks including review websites, like Google My Business, to track competitor benchmarks and understand your customers’ attitudes toward the competition.

5. What customers expect from you in the future

Consumers frequently take to social to share what they want from brands in the future. A brand may receive a request for a future store location or a coffee company might see comments asking for a specific roast or drink to come back on the menu. At Sprout, we often field Tweets from customers with new feature requests that we pass along to our product team for consideration down the road.

Another way to solicit feedback about what customers want from your brand is to simply ask. You’d be surprised how many people respond with their thoughts and ideas to a question published on Twitter, LinkedIn and Facebook.

How to find: One place to find this information is right in your inbox. With Sprout’s tagging capabilities , you can label and sort messages by type as they come through, making it easy to pass along product requests to the appropriate teams.

6. Trends your audience is interested in

Be attentive to market drivers and use them to catapult your brand strategy. Take how Netflix used the nostalgia marketing trend for their popular TV show, Stranger Things. The show capitalized on people’s fond memories of days past while using children as the main protagonists to cover a wide age segment.

To harness trends successfully, it’s important to map out your business objectives, data analysis plans and baseline metrics before you start scrolling for inspiration.

How to find: Explore social media networks and other tools like Google Trends to keep a pulse on what’s hot. Also, use Sprout’s listening tool to identify trends and topics in your industry and among customers through keywords. Uncover keywords and terms most used by your target audience and discover related topics frequently mentioned with any terms you’re currently tracking.

7. What content resonates with your audience

With millions of posts published on social daily, you need to be strategic to capture your audience’s attention. Take stock of your existing content to see what themes or content formats fuel your performance goals.

If in-feed video gets you more impressions than text posts, consider investing more in video production. If your goal is to drive conversations, refer to posts with high engagement (likes, comments or shares). And don’t be afraid to ask your audience directly what topics or social content they want to see from you.

How to find: Sprout’s Post Performance Report helps you break down the types of content you’ve published and identify which performed the best. For more granular insights, sort the data by impressions, engagement and clicks to determine what formats and themes are most effective on specific networks.

8. Find who to partner with

Social media market research helps you find the right influencers or content creators to collaborate with. Insights can tell you if they match your brand values, have similar audience types and create content that consistently engages. Your co-creators can also play a key role in product development to expand your reach.

For example, Target offered influencer Tabitha Brown a first-of-its-kind deal following her first year working with the retailer. Their exclusive Tabitha Brown collections include clothing, swimwear, accessories, home and office products, food and kitchenware.

How to find: Monitor collaborations to see how their content is performing and get insights to make tweaks to remain aligned with goals. Also, track your lead conversions and see if you are getting enough return on investment (ROI).

How to share social media market research findings org-wide

Social insights provide rich insights that benefit the whole org including product teams, customer care, sales and support.

To share your findings with these various teams, your data needs to present a holistic narrative and connect your data to company goals to highlight its true impact, without being complicated. This type of strategic data storytelling requires effective visualization elements like charts, graphs and word clouds that synthesize the data alongside other business intelligence. This enables all stakeholders to understand the larger picture, with insights from every key business channel.

Sprout creates presentation-ready charts and graphs that break down customer, competitor and industry insights from across social networks. Also, combine these insights with your Tableau data for a more comprehensive omnichannel view.

With these combined insights you can:

• Track social KPIs org-wide and measure against benchmarks.
• Share crucial product experience insights with the product development teams.
• Inform marketing teams of any negative brand buzz.
• Collaborate with the broader marketing teams to refine messaging and content marketing strategies.

Better research starts with social

When brands demonstrate they understand their customers’ wants and needs, they stand to strengthen their bottom line and develop customers for life. Thanks to social media market research, brands have access to fast and reliable insights that deepen their understanding of what their customers want and expect. From identifying new opportunities to improving customer relationships, data from social empowers you to make smarter decisions that contribute to your business goals.

Ready to turn your social data into valuable insights about your industry and customers? Download this worksheet to learn how to conduct quick and valuable market research in under 90 minutes.

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Conceptualising and measuring social media engagement: A systematic literature review

Mariapina trunfio.

Department of Management and Quantitative Studies, University of Naples “Parthenope”, Naples, Italy

Simona Rossi

The spread of social media platforms enhanced academic and professional debate on social media engagement that attempted to better understand its theoretical foundations and measurements. This paper aims to systematically contribute to this academic debate by analysing, discussing, and synthesising social media engagement literature in the perspective of social media metrics. Adopting a systematic literature review, the research provides an overarching picture of what has already been investigated and the existing gaps that need further research. The paper confirms the polysemic and multidimensional nature of social media engagement. It identifies the behavioural dimension as the most used proxy for users' level of engagement suggesting the COBRA model as a conceptual tool to classify and interpret the construct. Four categories of metrics emerged: quantitative metrics, normalised indexes, set of indexes, qualitative metrics. It also offers insights and guidance to practitioners on modelling and managing social media engagement.

Introduction

Over the last decade, customer engagement has received increasing attention in academic and professional debate (Hollebeek, 2019 ; Kumar et al., 2019 ; Marketing Science Institute, 2020 ; Peltier et al., 2020 ; Rather et al., 2019 ; Rossmann et al., 2016 ). It can be considered a “consumer’s positively brand-related cognitive, emotional and behavioural activity during, or related to, focal consumer/brand interactions” (Hollebeek, 2014 , p.149). Engaged customers display greater brand loyalty and satisfaction (Bowden, 2009 ; Jaakkola & Alexander, 2014 ) and are more likely to contribute to new product development (Haumann et al., 2015 ), service innovation (Kumar et al., 2010 ), and viral marketing activity spread by word of mouth (Wu et al., 2018 ). Customer engagement can also be linked with important brand performance indicators, including sales growth, feedback, and referrals (Van Doorn et al., 2010 ).

Acknowledging the potential of ICTs, scholars and practitioners are experimenting with new ways to capitalise on customer engagement and adapt to the new challenges of digital platforms (Barger et al., 2016 ; Peltier et al., 2020 ). Social media platforms reshaped the dyadic interaction between customers and organisations, creating spaces for digital sharing and engagement. By enabling users to comment, review, create, and share content across online networks, social media provide direct access to brands and allow co-creation processes. As such, the pervasive character of social media with its potential for engaging with customers and building relationships generated much interest in the concept of social media engagement (Barger et al., 2016 ; Hallock et al., 2019 ; Oviedo-García et al., 2014 ; Peltier et al., 2020 ; Schivinski et al., 2016 ). Engaging with customers in real-time and managing many incoming customers’ big data interested academic investigation and opened opportunities for marketers to enhance social media marketing success (Liu et al., 2019 ).

Understanding, monitoring, and measuring social media engagement are key aspects that interest scholars and practitioners who proposed diverse conceptualisations, several indicators and KPIs. With the spread of social media analytics, social networking platforms, digital service providers, marketers, and freelancers developed their metrics to measure engagement with brand-related social media contents and advertising campaigns. At the same time, scholars have pointed out various metrics and procedures that contribute to evaluating social media engagement in different fields (Mariani et al., 2018 ; Muñoz-Expósito et al., 2017 ; Trunfio & Della Lucia, 2019 ). Nevertheless, many of these studies offer a partial perspective of analysis that does not allow the phenomenon to be represented in diverse aspects (Oviedo-García et al., 2014 ). As a result, social media engagement remains an enigma wrapped in a riddle for many executives (McKinsey, 2012 ). How communities across an ever-growing variety of platforms, new forms of customer-brand interactions, different dimensions and cultural differences impact social media engagement measurement represents one of the main challenges (Peltier et al., 2020 ).

Although social media engagement represented a key topic in marketing research (Barger et al., 2016 ; Peltier et al., 2020 ), an overarching perspective of the existing knowledge can drive the investigation of the state of the field, including the study of the research streams, and the analysis of the measurement tools. This paper aims to systematically contribute to the academic debate by analysing, discussing, and synthesising social media engagement literature from the social media metrics perspective. A systematic literature review approach provides an overarching picture of what has already been investigated and the existing gaps that need further research. It contributes towards a systematic advancement of knowledge in the field and offers insights and guidance to practitioners on modelling and managing social media engagement (Tranfield et al., 2003 ).

The remainder of the paper is structured as follows. Section  2 presents the theoretical background of the study on customer engagement and social media engagement. Section  3 describes the methodology used for conducting the systematic literature review (Pickering & Byrne, 2014 ; Tranfield et al., 2003 ). Section  4 presents the bibliometric analysis results, including the year in which research began, the journals that publish most research, and the most relevant authors with publications on the topic. Then, Sect.  5 classifies these studies in terms of four macro-themes, conceptualisations, platforms, measurement, and behaviours and describes the key results available in the literature. Section  6 provides a critical discussion of the findings from the literature review and highlights its key contributions. Lastly, Sect.  7 concludes the study by highlighting its limitations and proposing directions for future research.

Theoretical background

Customer engagement.

Although customer engagement research has increased theoretical and managerial relevance (Brodie et al., 2011 ; Hollebeek et al., 2016 , 2019 ; Kumar et al., 2019 ; Vivek et al., 2012 ), to date, there is still no consensus on its definition due to its multidimensional, multidisciplinary and polysemic nature.

Several customer engagement conceptualisations have been proposed in the literature, drawing on various theoretical backgrounds, particularly service-dominant logic, and relationship marketing. From a psychological perspective, one of the first definitions of customer engagement is the one of Bowden ( 2009 ) that conceptualises it as a psychological process that drives customer loyalty. Similarly, Brodie et al. ( 2011 ) define customer engagement as a psychological state that occurs by interactive, co-creative customer experiences with a focal object. Later, focusing on the behavioural aspects, it has been described as the intensity of an individual’s participation in an organisation’s offerings or organisational activities (Vivek et al., 2012 ). More recently, from a value-based perspective, customer engagement has been defined as the mechanics that customers use to add value to the firm (Kumar et al., 2019 ).

Although the perspectives may vary, common elements can be identified in various conceptualisations. Literature generally understands customer engagement as a highly experiential, subjective, and context-dependent construct (Brodie et al., 2011 ) based on customer-brand interactions (Hollebeek, 2018 ). Moreover, scholars agree on its multidimensional nature (Brodie et al., 2013 ; Hollebeek et al., 2016 ; So et al., 2016 ; Vivek et al., 2012 ) encompassing cognitive (customer focus and interest in a brand), emotional (feelings of inspiration or pride caused by a brand), and behavioural (customer effort and energy necessary for interaction with a brand) dimensions. Also, researchers have proposed that customer engagement affects different marketing constructs (Brodie et al., 2011 ; Van Doorn et al., 2010 ). For example, in Bowden’s research (2009), there is evidence to support that customer engagement is a predictor of loyalty. Brodie et al. ( 2011 ) explore its effects on customer satisfaction, empowerment, trust, and affective commitment towards the members of a community. Van Doorn et al. ( 2010 ) propose customer-based drivers, including attitudinal factors such as satisfaction, brand commitment and trust, as well as customer goals, resources, and value perceptions.

Social media engagement: The academic perspective

Social media engagement has also been investigated as brand-user interaction on social media platforms (Barger et al., 2016 ; De Vries & Carlson, 2014 ; Hallock et al., 2019 ; Oviedo-García et al., 2014 ; Peltier et al., 2020 ; Schivinski et al., 2016 ). However, while conceptual discussions appear to dominate the existing customer engagement literature, research results fragmented when moving to the online context. Scholars agree that social media engagement is a context-specific occurrence of customer engagement (Brodie et al., 2013 ) that reflects customers’ individual positive dispositions towards the community or a focal brand (Dessart, 2017 ). Social media engagement can emerge with respect to different objects: the community, representing other customers in the network, and the brand (Dessart, 2017 ). Furthermore, antecedents and consequences of social media engagement have been identified to understand why customers interact on social media and the possible outcomes (Barger et al., 2016 ), such as loyalty, satisfaction, trust, and commitment (Van Doorn et al., 2010 ).

In continuity with literature on customer engagement, also social media engagement can be traced back to affective, cognitive, and behavioural dimensions (Van Doorn et al., 2010 ). Most of the literature focuses on the behavioural dimension as it can be expressed through actions such as liking, commenting, sharing, and viewing contents from a brand (Barger et al., 2016 ; Muntinga et al., 2011 ; Oh et al., 2017 ; Oviedo-García et al., 2014 ; Peltier et al., 2020 ; Rietveld et al., 2020 ; Schivinski et al., 2016 ). It is worth pointing out that not all these actions determine the same level of engagement. Schivinski et al. ( 2016 ) in the COBRA (Consumer Online Brand Related Activities) Model differentiate between three levels of social media engagement: consumption, contribution, and creation. Consumption constitutes the minimum level of engagement and is the most common brand-related activity among customers (e.g., viewing brand-related audio, video, or pictures). Contribution denotes the response in peer-to-peer interactions related to brands (e.g., liking, sharing, commenting on brand-related contents). Creation is the most substantial level of the online brand-related activities that occur when customers spontaneously participate in customising the brand experiences (e.g., publishing brand-related content, uploading brand-related video, pictures, audio or writing brand-related articles). Starting from these social media actions, scholars attempted to measure social media engagement in several ways developing scales, indexes, and metrics (Harrigan et al., 2017 ; Oviedo-García et al., 2014 ; Schivinski et al., 2016 ; Trunfio & Della Lucia, 2019 ). Nevertheless, many of these studies offer a partial perspective of analysis that does not allow the phenomenon to be represented in its diverse aspects (Oviedo-García et al., 2014 ). Researchers have also examined emotional and cognitive dimensions (Dessart, 2017 ) as essential components of social media engagement that lead to positive brand outcomes (Loureiro et al., 2017 ).

Social media engagement: The practitioners’ perspective

In business practice, the concept of customer engagement appeared for the first time in 2006 when the Advertising Research Foundation (ARF), in conjunction with the American Association of Advertising Agencies and the Association of National Advertisers, defined it as a turning on a prospect to a brand idea enhanced by the surrounding context (ARF, 2006 ) . Later, several consulting firms tried to give their definition emphasising different aspects and perspectives. For example, in 2008, Forrester Consulting, an American market research company, defined customer engagement as a way to create ‘deep connections with customers that drive purchase decisions, interaction, and participation over time’ (Forrester Consulting, 2008 , p.4). Gallup Consulting identified four levels of customer engagement and defined it as an emotional connection between customers and companies (Gallup Consulting, 2009 ). Similarly, the famous American software provider Hubspot ( 2014 ) identified social media engagement as ‘ the ongoing interactions between company and customer, offered by the company, chosen by the customer’ (Hubspot, 2014 , p.1).

With the increasing spread of social networks and their exploitation as an important marketing tool, practitioners recognised a clear linkage between customer engagement and the metrics to assess digital strategy success. Over time, social networking platforms such as Facebook, LinkedIn, and YouTube, developed their metrics to measure engagement with brand-related social media contents and advertising campaigns (Table ​ (Table1 1 ).

Social media engagement metrics by social networking platforms (2020)

With the spread of social media analytics, platforms and digital service providers developed dashboards and analytical indicators to assess, measure and monitor the engagement generated by social media marketing activities (Table ​ (Table2). 2 ). At the same time, many bloggers, marketers, and freelancers have weighed in on the topic, enriching the debate with new contributions.

Social media engagement metrics by social media management and analytics platforms (2020)

As a result, while scholars still have to agree upon a shared definition of social media engagement, marketers have recognised it as one of the most important online outcome companies need to deliver with social media and a key metric to assess social media strategy success . Despite the growing interest in business practice and its solid traditional theoretical roots, most of the existing literature on social media engagement offers only conceptual guidelines (Barger et al., 2016 ; Peltier et al., 2020 ). The measurement of engagement in social media and its financial impact remains an enigma wrapped in a riddle for many executives (McKinsey, 2012 ) and requires further investigations. Mainly, how new and emerging platforms, new forms of customer-brand interactions, different dimensions, and cultural differences impact social media engagement measurement remains an understudied phenomenon (Peltier et al., 2020 ).

Methodology

The literature review is one of the most appropriate research methods, which aims to map the relevant literature identifying the potential research gaps that need further research to contribute towards a systematic advancement of new knowledge in the field (Tranfield et al., 2003 ). This research is built upon the rigorous, transparent, and reproducible protocol of the systematic literature review as a scientific and transparent process that reduces the selection bias through an exhaustive literature search (Pencarelli & Mele, 2019 ; Pickering & Byrne, 2014 ; Tranfield et al., 2003 ). Building on recent studies (Inamdar et al., 2020 ; Linnenluecke et al., 2020 ; Phulwani et al., 2020 ), in addition to the systematic literature review, a bibliometric analysis (Li et al., 2017 ) was also performed to provide greater comprehensions into the field's current state and highlight the future research directions.

Database, keywords, inclusion, and exclusion criteria

To conduct a literature review, quality journals are considered the basis for selecting quality publications (Wallace & Wray, 2016 ). Therefore, the database Scopus, run by Elsevier Publishing, was considered to search for relevant literature, being the most significant abstract and citation source database used in recent reviews.

When conducting a literature review, a fundamental issue is determining the keywords that allow identifying the papers (Aveyard, 2007 ). To address it, the most frequently used keywords in peer-reviewed literature have been under investigation. As such, the following research chain was used: “Social media” “Engagement” AND “metric*”, searching under title, abstract, and keywords.

The systematic literature review protocol (Fig.  1 ) has been conducted on the 26 th of March 2020. The study considers an open starting time to trace back to the origin of social media engagement metrics research up to late March 2020. The initial search attempts identified 259 documents.

The systematic literature review protocol

After the articles’ identification, criteria for inclusion and exclusion were adopted. First, the 259 articles were screened, considering English-language articles published in peer-reviewed academic journals to safeguard the quality and effectiveness of the review. Due to variability in the peer-review process and their limited availability, book reviews, editorials, and papers from conference proceedings were excluded from this research. After the screening, a sample of 157 papers was obtained.

Afterwards, the full text of these papers was reviewed to assess eligible articles. As a result, 116 articles were excluded because their subject matter was not closely related to the topic of social media engagement metrics. In detail, papers were excluded when: 1) they mainly focused on social media engagement but superficially touched the metrics or 2) they mainly focused on metrics but superficially touched on social media engagement. In the end, 41 eligible articles were identified.

Analysis tools

The relevant data of the 41 documents in the final sample were saved and organised in a Microsoft Excel spreadsheet to include all the essential paper information such as paper title, authors’ names, and affiliations, abstract, keywords and references. Then, adopting the bibliometrics analysis method (Aria & Cuccurullo, 2017 ), the R-Tool ‘Biblioshiny for Bibliometrix’ was used to perform a comprehensive bibliometric analysis. Bibliometrix is a recent R-package that facilitates a more complete bibliometric analysis, employing specific tools for both bibliometric and scientometric quantitative research (Aria & Cuccurullo, 2017 ; Dervis, 2019 ; Jalal, 2019 ).

An overview of social media engagement metrics research.

The bibliometric analysis provided information on the 41 articles, allowing to highlight the significance of the topic.

Publication trend

The number of annual publications shows a rollercoaster trend (Fig.  2 ). Although the first relevant paper was published in 2013, only since 2016 publications begun to increase significantly with a slight decrease in 2018. This renders social media engagement metrics a relatively young research field.

Timeline of the studies (January 2013- March 2020)

It is worth pointing out that the articles extraction was done in March 2020: this explains the low number of articles published in 2020.

Most relevant sources

When looking at the Journal sources overview, the analysis revealed 34 journals covering different fields, including marketing, management, economics, tourism and hospitality, engineering, communication, and technology. As shown in Fig.  3 , only four journals have more than two publications: Internet Research , Journal of Engineering and Applied Sciences , International Journal of Sports Marketing and Sponsorship. and Online Information Review .

Seminal papers

Interesting findings emerged considering the most global cited documents that allow identifying the seminal articles in according to the timeliness, utility and quality, expressed by the scientific community (Okubo, 1997 ). The number of citations an article receives, and the studies cited in an article are two of the most popular bibliometric indicators used to determine the popularity of a publication.

Figure  4 shows the number of author citations for each article, identifying as seminal works: Malthouse’s (2013) paper ‘ Managing Customer Relationships in the Social Media Era: Introducing the Social CRM House’ with 278 global citations; Sabate’s (2014) paper ‘Factors influencing popularity of branded content in Facebook fan pages’ with 145 global citations; Mariani’s (2016) paper ‘ Facebook as a destination marketing tool: Evidence from Italian regional Destination Management Organizations ’ with 104 global citations; Oh’s (2017) paper ‘ Beyond likes and tweets: Consumer engagement behavior and movie box office in social media ’ with 54 global citations; Colicev’s (2018)’ Improving consumer mindset metrics and shareholder value through social media: The different roles of owned and earned media ’ with 39 global citations; Rossmann’s (2016) ‘ Drivers of user engagement in eWoM communication ’ with 35 global citations; Oviedo-Garcia’s (2014) ‘ Metric proposal for customer engagement in Facebook’ with 33 global citations .

Most cited articles

The analysis of the papers reviewed revealed that the theme of social media engagement metrics turns out to be a hot topic and a newly emerging stream of research.

Social media engagement: areas of investigation

In recent years social media engagement has gained relevance in academic research, and many scholars have questioned its measurement, intensifying the academic debate with ever new contributions. Following previous studies, a comprehensive analysis allows framing the following categories of broad research subjects, used to conduct the subsequent systematic literature review (Fig.  5 ): (1) conceptualisation, (2) platforms, (3) measurement and (4) behaviours. All 41 articles were analysed according to the proposed scheme.

Areas of investigation

Investigating social media engagement

What emerges from the analysis of the 41 papers is that scholars used different approaches and methodologies to conceptualise and measure engagement in the digital context of social media.

As shown in Fig.  6 , most studies (66%) employ quantitative methodologies. For instance, Yoon et al. ( 2018 ) explored the relationship between digital engagement metrics and financial performance in terms of company revenue, confirming that customer engagement on a company’s Facebook fan page can influence revenue. Colicev et al. ( 2018 ) developed three social media metrics, including engagement, to study the effects of earned social media and owned social media on brand awareness, purchase intention, and customer satisfaction. In comparison, Wang and Kubickova ( 2017 ) examined factors affecting the engagement metrics of Facebook fan pages in the Northeast America hotel industry, factors such as time-of-day, day-of-week, age, gender and distance between the hotel and users’ origin of residence. They also analysed the impact of Facebook engagement on electronic word-of-mouth (eWOM), to better understand the importance of the engagement metrics within the hospitality context.

Classification of the 41 articles based on the methodology applied

From a qualitative point of view (17% of the papers), Hallock et al. ( 2019 ) used a case study approach to understand the firm perspective on social media engagement metrics, shedding light on how companies view engagement with social media as measurable metrics of customer interactions with the platform. Conversely, Michopoulou and Moisa ( 2019 ) used the same approach to investigate the use of social media marketing metrics and practices in the U.K. hotel industry.

Only a small part of the studies analysed (10% of the papers) explores social media engagement from a purely conceptual perspective. In this sense, Oviedo-Garcìa et al. ( 2014 ) and Muñoz-Expósito et al. ( 2017 ) directly identified social media engagement metrics for Facebook and Twitter, providing fascinating insights for scholars and practitioners.

Finally, among the papers analysed, only three studies (7% of the papers) use mixed methodologies to explore the phenomenon from qualitative and quantitative perspectives.

Defining social media engagement

Researchers identified 30 unique definitions of engagement applied to the social media context. Multiple definitions used several terms when defining engagement on social media. They were not singular and straightforward but were interspersed with various key terms and overlapping concepts, as presented in Table ​ Table3 3 .

Frequency of the terms used to define engagement in social media

The presence of synonymous terms directly addresses the lack of a standard definition and the challenges that this presents to researchers and practitioners in the field (Table ​ (Table4 4 ).

Social media engagement main definitions

As a relevant result, most authors focus on its behavioural manifestation (22% of the studies) resulting from motivational drivers when defining social media engagement. It is considered as the active behavioural efforts that both existing and potential customers exert toward online brand-related content (Yoon et al., 2018 ). It involves various activities that range from consuming content, participating in discussions, and interacting with other customers to digital buying (Oh et al., 2017 ; Yoon et al., 2018 ). Similarly, in addition to the behavioural manifestations, other scholars (12%) focus on the emotional connection expressed through the intensity of interactions and their implications, toward the offers and activities of a brand, product, or firm, regardless of whether it is initiated by the individual or by the firm (Muñoz-Expósito et al., 2017 ).

Shifting the observation lens from the customers to the firms, another group of scholars (10% of the studies) define social media engagement as the non-monetary return that derives from the online marketing strategies of brands (Khan, 2017 ; Medjani et al., 2019 ; Michopoulou & Moisa, 2019 ). In this case, engagement is viewed exclusively as a non-financial metric and as a measure of the performance of social media marketing activities.

Lastly, a small percentage of studies (10% of the studies) considers engagement as the number of people who acknowledge agreement or preference for content, who participate in creating, sharing and using content (Colicev et al., 2018 ; Li et al., 2019 ; Rahman et al., 2017 ).

Social Media Platforms

In a total of 41 articles reviewed, 85% of studies mention the platforms analysed, as shown in Table ​ Table5. 5 . Facebook is the most popular platform analysed, followed by Twitter, YouTube, LinkedIn, and Instagram. These results were rather expected, given the fact that Facebook, with 2.6 billion monthly active users (Facebook, May 2020), is the most popular social media platform worldwide.

Platforms mentioned in the 41 articles and related frequencies

An interesting finding is that there are several articles (15% of the studies) which do not refer to a specific platform or that consider all the platforms together, when measuring social media engagement (e.g., Hallock et al., 2019 ; Medjani et al., 2019 ). This is interesting, given that each social network has different features that make the engagement measurement unique and not replicable.

Measuring social media engagement

The systematic literature review confirms that there is no theoretical certainty or solid consensus among scholars about measuring engagement on social media.

As can be seen from Table ​ Table6, 6 , studies on social media engagement metrics can be grouped and classified into four macro-categories. The first group of studies, namely ‘quantitative metrics’, which is also the most numerous (66% of the studies), attempts to propose a simplistic assessment of the impact of social media engagement, based on the number of comments, likes, shares, followers etc. (Khan et al., 2019 ; Medjani et al., 2019 ; Yoon et al., 2018 ).

Social media engagement metrics used in the 41 articles

The second group of studies (17% of the studies), namely ‘normalised indexes’, provide a quantitative evaluation of the engagement a content generates in relation to the number of people to whom that content has been displayed. In this way, it is possible to obtain an average measure of the users’ engagement, dividing the total actions of interest by the total number of posts (Osokin, 2019 ; Zanini et al., 2019 ), the number of followers (Vlachvei & Kyparissi, 2017 ) or the number of people reached by a post (Muñoz-Expósito et al., 2017 ; Rossmann et al., 2016 ).

In a more complex and detailed way, studies from the third group (10% of the studies) identify social media engagement metrics developing ‘set of indexes’. For example, Li et al. ( 2019 ) use three social media metrics to measure engagement in the casual-dining restaurant setting: rates of conversation, amplification, and applause. In detail, conversation rate measures the number of comments or reviews in response to a post, amplification rate measures how much online content is shared, and applause rate measures the number of positive reactions on posts. Similarly, drawing from previous literature, Mariani et al. ( 2018 ) develop three social media metrics, namely generic engagement, brand engagement, and user engagement. Authors calculated these metrics by assessing different weights to different interaction actions, to emphasise the degree of users’ involvement implied by the underlying activities of respectively liking, sharing, or commenting.

Despite their great diffusion among academics and practitioners, some scholars (7% of the studies) argue that quantitative metrics are not enough to appreciate the real value of customer engagement on social media, and a qualitative approach is more suitable. For example, Abuljadail and Ha ( 2019 ) conducted an online survey of 576 Facebook users in Saudi Arabia to examine customer engagement on Facebook. Rogers ( 2018 ) critiques contemporary social media metrics considered ‘vanity metrics’ and repurpose alt metrics scores and other engagement measures for social research—namely dominant voice, concern, commitment, positioning, and alignment—to measure the ‘otherwise engaged’.

Social media engagement brand-related activities

When measuring social media engagement, scholars dealt with different social media actions that can be classified (Table ​ (Table7) 7 ) according to the three dimensions of the COBRA model (Consumer Online Brand Related Activities): consumption, contribution, or creation (Schivinski et al., 2016 ).

Dimensions of the COBRA model and related frequencies

In a total of 41 articles reviewed, the most investigated dimension by researchers is contribution, i.e. when a customer comments, shares, likes a form of pre-existing brand content (e.g., Buffard et al., 2020 ; Khan et al., 2019 ). Its popularity among the studies may be due to its interactive nature of “liking” and “commenting”, which can be said to be the most common behaviour exhibited across social media platforms and often one of the most manageable interactions to obtain data. Additionally, studies that include creation in the measurement of social media engagement consider posting/publishing brand-related content, uploading brand-related video, pictures, audio or writing brand-related articles (e.g., Zanini et al., 2019 ). Among the sampled papers, the least investigated dimension of the COBRA model is consumption, considered by only seven studies (e.g., Colicev et al., 2018 ; Oh et al., 2017 ). It considers viewing brand-related audio, video, and pictures, following threads on online brand community forums or downloading branded widgets.

Dimensions have been investigated individually, for example, just considering the number of likes or comments (Khan et al., 2019 ; Yoon et al., 2018 ), or jointly using composite indicators, as in the case of Oviedo-Oviedo-García et al., 2014 ).

This research presents fresh knowledge in the academic debate by providing an overarching picture of social media engagement, framing the phenomenon conceptually and offering a lens to interpret platforms and measuring tools. Conceptual and empirical studies tried to define, conceptualise, and measure social media engagement in diverse ways from different fields of research. They increased the gap between academia and managerial practice, where the topic of social media engagement metrics seems to be much more consolidated. The paper contributes to the academic debate on social media engagement, presenting continuity and discontinuity elements between different fields of enquiry. It also offers avenues for future research that both academics and marketers should explore. It also provides insights and guidance to practitioners on modelling and managing social media engagement.

Theoretical contribution

The article offers some theoretical contributions to this relatively young research field through the systematic literature review approach.

Firstly, the paper confirms the multidimensional and polysemic nature of engagement, even in the specific context of social media platforms, in continuity with the academic customer engagement research (Brodie et al., 2013 ; Hollebeek et al., 2016 ; So et al., 2016 ; Vivek et al., 2012 ). The concept of social media engagement can be traced back to three dimensions of analysis (Van Doorn, 2010 )—affective, cognitive, and behavioural—and some empirical studies measure it as such (Dessart, 2017 ; Vivek et al., 2014 ). However, the behavioural dimension is still the most used proxy to measure users’ level of engagement. Similarly, marketers and social media platforms have focused on behavioural interactions associated with likes, comments and sharing when reporting engagement metric (Peltier et al., 2020 ). What is worth pointing out is that emotional and cognitive dimensions are also essential components of social media engagement and should be adequately addressed by future research.

Secondly, strictly related to the first point, the paper suggests the COBRA model (Schivinski, 2016 ) as a conceptual tool to classify and interpret social media engagement from the behavioural perspective. Social media engagement can be manifested symbolically through actions (Barger et al., 2016 ; Oh et al., 2017 ; Van Doorn et al., 2010 ) that can be traced back to the three dimensions of consumption, contribution and creation (Schivinski et al., 2016 ). However, it is worth pointing out that not all these actions determine the same level of engagement. When measuring social media engagement, researchers should pay attention not only to ‘contribution’ but also to ‘consumption’ and ‘creation’, which are important indicators of the attention a post receives (Oviedo-Garcìa, 2014 ; Schivinski et al., 2016 ), giving them a different weight. It becomes even more important if considering that the same social networks provide different weights to users' actions. For example, in several countries, Instagram has tested removing the like feature on content posted by others, although users can still see the number of likes on their posts. YouTube has also decided to stop showing precise subscriber counts and Facebook is experimenting with hiding like counts, similar to Instagram.

Thirdly, the paper presents some of the key metrics used to evaluate social media engagement identifying quantitative metrics, normalised indexes, set of indexes and qualitative metrics. Although all indicators are based on the interaction between the user and the brand, as the literature suggests (Barger et al., 2016 ; Oviedo-Garcìa, 2014 ; Vivek et al., 2014 ), the paper argues that different metrics measure diverse aspects of social media engagement and should be used carefully by researchers. Despite the conceptual and qualitative research on the topic, even the most recent metrics offer measurements that do not allow engagement to be widely represented in its multidimensional and polysemic nature (Oviedo-García et al., 2014 ; Peltier et al., 2020 ). To get a deeper understanding of the construct, researchers should also consider some of the most recent advances in business practice. As an example, more and more practitioners have the chance to measure engagement by tracking the time spent on content and web pages to blend the different types of material, such as pictures, text, or even videos. Also, cursor movements, which are known to correlate with visual attention, and eye-tracking, can provide insights into the within-content engagement.

Managerial implications

Even if the topic of social media engagement seems to be more consolidated in business practice, this study also provides valuable implications for practitioners. Particularly, the findings shed light on the nature of social media engagement construct and on how metrics can be an extremely useful tool to evaluate, monitor, and interpret the effectiveness of social media strategies and campaigns.

This research offers a strategic-operational guide to the measurement of social media engagement, helping marketers understand what engagement is and choose the most effective and suitable KPIs to assess the performance and success of their marketing efforts. In this sense, marketers should accompany traditional metrics, such as likes, comments and shares, with new metrics capable of better capturing user behaviours.

Marketers also need to realise that engagement is a complex construct that goes beyond the simple behavioural dimension, encompassing cognitive and emotional traits. As a result, in some cases, the so-called “vanity metrics” could fail in fully representing all the aspects of social media engagement. In these cases, it should be accompanied by qualitative insights to analyse what users like to share or talk about and not merely look at likes, comments, and shares counts.

Limitations and future research

This research is not without limitations. First, the systematic literature review only includes English articles published in Journals. As social media engagement and engagement metrics are emerging research topics, conference proceedings and book chapters could also be included to deepen the understanding of the subject. Second, this research was conducted on the database Scopus of Elsevier for the keywords “social media engagement metrics”. Researchers could use a combination of different databases and keywords to search for new contributions and insights. Third, although the paper is based on a systematic literature review, this methodology reveals the subjectivity in the social sciences.

As this is a relatively young field of research, a further academic investigation is needed to overcome the limitations of the study and outline new scenarios and directions for future research. In addition, considering the growing importance of social media, there is value in broadening the analysis through additional studies. Future marketing research could use mixed approaches to integrate the three dimensions of social media engagement, linking qualitative and quantitative data. Advanced sentiment web mining techniques could be applied to allow researchers to analyse what users like to share or talk about and not merely look at likes, comments, and shares as the only metrics (Peltier et al., 2020 ).

Although Facebook and Twitter are the most used social network by brands, and the most significant part of the literature focuses on these two platforms, researchers should not forget that there are new and emerging social media in different countries (e.g., TikTok, Clubhouse). They already represent a hot topic for practitioners and are calling scholars to define new metrics to measure engagement. Additionally, as the use of social media increased during the COVID-19 pandemic, future research should take this into account to better understand social media engagement across different social media platforms.

Open access funding provided by Università Parthenope di Napoli within the CRUI-CARE Agreement.

Publisher's Note

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Contributor Information

Mariapina Trunfio, Email: ti.eponehtrapinu@oifnurt .

Simona Rossi, Email: [email protected] .

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Social Media ROI and Value Analysis chapter 2  lesson 2.4

Measuring Your Social Activity

Apply quantitative and qualitative measurement to your social media strategy..

Advance your social media skills and career

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Course Information

Course Introduction

Take our Social Media ROI and Value Analysis course and learn the strategies, tools, and tactics to more effectively measure and demonstrate the ROI of social media marketing efforts.

Video Transcript | Intro

In order to prove its value, the impact your social media strategy has on your business must be measurable  (here are some metrics that are ). By measuring your results against your objectives at regular intervals, you can identify what strategies and tactics work well, those that don’t, and adjust course as necessary. This ensures that resources such as time, payroll and other expenditures are used wisely. 

When to Select Metrics

The best time to think about how you’ll measure the success of any given strategy is in the development stage. Before you undertake a certain strategy, take the time to establish baselines, targets and benchmarks, so when it comes time to report, you can clearly articulate your progress. 

Quantitative vs Qualitative

The two types of measurement to consider for your KPIs are Quantitative and Qualitative .

Quantitative

Quantitative measurement focuses on numerical values and their growth or decline over time. A good way to think about quantitative metrics is through a framework of base, reach, engagement, and conversion . For example, if you’re a healthy grocer, you may be considering a content strategy that involves publishing four e-books about healthy cooking. First, take a look at your base or potential audience size, in this case, your Facebook and Twitter followers. As you release the books, track the reach of the posts through Facebook Insights and Twitter Analytics. Next, analyze the engagement the posts recieve - are you getting a lot of comments and shares on this content? Lastly, track the number of conversions, or in this case, the number of downloads. If you see growth in these numbers with every release, you can infer that your content strategy is resonating with your customer base and you can look for ways to optimize any problematic areas.

Base and re ach numbers are important as they provide guidance on what kind of strategies and tactics you want to focus on. If you only have an audience of 100 people, dedicating resources to developing high quality e-books may not be the best first step. However, engagement and conversion are much more meaningful metrics as they indicate the actions taken on your content, so even if your base audience isn’t very large but a lot of shares and conversation are happening around your content, it’s indicative of success.

The metrics you may want to consider in your quantitative KPIs are follower growth, engagement rate and conversions such as email sign ups, qualified leads and sales transactions.

Qualitative

In contrast, qualitative measurement is a more nuanced approach to collecting insight and is best done on a post by post basis. For example, you may have set a goal to increase interactions on your brand page. And while the number of comments on your content has gone up, it’s necessary to also examine the nature of the comments. Do they include many positive expressions of love for your products? Or is there a lot of disgruntled feedback expressing frustration with your service? Or lets say you’ve set a goal to to connect with influencers in your industry. Are those relationships bringing you new customers that are interested in your brand? Lastly, establish reporting intervals that make sense for your strategy. Analyzing variations in your follower growth every day won’t give you a clear picture of your success - look at the trend over a period of weeks. Likewise, give your content strategy some time to take hold as you’ll need more than a few days worth of data to make an intelligent assessment of its performance.

Looking for more? Checkout our blog and learn 19 great social media metrics and how to track them .

Want to be a social pro?