Challenges in Studying Depression and Social Media

The relationship between social media, depression, and anxiety is a challenging area of research for several reasons. As illnesses that wax and wane over time, assessments of depression or anxiety at a single time point may not fully capture the illness experience. A critical approach is to use measurements of mental illness that are shown to be valid measurements of the illness in question, such as the Center for Epidemiologic Studies.

The Depression Scale for Children (CES-DC), the Patient Health Questionnaire-9 (PHQ 9), the Generalized Anxiety Disorder scale – 7 (GAD 7), and the Screen for Child Anxiety Related Emotional Disorders (SCARED) are all empirically supported measures of depression or anxiety in adolescents and young adults (Cannon et al., Reference Cannon, Tiffany, Coon, Scholand, McMahon and Leppert2007; Keles et al., Reference Keles, McCrae and Grealish2019; Kroenke et al., Reference Kroenke, Spitzer and Williams2001; Piteo & Ward, Reference Piteo and Ward2020; Richardson et al., Reference Richardson, McCauley and Grossman2010; Weissman et al., Reference Weissman, Orvaschel and Padian1980). Despite the availability of valid and reliable screening tools for depression and anxiety, some studies have not employed such tools and have selected instead ad hoc measurement tools, making the results of these studies difficult to interpret (Twenge & Campbell, Reference Twenge and Campbell2019).

A second challenge when studying depression and social media is determining a measurement approach for social media use. Most commonly, studies focus on quantity of social media use in terms of hours or minutes. Screen time is typically measured using self-reported estimates, which are often inaccurate (Ellis, Reference Ellis2019; Moreno, Jelenchick, et al., Reference Moreno, Christakis and Egan2012). Other studies have employed passive observation to understand screen time, which involves asking a participant to download an application on their phone to track their social media use (Messner et al., Reference Messner, Sariyska and Mayer2019). However, these studies tend to capture time on a specific device, and adolescent technology use is known to incorporate multiple devices. It is recommended that future studies focus on other aspects of adolescents’ technology experiences, such as quality or importance placed on use. These measurement approaches are less common, and present new ways to examine media’s relationship to adolescent health.

Third, many studies examining social media use, depression, and anxiety do not focus on normative social media use. There is a wealth of studies measuring problematic social media use or social media “addiction,” specifically, as opposed to various qualities of normative use (Duradoni et al., Reference Duradoni, Innocenti and Guazzini2020; Hussain et al., Reference Hussain, Wegmann, Yang and Montag2020). While these constructs are relevant to adolescent mental health, positive associations between problematic social media use and mental illness may not apply to normative social media use (Przepiorka & Blachnio, Reference Przepiorka and Blachnio2020). Thus, much of the research on social media use and depression or anxiety among adolescents actually comments on nonnormative use, and the implications for the general population of adolescents cannot be inferred.

Key Hypotheses on the Relationship between Social Media, Depression, and Anxiety

As we consider several key hypotheses in the literature on the relationship between social media screen time, depression, and anxiety, we ask you to keep in mind the measurement and study design issues that may influence these study findings.

The first hypothesis posits that there is a positive linear relationship between social media, depression, and anxiety. That is, as social media use increases, so does risk for anxiety and depression. From a cognitive theoretical perspective, it may be that exposure to certain stimuli on social media (e.g., a photo-shopped image, a photo from a party to which one was not invited, a heartbreaking news story) might activate or reinforce existing negative beliefs about oneself (“I’m worthless”) or the world (“everything is out of control”). Further, time spent on social media might displace time spent on other behaviors, behaviors that may have resulted in mental health-promoting thoughts (e.g., “I have a knack for piano” or “I’m a good teammate”). The “crowding out” hypothesis explains positive associations between depression and screen time by saying that screen time is related to depression when it occurs at the expense of other beneficial activities (McDool et al., Reference McDool, Powell, Roberts and Taylor2020; Twenge, Joiner, Martin, & Rogers, Reference Twenge, Joiner, Martin and Rogers2018). As discussed, multiple systematic reviews support a weak positive relationship between social media use and adolescent depression or anxiety. However, because the studies reviewed are often cross-sectional, it is difficult to ascertain whether social media use causes depression or anxiety, or whether the presence of anxiety or depression makes one more prone to use social media (and less prone, for example, to activities such as exercise, in-person socialization, vocational pursuits, or recreation). Certain studies have detected greater risk for anxiety and depression after a certain threshold of social media use is met, which has been cited as three hours per day (Riehm et al., Reference Riehm, Feder and Tormohlen2019), four hours per day (Barman et al., Reference Barman, Mukhopadhyay and Bandyopadhyay2018), and nearly five hours per day (O’Keeffe et al., Reference O’Keeffe and Clarke-Pearson2011).

A second hypothesis states that a U-shaped curve best captures the relationship between internalizing symptoms and social media use, with negative mental health associated with very low or very high use. There is some empirical support for this hypothesis (Belanger et al., Reference Belanger, Akre, Berchtold and Michaud2011; Liu et al., Reference Liu, Wu and Yao2016; Moreno, Jelenchick, et al., Reference Moreno, Christakis and Egan2012). From a multicultural perspective on anxiety or depression, adolescents with nonnormative (very high or very low) use may be in other ways alienated from the dominant culture. High social media use might indicate a lack of participation in other areas of life, while very low social media involvement might signify estrangement from what now constitutes a developmentally appropriate activity: social media. Further, youth at the very high and very low ends of social media may be socioeconomically disadvantaged; they may live in low-resource settings, without consistent access to the Internet, or in contexts where social media use is the only activity available. Financial hardship, or disempowerment, is associated with depression and anxiety (Selfhout et al., Reference Selfhout, Branje, Delsing, ter Bogt and Meeus2009). Further, the U-shaped curve may also result from the stimuli encountered on social media. Frequent social media use puts minority youth at risk of daily, and sometimes hourly, evidence of minority oppression in the form of news media. Similarly, those who choose to stay off of social media may be trying to avoid these stimuli. Importantly, detecting the U-shaped curve requires the use of analytic approaches beyond traditional linear regression. Therefore, it is possible that studies presumed to support a linear positive relationship actually support the U-shaped curve hypothesis.

A third hypothesis is that there is no relationship between social media and depression, or social media and anxiety. More specifically, this hypothesis suggests that there is no population-level clinically significant relationship between these illnesses and social media use. Rather, certain subgroups may be at elevated risk for depression and anxiety due to social media use (Radovic et al., Reference Radovic, Gmelin, Stein and Miller2017) while for others there is no relationship, and for still others social media use actually promotes mental health. At a population level, this variability cannot be detected. From a multicultural perspective, this makes sense; one would never expect to find a “population level” effect of social media on depression or anxiety, in a world where oppression (and consequential mental illness) is not equally distributed. A white, cisgender child from a middle-class household is likely to see aspects of their own life reflected online; in contrast, those with any number of minority identities may feel “othered” by going online. The “no relationship” hypothesis is supported by several studies that have identified no population-level statistically significant association between social media use and depression or emotional problems (Anjum et al., Reference Anjum, Hossain, Sikder, Uddin and Rahim2019; Fardouly et al., Reference Fardouly, Magson, Johnco, Oar and Rapee2018; Ferguson, Reference Ferguson2021; Jelenchick et al., Reference Jelenchick, Eickhoff and Moreno2013). Given the additional difficulty of publishing statistically insignificant findings, it may be that more studies have detected the “null” relationship than have been published.

The fourth and final hypothesis is “it’s complicated,” which mirrors a common relationship status adolescents themselves like to use. The majority of studies focus on screen time as a measure of social media use, and it may be that other aspects of social media use relate more to depression and anxiety than does screen time. This hypothesis finds theoretical support from a cognitive perspective of anxiety and depression. Different online behaviors generate different thoughts, thoughts that may either reinforce or challenge beliefs about the self. Cognitive theory further states that avoidance is a key behavior maintaining illnesses like anxiety and depression. If an adolescent scrolls through social media primarily as a means of avoiding – rather than confronting – dreaded stimuli, social media use would likely contribute to the maintenance of anxiety. In contrast, a youth who uses their Finsta (Fake Instagram) to air the “less acceptable” sides of themselves may learn over time, through this online “validity testing,” that what they thought were unacceptable features are warmly received by peers. Last, children who already have depression or anxiety might assign social media different worth; they may compulsively check social media for evidence in support of their own worth, while a child who affords social media no such power would not feel this attachment toward use.

Few studies have been designed to test Hypothesis 4; that is, few assess the specific features or intentions underlying adolescents’ social media use. As such, it remains to be seen how the specific uses of social media differentially relate to depression and anxiety. A recently developed tool to measure the quality of use, the Adolescents’ Digital Technology Interactions and Importance scale, is a promising means of evaluating the importance that adolescents assign to different affordances of technology, including technology to bridge online/offline experiences and preferences, technology to go outside one’s identity or offline environment. and technology for social connection (Moreno et al., Reference Moreno, Binger, Zhao and Eickhoff2020). Tools like these are needed to understand the nuanced relationship between social media use on depression and anxiety in adolescents.

Where Are We Now?

A 2020 paper synthesized data from systematic reviews and meta-analyses between 2014 and 2019. This included cohort, longitudinal, and ecological momentary assessment studies (Odgers & Jensen, Reference Odgers and Jensen2020). They authors concluded that most research has been correlational, focused on adults, and has led to a mix of conflicting results. They also observed that most studies report “small associations … that do not offer a way of distinguishing cause from effect and, as estimated, are unlikely to be of clinical or practical significance” (p. 336). It has become increasingly evident that the current literature may not support Hypothesis 1, but that Hypotheses 2–4 above remain available for more nuanced and high-quality studies to address.

Risk 1: Adverse Online Experiences

Risk 2: Highly Visual Social Media

Exposure to certain highly visual social media (HVSM) is a risk factor for depression and anxiety in some adolescents. Undoubtedly, visual social media can be positive. However, in this section, we use HVSM as shorthand for risky HVSM – for example, media that enables users to modify or “improve” their appearance before uploading (Weissman et al., Reference Weissman, Orvaschel and Padian1980). Many of the most popular social media platforms for adolescents (Instagram, Snapchat, and most recently TikTok) are visual platforms that allow for appearance modification (Anderson & Jiang, 2018). While use of HVSM has also been associated with disordered eating, the relationship between social media and disordered eating is covered elsewhere in this handbook. The present section will explore the relationship between HVSM and depression and anxiety.

Some of the thoughts and feelings that characterize depression and anxiety may be triggered by exposure to HVSM. Feelings of worthlessness or fears of inadequacy may be sparked or exacerbated by frequent exposure to visually “perfected” images.

Youth may compare themselves to the people they “follow,” and find themselves lacking (Marengo et al., Reference Marengo, Longobardi, Fabris and Settanni2018). From a multicultural perspective, visual media are uniquely able to transmit messages from the dominant culture: how to look, how to behave, and the types of people and behaviors that are deserving of praise.

People with existing tendencies toward poor body image are at particular risk of depression or anxiety as a result of exposure to HVSM (Kelly et al., Reference Kelly, Zilanawala, Booker and Sacker2018; Marengo et al., Reference Marengo, Longobardi, Fabris and Settanni2018). The tendency to be bothered if tagged in an unflattering picture is associated with depression among college students (Robinson et al., Reference Robinson, Bonnette and Howard2019). HVSM also allows for taking, editing, and uploading pictures of oneself online, which has been linked to anxiety in college students (Mills et al., Reference Mills, Musto, Williams and Tiggemann2018; Wick & Keel, Reference Wick and Keel2020). Appearance-related social comparisons, which are uniquely afforded by HVSM, have been associated with depression (Choukas-Bradley et al., Reference Choukas-Bradley, Nesi, Widman and Galla2020; Hawes et al., Reference Hawes, Zimmer-Gembeck and Campbell2020). Engaging with “pro-ana” (pro-anorexia) media or “thinspiration,” which often contains depictions of thinness, “clean” foods, and calorie-deficient diets, has been linked with depression and anxiety (Fitzsimmons-Craft et al., Reference Fitzsimmons-Craft, Krauss, Costello, Floyd, Wilfley and Cavazos-Rehg2020; Jennings et al., Reference Jennings, LeBlanc, Kisch, Lancaster and Allen2020). From a cognitive perspective, social media may reinforce the negative belief that one’s worth is tied to bodily appearance. If viewers perceive themselves as failing to meet these standards, depressive or anxious symptoms may increase. It may also be that adolescents who are anxious or depressed and dissatisfied with their bodies are more likely to engage with HVSM in pursuit of information (a like, comment, or share) challenges or confirms of their self-beliefs.

However, despite the theoretical justification and some empirical support, a recent scoping review on HVSM and depression found that the relationship between HVSM and depression is inconclusive (McCrory et al., Reference McCrory, Best and Maddock2020). It may be that the relationships between HVSM and depression are simply better explained by other variables. The absence of an effect may also be due in part to a lack of research studies designed to detect this effect: research on social media use does not always distinguish between HVSM and other social media, let alone differentiate between positive and negative forms of visual social media. Further, research typically relies on quantitative methods to evaluate the relationship between HVSM and depression and anxiety, which lacks richness and possibility for participants to elaborate on their experiences.

The relationship between social media use and depression or anxiety also may be dependent on a variety of factors that increase risk for internalizing symptoms. Several of these potential moderators are discussed below.

Fear of Missing Out

Fear of missing out, or FOMO, is defined as the “pervasive apprehension that others might be having rewarding experiences from which one is absent” (Przybylski et al., Reference Przybylski, Murayama, DeHaan and Gladwell2013, p. 1841). FOMO in adolescents has been independently associated with both depression and anxiety and, less consistently, with social media use (Barry et al., Reference Barry, Sidoti, Briggs, Reiter and Lindsey2017; Franchina et al., Reference Franchina, Vanden Abeele, van Rooij, Lo Coco and De Marez2018; Przybylski et al., Reference Przybylski, Murayama, DeHaan and Gladwell2013). Given that social media is a place where the (often enviable) experiences of others are constantly on display, it is not difficult to explain the link between social media use and FOMO. More complex is to explain why adolescents who are higher in FOMO are more at risk for depression or anxiety as a consequence of social media use (Fabris et al., Reference Fabris, Marengo, Longobardi and Settanni2020). It may be that, for adolescents with tendencies toward FOMO, exposure to friends’ and influencers’ “highlight reels” creates feelings of worthlessness, worry, and dissatisfaction with one’s own daily life. From a cognitive perspective, scrolling through social media might trigger automatic thoughts, such as “no one invites me to anything” or “my life sucks in comparison with hers.” The action of scrolling through social media may also be motivated by FOMO, as depressed or anxious adolescents search for evidence to assuage or confirm the belief that they are missing out.

At present, research is mixed on whether FOMO affects the relationship between social media use and depression or anxiety in adolescents. While there is ample evidence that FOMO is associated with problematic social media use and problematic smartphone use (Franchina et al., Reference Franchina, Vanden Abeele, van Rooij, Lo Coco and De Marez2018; Przepiorka & Blachnio, Reference Przepiorka and Blachnio2020), there is insufficient evidence that FOMO explains or strengthens the relationship between typical use and depression or anxiety at a population level. In some cases, this absence of an effect may be due to insufficient measures of social media; as discussed, insufficient measures focus solely on time spent, rather than activities performed or experiences had while online.

Sleep

Sleep is critical to consider in any study of social media and mental illness. Indeed, sleep is perhaps the most consistently supported variable to explain the relationship between social media use and depression or anxiety (Alonzo et al., Reference Alonzo, Hussain, Anderson and Stranges2019; Kelly et al., Reference Kelly, Zilanawala, Booker and Sacker2018; Lemola et al., Reference Lemola, Perkinson-Gloor and Hagmann-von Arx2015; Oshima et al., Reference Oshima, Nishida and Shimodera2012). However, studies suggesting that sleep explains the relationship between social media use and depression and anxiety have been largely cross-sectional, meaning directionality is subject to interpretation. Before exploring these hypotheses, it is important to note that poor sleep (e.g., sleeping too much or too little, trouble falling asleep) is actually a symptom of both depression and anxiety. Thus, sleep trouble is central to the experience of depression and anxiety for many people.

One hypothesis suggests that social media use causes sleeplessness, which in turn causes or exacerbates symptoms of depression or anxiety. Social media may cause sleeplessness by displacing sleeping hours and delaying bedtime (Quante et al., Reference Quante, Khandpur, Kontos, Bakker, Owens and Redline2019). The blue light exposure associated with social media use may disrupt melatonin and cause wakefulness (Blass et al., Reference Blass, Anderson, Kirkorian, Pempek, Price and Koleini2006; Levenson, Reference Levenson2016; Wahnschaffe et al., Reference Wahnschaffe, Haedel and Rodenbeck2013). It may be that social media is uniquely stimulating as compared to nonsocial online activities, given that it contains a wealth of self-relevant social information and capacity for social interaction.

A second hypotheses interprets the association in the reverse direction. That is, it may be that adolescents who are already depressed or anxious are more prone to sleep disruption. In turn, disrupted sleep leads to social media use, perhaps as adolescents seek distraction or support online. However, this hypothesis is contentious. Some research has shown that the relationship between poor sleep and social media use cannot be explained by existing depression or anxiety (Twenge & Campbell, Reference Twenge and Campbell2019; Woods & Scott, Reference Woods and Scott2016). Thus, all teens – not just those who are anxious or depressed – may benefit from finding soothing activities that are less stimulating than social media, and from following the American Academy of Pediatrics’ recommendations to keep devices out of bedrooms at nighttime (Moreno et al., Reference Moreno, Chassiakos and Cross2016).

Gender

Some studies have suggested that gender may influence the effect of social media use on depression and anxiety. Several individual studies have found that adolescent females are more likely than males to experience depression associated with social media use (Barthorpe et al., Reference Barthorpe, Winstone, Mars and Moran2020; Kelly et al., Reference Kelly, Zilanawala, Booker and Sacker2018; Twenge, Joiner, Rogers, & Martin, Reference Twenge, Joiner, Martin and Rogers2018; Twenge & Martin, Reference Twenge and Martin2020; Waterloo et al., Reference Waterloo, Baumgartner, Peter and Valkenburg2018). From a multicultural theoretical perspective, the potentially unique negative effects of social media for adolescent females finds support. The design of social media (including affordances for appearance feedback and negative self-comparison) may be uniquely oppressive to adolescent females, particularly females of color, who face great pressure from the dominant culture to conform to a certain beauty ideal (Coyne et al., Reference Coyne, Padilla-Walker, Holmgren and Stockdale2019; Messner et al., Reference Messner, Sariyska and Mayer2019). Under multicultural theory, symptoms of anxiety and depression (e.g., guilt, worry, restlessness, diminished pleasure) should not be understood as reflecting psychopathology, but instead reflecting reasonable reactions to the dominant culture.

However, summaries of research found in recent systematic reviews conclude no consistent effect of gender on the relationship between social media use and internalizing symptoms, and typically conclude that more research is needed on this topic (Keles et al., Reference Keles, McCrae and Grealish2019; Piteo & Ward, Reference Piteo and Ward2020). A meta-analysis evaluated 67 independent samples of a combined 19,652 participants, and found that the effect of gender on the relationship between time spent on social media and psychological well-being was insignificant (Huang, Reference Huang2017). A recent review of reviews reported something slightly different: after controlling for confounding variables, the least-depressed adolescent females in the sample had “slightly increased risk for depressive symptoms with daily social media use”(Odgers & Jensen, Reference Odgers and Jensen2020, p. 341).

While the moderating effect of gender is inconclusive, research supports that males and females do use social media differently (Boyle et al., Reference Boyle, LaBrie, Froidevaux and Witkovic2016). Research suggests that females spend more time online and are more likely to say they are nearly constant online users compared to adolescent males (50% vs. 39%) (Anderson & Jiang, 2018; Duggan, Reference Duggan2013). Females are more likely than males to use social media for self-expression, including expression of joy and pride, as well as expression of negative emotions, such as worry, stress, and depression (Egan & Moreno, Reference Egan and Moreno2011; Moreno, Christakis, et al., Reference Moreno, Christakis and Egan2012; Waterloo et al., Reference Waterloo, Baumgartner, Peter and Valkenburg2018). Given that females compared to males are likely to use online platforms for emotional expression, it is also possible that females with depression turn to social media more readily for support. Thus, it may be that social media use does not predict depressive symptoms, but greater depressive symptoms predict more frequent social media use, especially among females (Heffer et al., Reference Heffer, Good, Daly, MacDonell and Willoughby2019).

Benefits of Social Media for the Typical Adolescent

Typical adolescents report using social media in ways that may ward off depressive and anxious symptoms, both by seeking information related to these symptoms and by finding support and connection online (Rideout et al., Reference Rideout, Fox and Trust2018). Research has shown that adolescents often feel happy, amused, or closer to friends while using social media (Weinstein, Reference Weinstein2018; Wenninger et al., Reference Wenninger, Krasnova and Buxmann2019). While studies over the years have repeatedly demonstrated social networks and support contribute to overall and mental health, too much online social networking may put one at risk for negative experiences, cognitions, and emotions (Ahn, Reference Ahn2012; Longobardi et al., Reference Longobardi, Settanni, Fabris and Marengo2020; Negriff, Reference Negriff2019; Rajani et al., Reference Rajani, Berman and Rozanski2011). Those who experience isolation, stress, and unmet needs in their offline worlds may find corrective experiences or buffering effects by going online (Nick et al., Reference Nick, Cole, Cho, Smith, Carter and Zelkowitz2018; Prochnow et al., Reference Prochnow, Patterson and Hartnell2020). One qualitative interview study found that young people naturally and organically developed close-knit communities of close friends, often in the form of private Instagram accounts, on which privacy was a priority and emotional disclosure was safe and commonplace (Gibson & Trnka, Reference Gibson and Trnka2020). These findings support the positive, adaptive, and strategic use of social media for typical adolescents.

Benefits of Social Media for Adolescents with Depression and Anxiety

Adolescents with depression and anxiety use social media differently than their mentally well peers (Radovic et al., Reference Radovic, Gmelin, Stein and Miller2017). Thus, the commonly cited associations between social media use, depression, and anxiety may be explained in part by the unique offerings of social media for depressed and anxious youth. Youth have described feeling motivated to share their depression online because it is perceived as easier than sharing in-person, and because they are hoping to connect with others who understand and have had similar experiences (Carey et al., Reference Carey, Carreiro and Chapman2018; Rideout et al., Reference Rideout, Fox and Trust2018). A systematic narrative review of 28 studies on online help-seeking among adolescents found that adolescents commonly cited anonymity, ease of access, and sense of community as driving motivators to find mental health support online (Pretorius et al., Reference Pretorius, Chambers and Coyle2019). Thus, youth who are already experiencing depression and anxiety may find particular mental health benefits by going online.

Benefits of Social Media for Marginalized Adolescents

Social media may be particularly beneficial to marginalized adolescents, for whom it may not be safe, feasible, or appealing to find support in the offline world. This may include homeless youth, as well as racial, sexual, and gender minority youth. From a multicultural perspective, the possibility of finding support for mental illness while remaining anonymous may help adolescents to overcome shame and stigma, imposed by the dominant culture, around help-seeking. Further, in the wide world of online support, adolescents may be more likely to find support that is tailored to their cultural values and worldview.

A scoping review of 19 studies on individuals experiencing homelessness and their social media use found that for homeless youth, seeking help online minimized barriers and prejudices often encountered in-person (Calvo & Carbonell, Reference Calvo and Carbonell2019). Perhaps for the same reason, sexual and gender minority youth are significantly more likely than straight and cisgender youth to go online for information about depression and anxiety (Marengo et al., Reference Marengo, Longobardi, Fabris and Settanni2018; Rideout et al., Reference Rideout, Fox and Trust2018). Transgender youth have affirmed that social media is a place to garner emotional, informational, and “appraisal” support, or the validation in seeing their same experience reflected in others (Selkie et al., Reference Selkie, Adkins, Masters, Bajpai and Shumer2020). In sum, both qualitative and quantitative research studies support that homeless youth, as well as sexual and gender minority youth, use social media to find affirming communities and avoid discrimination (Craig et al., Reference Craig, McInroy, McCready and DeCesare2015; Escobar-Viera et al., Reference Escobar-Viera, Shensa and Hamm2020; Jenzen, Reference Jenzen2017).

Another area of study has focused on experiences of racial minority youth. Perhaps due to a lack of culturally competent healthcare providers offline, black youth are more likely to go online to share their health stories (Rideout et al., Reference Rideout, Fox and Trust2018). One study interviewed 25 racially and economically diverse undergraduate students to understand the empowering and disempowering aspects of social media (Brough et al., Reference Brough, Literat and Ikin2020). Interviewees noted that social media allowed them to find and connect with similar others (e.g., by using the #blackLGBTQ hashtag), as well as to represent their voice both by sharing their own stories and observing as others share theirs. However, the same youth noted that social media can have the opposite effect, encouraging conformity to the dominant culture and exposing them to lifestyles that were not relatable (Brough et al., Reference Brough, Literat and Ikin2020). Thus, while social media may have unique affordances for marginalized youth, its potential to “other” its end users could also worsen mental health symptoms.

With the exception of the studies mentioned above, there is less support for the differential use of social media by racial or ethnic minorities. None of the recent systematic reviews on the relationship between social media use and internalizing symptoms mention race, although two call for more diverse samples (Odgers & Jensen, Reference Odgers and Jensen2020; Orben, Reference Orben2020). This suggests that there is little conclusive evidence on the differential use of social media by race, as well as any differences in associated mental health outcomes, whether positive or negative. Given that certain racial and ethnic groups may have fewer opportunities for culturally competent in-person mental health care and support, it is important to understand how they have built alternative spaces online.

Objectification Theory

Objectification theory was proposed as a framework for explaining the psychological consequences women experience from growing up in a society that sexually objectifies the female body (Fredrickson & Roberts, Reference Fredrickson and Roberts1997). It argues that women and girls in Western society learn to adopt an observer’s perspective of their own bodies – a process called self-objectification – after being exposed to frequent sexual objectification, which reinforces the societal message that a woman’s interpersonal value is based primarily on her physical appearance (Fredrickson & Roberts, Reference Fredrickson and Roberts1997). Self-objectification is linked to body shame, depression, anxiety, and the development of disordered eating (Butkowski et al., Reference Butkowski, Dixon and Weeks2019; Calogero et al., Reference Calogero, Tantleff-Dunn and Thompson2011; Erchull et al., Reference Erchull, Liss and Lichiello2013). Research now suggests that boys and men also experience self-objectification, as they are also exposed to sociocultural appearance pressures and may experience sexual objectification (Vandenbosch & Eggermont, Reference Vandenbosch and Eggermont2013). The act of curating one’s social media profile can be thought of as a behavioral manifestation of self-objectification, as the user is specifically creating content about one’s identity that is meant to be consumed by others (Choukas-Bradley et al., Reference Choukas-Bradley, Nesi, Widman and Higgins2019, Reference Choukas-Bradley, Nesi, Widman and Galla2020, Reference Choukas-Bradley, Roberts, Maheux and Nesi2021). In this way, social media users are encouraged to adopt an observer’s perspective of themselves and post social media content that will elicit positive feedback from their social media audience. Later in this chapter, we discuss specific behaviors and experiences on social media that are associated with self-objectification.

Social Comparison Theory

Adolescents who derive self-esteem from their physical attractiveness are likely to engage in social comparison, evaluating their attractiveness by comparing it to other social media users. Festinger’s (Reference Festinger1954) social comparison theory argues that individuals engage in social comparison in order to estimate their own social status relative to others. While this is a natural process, it can be problematic in the case of physical attractiveness. Festinger’s seminal paper on social comparison theory (Reference Festinger1954) argues that individuals have a tendency to make upward appearance comparisons when evaluating physical attractiveness (i.e., individuals tend to compare themselves to people they perceive as more attractive than themselves), resulting in worse body image. Furthermore, when engaging in social comparison, people try to compare themselves to similar others. Taken together, peers on social media may be perceived as realistic comparison targets, but by presenting highly edited images, these “similar” comparison targets may in fact serve as upward comparison reference groups depicting unattainable attractiveness. Regardless of whether adolescents compare themselves to individuals perceived to be more or less attractive, engaging in social comparison is associated with body dissatisfaction, especially among adolescent girls (Jones, Reference Jones2001). Indeed, social appearance comparisons appear to be a primary mechanism through which social media exerts influence on body image disturbances and disordered eating during adolescence. Later in this chapter, we describe specific features of social media that encourage social comparison.

Tripartite Influence Model

A third theory relevant to understanding social media’s influence on adolescents’ body image and disordered eating is the tripartite influence model (Thompson et al., Reference Thompson, Heinberg, Altabe and Tantleff-Dunn1999), which was developed to explain the mechanisms through which body dissatisfaction originates. This model proposes that through peers, parents, and the media, adolescents are frequently exposed to unattainable standards of beauty. After encountering such exposure, adolescents internalize an unattainable appearance ideal and, like in social comparison theory, engage in appearance comparisons, processes known to lead to greater body dissatisfaction (Keery et al., Reference Keery, van den Berg and Thompson2004; Thompson et al., Reference Thompson, Heinberg, Altabe and Tantleff-Dunn1999). Internalization of an appearance ideal (often the “thin ideal” for adolescent girls and the “muscular ideal” for adolescent boys) refers to the extent to which an individual ascribes to culturally defined standards of beauty. Social media perpetuates these unattainable ideals and encourages social appearance comparisons through comments, images, and interactions that communicate societal expectations for adolescents’ bodies, ultimately fostering body dissatisfaction because these appearance ideals are unattainable for the majority of individuals (Thompson & Stice, Reference Thompson and Stice2001). Given its ubiquity, social media has become a primary source of appearance pressure in adolescents’ lives.

The Transformation Framework

The aforementioned theories were all developed before the advent of social media. However, scholars have recently identified features of social media directly implicated in the development of body image disturbances. The transformation framework argues that widespread adoption of social media among today’s adolescents has fundamentally changed the ways in which they are interacting with one another (Choukas-Bradley et al., Reference Choukas-Bradley, Roberts, Maheux and Nesi2021; Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018a, Reference Nesi, Choukas-Bradley and Prinstein2018b). Here, we discuss three of the seven specific features of the transformation framework that are most relevant to understanding social media’s effects on body image: visualness, publicness, and quantifiability.

First, social media is characterized by visualness and publicness, meaning that users rely on photographs and videos to communicate to broad, public audiences (Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018a). This reliance on visual forms of communication can make adolescents hyperaware of their own physical appearance. Currently, highly visual social media (HVSM) – such as Instagram, Snapchat, and TikTok – is the most popular type of social media among adolescents (Anderson & Jiang, Reference Anderson and Jiang2018). When adolescents use HVSM, they increase their focus on others’ attractiveness, are exposed to unattainable beauty standards, and may engage in appearance-driven self-presentation techniques to elicit positive peer feedback in the form of “likes” or comments. These “likes” and comments represent quantifiability, or the Numerical indicators of popularity and attractiveness indicated by one’s peers and social media audience. We have provided a specific section later in the chapter describing how quantifiability of appearance-based feedback influences adolescents’ body image and disordered eating. Collectively, these features of social media may encourage self-objectification and social comparison.

Selfies

Social media offers adolescents the opportunity to take, edit, and post photos of themselves – “selfies” (Lim, Reference Lim2016). Selfie behaviors, including taking and posting selfies, are relatively common among adolescents (Dhir et al., Reference Dhir, Pallesen, Torsheim and Andreassen2016; McLean et al., Reference McLean, Jarman and Rodgers2019), with nationally representative US data reporting that 45% of adolescents often or sometimes post selfies (Anderson & Jiang, Reference Anderson and Jiang2018). The association between selfie behaviors and body image and disordered eating outcomes is not yet fully understood. Some evidence from adolescent girls and young adult women in China and Australia suggests an association between selfie posting and body dissatisfaction, overvaluation of shape and weight, greater internalization of the thin ideal (McLean et al., Reference McLean, Paxton, Wertheim and Masters2015), greater engagement in appearance comparisons (Mingoia et al., Reference Mingoia, Hutchinson, Gleaves and Wilson2019), self-objectification (Meier & Gray, Reference Meier and Gray2014; Zheng et al., Reference Zheng, Ni and Luo2019), and restrained eating (Niu et al., Reference Niu, Sun, Liu, Chai, Sun and Zhou2020). Among samples with both adolescent boys and girls, posting a selfie is associated with self-objectification (Meier & Gray, Reference Meier and Gray2014), body shame (Salomon & Brown, Reference Salomon and Brown2019), and restrained eating (Wilksch et al., Reference Wilksch, O’Shea, Ho, Byrne and Wade2020). Interestingly, other research has found that disordered eating behaviors are associated with greater avoidance of posting selfies among adolescent boys and girls (Lonergan et al., Reference Lonergan, Bussey and Fardouly2020), and that Singaporean adolescent girls with greater body esteem are more likely to post selfies than those with lower body esteem (Chang et al., Reference Chang, Li, Loh and Chua2019). Some research with adolescent boys and girls in the USA (Nesi et al., Reference Choukas-Bradley, Roberts, Maheux and Nesi2021) and China (Wang et al., Reference Wang, Xie, Fardouly, Vartanian and Lei2019) has found no association between selfie posting and body esteem. Experimental research with adolescent girls and adult women in lab settings shows that those assigned to take and post a selfie to social media report heightened anxiety, less confidence, and feeling less physically attractive afterwards (Mills et al., Reference Mills, Musto, Williams and Tiggemann2018). Notably, these outcomes were found whether participants were uploading an unedited selfie or had the opportunity to edit and choose a preferred selfie, highlighting that simply focusing on one’s appearance and posting it to a semi-public audience may help explain this association. The somewhat conflicting results suggest a need for more research in this area, particularly with mixed-gender samples.

Photo Editing

Social media allows for adolescents to manage their online self-presentation by editing and applying filters to photos before posting. Editing one’s photos and selfies, including applying filters, cropping, and modifying one’s appearance directly, is not uncommon among adolescents and is more common among girls than boys (see McLean et al., Reference McLean, Paxton, Wertheim and Masters2015). Qualitative work suggests that adolescent girls engage in “meticulous backstage planning,” spending hours planning and editing their photos to meet societal beauty norms, a practice that many consider “necessary” to be “pretty enough” online (Chua & Chang, Reference Chua and Chang2016, p. 193). Editing one’s own photos may exacerbate the deleterious effects of social media by encouraging self-objectification, social comparison, and internalization of the thin ideal. Research with adolescents has shown that editing one’s photos is associated with self-objectification, which in turn is linked to appearance anxiety, body shame, negative appearance evaluation (Terán et al., Reference Terán, Yan and Aubrey2020), and body image concerns (Wang et al., Reference Wang, Xie, Fardouly, Vartanian and Lei2019). Photo-editing encourages social appearance comparisons (Mingoia et al., Reference Mingoia, Hutchinson, Gleaves and Wilson2019) and disordered eating behaviors (Lonergan et al., Reference Lonergan, Bussey and Fardouly2020), even when controlling for time on social media and internalization of the thin ideal (McLean et al., Reference McLean, Paxton, Wertheim and Masters2015). Additionally, some qualitative work suggests that, especially for girls, the curation of one’s photos and selfies happens before the editing phase, including scrupulous photo planning and taking of multiple photos to ensure a desired outcome (Chua & Chang, Reference Chua and Chang2016; Mascheroni et al., Reference Mascheroni, Vincent and Jimenez2015), processes that some adolescents girls describe as “work” (Yau & Reich, Reference Yau and Reich2019, p. 203).

Exposure to Others’ Photos

Emerging evidence suggests photo-based social media activity, rather than total time spent on social media, contributes to adolescents’ body image disturbances (Choukas-Bradley et al., Reference Choukas-Bradley, Nesi, Widman and Galla2020; Cohen et al., Reference Cohen, Newton-John and Slater2017; Marengo et al., Reference Marengo, Longobardi, Fabris and Settanni2018; Meier & Gray, Reference Meier and Gray2014). HVSM in particular allows adolescents unprecedented opportunities to view the idealized and edited photos of their peers. Viewing others’ photos on social media is thought to engender risk for disordered eating and body dissatisfaction through internalization of cultural appearance ideals and social appearance comparisons (see Rodgers et al., Reference Rodgers, Slater, Gordon, McLean, Jarman and Paxton2020). Indeed, recent research with adolescents has shown that engaging in social appearance comparisons with others’ photos on social media is associated with body dissatisfaction (Chang et al., Reference Chang, Li, Loh and Chua2019) and disordered eating (Zimmer-Gembeck et al., Reference Zimmer-Gembeck, Webb, Kerin, Waters and Farrell2020), and that monitoring peers’ attractiveness on social media is associated with internalization of cultural appearance ideals (Vandenbosch & Eggermont, Reference Vandenbosch and Eggermont2015). Further, adolescent girls high in trait social comparison (those who engage in greater social comparison than their peers) may be especially vulnerable to the deleterious effects of viewing others’ photos on body image (Kleemans et al., Reference Kleemans, Daalmans, Carbaat and Anschütz2018).

Peer Approval: “Likes” and Comments

Adolescents are also highly attuned to quantifiable metrics of peer approval in the form of “likes,” comments, friends, and followers. Neuroimaging studies have demonstrated greater activation in the brain’s reward circuitry (e.g., the nucleus accumbens) when adolescents view photos that receive high numbers of “likes,” especially when these were their own photos (Sherman et al., Reference Sherman, Payton, Hernandez, Greenfield and Dapretto2016; Sherman, Greenfield, et al., Reference Sherman, Greenfield, Hernandez and Dapretto2018; Sherman, Hernandez, et al., Reference Sherman, Greenfield, Hernandez and Dapretto2018), suggesting that quantifiable approval of one’s online self-presentation may be especially rewarding. Among adolescent girls in Australia, number of friends on social media has been shown to positively correlate with body image concerns (Tiggemann & Slater, Reference Tiggemann and Slater2013) and dieting (Tiggemann & Slater, Reference Tiggemann and Slater2014).

Peer approval can also be conveyed through comments on adolescents’ posts. As expected, longitudinal evidence suggests that social media use generally is associated with more appearance-related peer feedback (i.e., comments) on adolescents’ social media posts, though the same study found that the reception of peer appearance-related feedback is unrelated to body dissatisfaction (de Vries et al., Reference de Vries, Peter, de Graaf and Nikken2016). Interestingly, positive appearance-related comments (compliments) have been implicated in adolescent girls’ self-objectification, possibly more so than negative comments or “teasing” (Slater & Tiggemann, Reference Slater and Tiggemann2015). However, negative appearance-related comments may be linked to adolescent girls’ lower self-esteem and depression and to boys’ tendency to act out (Berne et al., Reference Berne, Frisén and Kling2014). Some work with young adults suggests that the link between social media use and social comparison may be exacerbated by adolescents’ viewing “likes” and comments on others’ posts (Fardouly et al., Reference Fardouly, Pinkus and Vartanian2017; Fox & Vendemia, Reference Fox and Vendemia2016), to which they ostensibly compare their own peer feedback. Some longitudinal work also suggests that more liking and commenting on others’ social media content is associated with decreased appearance self-esteem across development (Steinsbekk et al., Reference Steinsbekk, Wichstrøm, Stenseng, Nesi, Hygen and Skalická2021).

No prior work to our knowledge has examined the experience of receiving or giving likes on adolescents’ disordered eating outcomes specifically, though research with adult women has demonstrated that Facebook use is implicated in the maintenance of disordered eating by providing reinforcement of shape and weight concerns (Mabe et al., Reference Mabe, Forney and Keel2014). Theoretically, if adolescents receive “likes” and comments on photos that have been edited, or promote an idealized version of their appearance, adolescents may infer that they receive positive feedback for altering their appearance, reinforcing their body dissatisfaction. For adolescents who engage in disordered eating, these “likes” and comments may provide reinforcement for disordered eating behaviors, though this should be studied directly in future research. Indeed, social reinforcement plays a role in adolescents’ disordered eating behaviors, and research has demonstrated that adolescent girls in particular encourage dieting and disordered eating among one another, and that girls who engage in disordered eating are more likely to be perceived as popular by their peers, despite having lower body esteem (Lieberman et al., Reference Lieberman, Gauvin, Bukowski and White2001).

Investment in Appearance

Likely due to the sociocultural emphasis on appearance, aspects of adolescent development, features of social media, and, for girls, gender socialization, adolescents are highly invested in how they present themselves online. Although girls report generally placing more importance on appearing attractive online, boys report investment in their online appearance as well (e.g., de Vries et al., Reference de Vries, Peter, Nikken and de Graaf2014; Mingoia et al., Reference Mingoia, Hutchinson, Gleaves and Wilson2019; Yau & Reich, Reference Yau and Reich2019). Investment in one’s selfies, including putting in more effort to take and edit selfies, is associated with greater body dissatisfaction and dietary restraint, even after controlling for overall social media use and internalization of the thin ideal, among adolescent girls in Australia (McLean et al., Reference McLean, Paxton, Wertheim and Masters2015), and with greater appearance comparisons among adolescent girls and boys in Australia (Mingoia et al., Reference Mingoia, Hutchinson, Gleaves and Wilson2019). Work with young adult women is more extensive and finds a similar pattern (e.g., Cohen et al., Reference Cohen, Newton-John and Slater2018; Lonergan et al., Reference Lonergan, Bussey and Mond2019). Importantly, photo editing and investment in photos are highly correlated among adolescents and young adults (Cohen et al., Reference Cohen, Newton-John and Slater2018; Mingoia et al., Reference Mingoia, Hutchinson, Gleaves and Wilson2019; McLean et al., Reference McLean, Paxton, Wertheim and Masters2015), suggesting that photo editing may be a behavioral manifestation of appearance investment.

Investment in Peer Feedback

Adolescents are also often highly invested in receiving peer feedback on their social media posts in the form of “likes,” followers, friends, and comments. Qualitative work suggests that adolescents, especially girls, post selfies for the primary purpose of appearing attractive or favorable to peers and ultimately receiving positive peer feedback (Burnette et al., Reference Burnette, Kwitowski and Mazzeo2017; Chua & Chang, Reference Chua and Chang2016; Yau & Reich, Reference Yau and Reich2019). Research also shows that adolescents and young adults engage in various behaviors to earn more “likes” on their content, including editing their photos, uploading photos at certain times of day, deleting photos when they do not get enough likes and reposting at a later time, purchasing followers and likes, asking their friends to like their photos, and liking others’ photos in exchange for more likes (Dumas et al., Reference Dumas, Maxwell-Smith, Davis and Giulietti2017; Yau & Reich, Reference Yau and Reich2019). Among adolescents, this behavior is associated with negative mental and behavioral health outcomes (Nesi & Prinstein, Reference Nesi and Prinstein2019), problematic social media use (e.g., using social media to cope with negative emotions; Martinez-Pecino & Garcia-Gavilán, Reference Martinez-Pecino and Garcia-Gavilán2019), and lower global self-esteem (Meeus et al., Reference Meeus, Beullens and Eggermont2019). Some preliminary work suggests that concern about peer feedback on one’s selfies specifically is associated with worse body esteem (Nesi et al., Reference Choukas-Bradley, Roberts, Maheux and Nesi2021). Among young adult women, greater investment in selfie feedback from peers was associated with body surveillance, body dissatisfaction, and drive for thinness, but not bulimic tendencies (Butkowski et al., Reference Butkowski, Dixon and Weeks2019). Notably, young adults who engage in negative feedback seeking (i.e., eliciting negative feedback to confirm negative perceptions of oneself) and who receive more negative comments on Facebook are more likely to report disordered eating concerns and behaviors a month later (Hummel & Smith, Reference Hummel and Smith2015).

Appearance-Related Social Media Consciousness

The visual nature of social media that leads to a focus on physical appearance, such as HVSM, may manifest as a broader preoccupation with one’s social media self-presentation, even in offline spaces. Appearance-related social media consciousness (ASMC) has been proposed as a novel subjective experience among adolescents and adults, defined as a preoccupation with one’s attractiveness to a real or potential social media audience (Choukas-Bradley et al., Reference Choukas-Bradley, Nesi, Widman and Higgins2019, Reference Choukas-Bradley, Nesi, Widman and Galla2020). ASMC is common among both adolescents and young adults, especially among young women (Choukas-Bradley et al., Reference Choukas-Bradley, Nesi, Widman and Higgins2019, Reference Choukas-Bradley, Nesi, Widman and Galla2020). In some ways, this experience reflects the extension of self-objectification to a social media audience, whereby adolescents and young adults imagine how their social media photos look to outside observers, overvalue their physical appearance on social media relative to other social media experiences, and even remain vigilant during in-person social interactions with the knowledge that at any moment a photo could be taken and posted to a larger social media audience (Choukas-Bradley et al., Reference Choukas-Bradley, Nesi, Widman and Higgins2019, Reference Choukas-Bradley, Nesi, Widman and Galla2020). ASMC is correlated with self-objectification, body surveillance, body shame, body comparison, depressive symptoms, and disordered eating among adolescents (Choukas-Bradley et al., Reference Choukas-Bradley, Nesi, Widman and Galla2020). Additionally, even when controlling for body surveillance (a behavioral manifestation of self-objectification) and overall time on social media, ASMC is associated with greater disordered eating behaviors for adolescent girls (Choukas-Bradley et al., Reference Choukas-Bradley, Nesi, Widman and Galla2020), suggesting the unique effect of social media-specific appearance cognitions.

Weight Loss and Fitness Social Media Content

“Thinspiration” and “fitspiration” refer to social media images meant to inspire viewers to be thin or fit, respectively. While social media users may believe this content teaches viewers healthy lifestyle and dieting techniques, it can be problematic if it encourages inaccurate, or even dangerous, health content (Carrotte et al., Reference Carrotte, Vella and Lim2015). Thinspiration and fitspiration images frequently depict weight loss techniques or fitness regimens, though there is no guarantee that these messages come from certified health professionals. More likely, the images have been posted by celebrities, models, influencers, or peers. Moreover, teenage girls with preexisting body image concerns are especially likely to seek out this type of content, hoping to gain inspiration for changing their own weight or appearance (Carrotte et al., Reference Carrotte, Vella and Lim2015). The presentation of these images on social media, where adolescents frequently see the personal life experiences of their peers, may make them appear more relatable and thus attainable, despite many negative outcomes related to viewing these images. Alarmingly, companies that manufacture “wellness” products such as FlatTummyShakes and FitTea hire popular social media influencers and celebrities to advertise their products, though these supplements contain appetite suppressants and laxatives, and thereby facilitate disordered eating (Auguste et al., Reference Auguste, Bradshaw and Bajalia2019; Wong, Reference Wong2018). Studies examining young adults’ exposure to and posting of such content consistently show associations with body dissatisfaction and disordered eating behaviors (e.g., Griffiths & Stefanovski, Reference Griffiths and Stefanovski2019; Holland & Tiggemann, Reference Holland and Tiggemann2017), often mediated by appearance comparisons (e.g., Tiggemann & Zaccardo, Reference Tiggemann and Zaccardo2015). To our knowledge, only one study on the topic has included adolescents, finding that participants with a diagnosed eating disorder were more than twice as likely to view fitness-related social media content, and consumption of such content was highest among adolescent girls relative to boys and young adult women (Carrotte et al., Reference Carrotte, Vella and Lim2015).

Social Media Content Encouraging Eating Disorders

Taken to the extreme, some social media content is dedicated to promoting and encouraging eating disorders. This content, often referred to “pro-ED” (pro-eating disorder), “pro-ana” (pro-anorexia nervosa), or “pro-mia” (pro-bulimia nervosa), facilitates community discussion by individuals with these disorders to maintain their disordered eating behaviors and cognitions. The majority of followers of pro-ED profiles are adolescent girls (Bert et al., Reference Bert, Gualano, Camussi and Siliquini2016). Content includes images of emaciated figures to inspire extreme thinness, challenges and competitions for caloric restriction, techniques for avoiding treatment, and anti-recovery messages (Arseniev-Koehler et al., Reference Arseniev-Koehler, Lee, McCormick and Moreno2016; Bert et al., Reference Bert, Gualano, Camussi and Siliquini2016; Ging & Garvey, Reference Ging and Garvey2018). Eating disorders may be acquired or exacerbated through social learning processes. Indeed, research on group treatments for adolescent eating disorders demonstrates that patients may bond over their weight loss goals, share tricks for preventing effective care, vomit together, or compete with one another for the most severe case presentation (McGilley, Reference McGilley2006; Vandereycken, Reference Vandereycken2011). Whereas in clinical contexts, trained clinicians are able to monitor and address these phenomena, such an opportunity is unavailable on social media. Adolescents who are most at risk for disordered eating and who are more easily impressionable may be especially at risk for valuing the potential social support these pro-ED platforms provide (Arseniev-Koehler et al., Reference Arseniev-Koehler, Lee, McCormick and Moreno2016). At a developmental stage when peer evaluation and feedback is of paramount importance, these platforms pose a dangerous threat for encouraging and exacerbating adolescent eating disorders.

Body Positivity Social Media Content

In response to the increased popularity of appearance-focused photo and video sharing on social media (Anderson & Jiang, Reference Anderson and Jiang2018), there has been an emergence of body positive content that focuses on challenging the unrealistic beauty standards depicted on social media by reconceptualizing body acceptance. More specifically, the social movement known as “the body positivity movement” has developed on social media with the intention of increasing body acceptance through broad definitions of beauty and the depiction of a greater range of body types and appearances, along with limited photo editing and manipulation (Cohen, Irwin, et al., Reference Cohen, Fardouly, Newton-John and Slater2019; Lazuka et al., Reference Lazuka, Wick, Keel and Harriger2020; Tylka & Wood-Barcalow, Reference Tylka and Wood-Barcalow2015; Webb et al., Reference Webb, Vinoski, Bonar, Davies and Etzel2017). Indeed, content analyses indicate that posts related to body positivity present varying constructs of beauty (Lazuka et al., Reference Lazuka, Wick, Keel and Harriger2020), and have gained popularity on mainstream online communities (see Rodgers et al., Reference Rodgers, Slater, Gordon, McLean, Jarman and Paxton2020). Research has begun to examine the potential benefits of exposure to this content, with recent experimental studies finding associations between young women’s exposure to body positive images and boosts in body satisfaction and body appreciation, when compared to viewing thin-ideal images (Cohen, Fardouly, et al., Reference Cohen, Fardouly, Newton-John and Slater2019; Williamson & Karazsia, Reference Williamson and Karazsia2018). Despite this promising evidence, there is debate regarding how body positive content may continue to place value on physical appearance and may increase shame for individuals who have lower body acceptance (see Cohen et al., Reference Cohen, Newton-John and Slater2020). Consistent with this critique, studies have found that despite women’s frequently encouraging responses to body positive images, such exposure is associated with higher levels of self-objectification and salience of physical appearance (e.g., describing the self through the lens of physical appearance rather than other attributes; Betz & Ramsey, Reference Betz and Ramsey2017; Cohen, Fardouly, et al., Reference Cohen, Fardouly, Newton-John and Slater2019). The numerous negative outcomes associated with self-objectification (Fredrickson & Roberts, Reference Fredrickson and Roberts1997) pose the possibility that body positive posts may have long-term negative impacts that need to be further investigated.

From a theoretical standpoint, there are also potential benefits of body positive social media. For example, the tripartite influence model (Thompson et al., Reference Thompson, Heinberg, Altabe and Tantleff-Dunn1999) offers another framework for evaluating the relationship between body positive social media content and body image concerns. Exposure to a more diverse range of bodies may lead to a decrease in the internalization of media’s unrealistic appearance ideals (i.e., the thin and muscular ideals), improving viewers’ body image. Additionally, it is possible that social media users’ engagement in social comparisons with more inclusive and realistic social media targets may positively affect body image outcomes. Since much is currently unknown regarding the impacts of body positive content, future research should investigate the short- and long-term benefits and consequences associated with exposure to body positive social media content, especially among adolescent girls, who may be distinctly vulnerable to these associated effects.

Conclusion

This chapter describes theoretical and empirical work on adolescents’ social media use, body image, and disordered eating. Although a few examples highlight the potential benefits of social media for adolescents’ body image, the majority of work in this area underscores the role of social media in perpetuating and encouraging body dissatisfaction and disordered eating behaviors, particularly among adolescent girls, by overemphasizing physical attractiveness and body ideals on HVSM. Many of the social media behaviors and experiences described in this chapter are normative and thus insidious in potentially causing harm. Others, such as pro-ED sites, are more flagrant. Adolescents, their parents, and clinicians should be made aware of the potential detriments and dangers of these platforms. Future research should continue to investigate these processes and develop intervention, prevention, and dissemination strategies to foster adolescents’ healthy body image and eating behaviors across development.

Problem Behavior and Delinquency

Problem behavior is generally conceptualized to include rule breaking, delinquency, antisocial behavior, and other acts that go against societal norms. In the digital age, problem behavior can (and does) occur online, and thus here we attend both to online manifestations of problem behavior alongside the ways in which adolescent engagement with digital media is associated with offline delinquency. As with all the externalizing and health risk behavior outcomes included here, we first consider whether there are consistent associations between the quantity of adolescent digital media engagement (e.g., screen time) and their problem behaviors before turning our attention to the quality/nature of online experiences.

Aggression, Bullying, and Violence

Here, we consider how digital media use may relate to both physical and social/relational forms of aggression (the latter of which is particularly relevant online; Archer & Coyne, Reference Archer and Coyne2005). Indeed, aggression online can take a number of forms, including online bullying, harassment, and discrimination. Prevalence estimates vary widely and range from 1.0% to 61.1% of youth experiencing cyber-victimization and 3.0% to 39.0% of youth engaging in cyber-perpetration of aggression, suggesting that social media is a prominent context for cyberbullying (Brochado et al., Reference Brochado, Soares and Fraga2017; Kowalski et al., Reference Kowalski, Limber and McCord2019; Thomas et al., Reference Thomas, Connor and Scott2015).

Research suggests that many of the social roles that serve to instigate and sustain traditional/offline bullying also can be seen online. Sterner and Felmlee (Reference Sterner and Felmlee2019) identified distinct roles of Perpetrator, Reinforcer, Victim, Defender, Bystander, and Informer around cyberbullying on Twitter. Reinforcers and defenders tended to enact these roles by commenting or by liking posts of the perpetrator or victim respectively, whereas informers tended to alert a site administrator to the cyberbullying incident. Interestingly, there were an average of 12 people directly involved (in one of the above roles) in each case of aggression on Twitter, suggesting that some features of social media (e.g., its permanence; Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018a, Reference Nesi, Choukas-Bradley and Prinstein2018b) may increase the reach of cyberbullying experiences beyond those typically seen in face-to-face bullying.

Sexual Risk Taking

In adolescence, high risk sexual behaviors include behaviors that increase risk of unintended pregnancy, HIV infection, and other STIs, including early age at first intercourse, multiple sexual partners, concurrent sexual partners, having one-night stands, using drugs or alcohol prior to having sexual intercourse, having sex in exchange for money, and lack of pregnancy prevention methods (Kann et al., Reference Kann, Eaton and Kinchen2018). Sex and sexual risk taking have always been salient in adolescence, and in the digital age they are increasingly also taking shape in online spaces.

Social media and platforms that allow private messages are prevalent among youth to develop and maintain their romantic relationships, with only a small minority of adolescents accessing formal dating apps (which are meant to be illegal for minors; Vandenbosch et al., Reference Vandenbosch, Beyens, Vangeel and Eggermont2016). About 8% of all teens have met a romantic partner online (Lenhart et al., Reference Lenhart, Smith and Anderson2015) and 30% of sexually experienced adolescents have met a sexual partner online, with those who met partners online more likely to engage in unprotected sex and with multiple concurrent sexual partners (Ybarra & Mitchell, Reference Ybarra and Mitchell2016). In this domain, social media may also contribute to health, safety, and privacy risks. Youth are exposed to and engage with sexual content in media, including pornography and sexting, that may impact their offline sexual behavior. In addition, youth may engage in online sexual behaviors such as cybersex or coordinating encounters with potential partners (including strangers). People have been very concerned about the risk that children will be targeted by sexual predators online, but empirical research suggests that this is in actuality very rare (Ybarra & Mitchell, Reference Ybarra and Mitchell2016).

Substance Misuse

Substance misuse is a major public health concern among adolescents, with implications for long-term mental and physical health (Grant & Dawson, Reference Grant and Dawson1998; Substance Abuse and Mental Health Services Administration, 2019). Here, we consider research at the intersection of technology and all classes of substance use (including alcohol, prescription and over-the-counter medicine, tobacco, marijuana, and other illicit drugs), though the existing literature (and thus too our review) focuses most closely on the most prevalent adolescent substance use type: alcohol use and misuse.

As with the other externalizing and health risk outcomes considered here, we will review studies on both the quantity of engagement with digital media (and its potential implications for adolescent substance misuse) and research on how adolescents engage around alcohol online. Unlike previously considered outcomes of problem behavior/delinquency, aggression, and sexual risk taking, substance use does not have an online analogue. Although teens can (and do) engage in online expression of sexual behavior and risk (e.g., sexting), delinquency (e.g., hacking and cracking), and aggression (e.g., cyberbullying), there is as of yet no way that adolescents can consume alcohol or other substances online. They do, however, post in both text and pictures (Moreno et al., Reference Moreno, Cox, Young and Haaland2015) about offline alcohol and drug consumption, view such posts from their friends, and use digital media to glorify, rehash, coordinate, and even lament drinking episodes online (D’Angelo et al., Reference D’Angelo, Zhang, Eickhoff and Moreno2014; Hebden et al., Reference Hebden, Lyons, Goodwin and McCreanor2015; Jensen et al., Reference Jensen, Hussong and Baik2018). We will thus here consider whether engaging with digital media in these different ways is associated with riskier adolescent substance use outcomes. Although alcohol-related marketing does occur online, research suggests that most adolescent exposure to alcohol-related content online is noncommercial (posted by individuals in the social network; Cavazos-Rehg et al., Reference Cavazos-Rehg, Krauss, Sowles and Bierut2015) and thus alcohol marketing is not considered here.

Shared Vulnerabilities

A long body of research suggests that externalizing and health risk behaviors (e.g., sexual risk taking, substance use, aggression, and problem behavior) frequently co-occur, and are likely driven by the same vulnerabilities (S. E. Young et al., Reference Young, Friedman and Miyake2009). So too we are beginning to find that youth who are engaged in online risky or externalizing behaviors are likely to be involved in other behaviors on the externalizing spectrum. For instance, we have seen that perpetrators of online bullying are more likely to engage in substance use and offline conduct behaviors (Sourander et al., Reference Sourander, Klomek and Ikonen2010; Ybarra & Mitchell, Reference Ybarra and Mitchell2004). We also see that sexting is related to nonsexual risk-taking behavior, with adolescents who engage in sexting having higher odds of tobacco and alcohol use (Kosenko et al., Reference Kosenko, Luurs and Binder2017).

One compelling explanation for this co-occurrence is that the same risk factors likely predispose youth to multiple types of (online and offline) externalizing spectrum and health risk behaviors. For instance, online antisocial behaviors are associated with many of the same risk factors for in-person antisocial behaviors (i.e., narcissism, exhibitionism, and exploitativeness; Carpenter, Reference Carpenter2012). Online aggression and cyberbullying seem to be facilitated by long-known individual (e.g., low agreeableness, moral disengagement, hyperactivity), family (e.g., low parental monitoring), peer (e.g., deviant peer group), and community factors (e.g., low school safety; Espelage et al., Reference Espelage, Rao, Craven, Bauman, Cross and Walker2012; Kowalski et al., Reference Kowalski, Giumetti, Schroeder and Lattanner2014; Marín-López et al., Reference Marín-López, Zych, Ortega-Ruiz, Monks and Llorent2020). Likewise, similar risks are associated with youth engagement in online and offline sexual behavior: sensation seeking, low levels of education, less parental monitoring, and less family cohesion (Baumgartner et al., Reference Baumgartner, Sumter, Peter and Valkenburg2012; Ševčíková et al., Reference Ševčíková, Šerek, Barbovschi and Daneback2014). In particular, risk factors for externalizing problems that are developmentally salient in adolescence (like behavioral disinhibition and its sister concepts of impulsivity, sensation seeking, and low self-control; Steinberg, Reference Steinberg2010) stand out as contributors to both offline and online behaviors. This pattern of shared risk across outcomes highlights the importance of accounting for relevant covariates in studies that seek to parse the nature of associations between digital media and externalizing and health risk behaviors and for ensuring that observed associations are meaningful and interpretable, and not just a result of a “third variable” problem.

In fact, some theorize that the online environment may be particularly well-suited for disinhibition. The online disinhibition effect theory posits that a confluence of factors that facilitate disinhibition are inherent in the online space (dissociative anonymity, invisibility, asynchronicity, solipsistic introjections, dissociative imagination, and minimization of authority; Suler, Reference Suler2004). Although social media is increasingly dropping some of these features (e.g., synchronous dyadic or group conversations via video or voice chat are increasingly common), it still may be the case that the Internet provides some psychological distance from the impact of one’s actions and lowers the threshold to rash action to a lower point than what would be present in face-to-face interactions.

Peer Selection

One of the most potent predictors of youth risk taking and externalizing behavior is the peer context, whether that be digital or in traditional, face-to-face spaces (Chan et al., Reference Chan, Jensen and Dishion2019; Leung et al., Reference Leung, Toumbourou and Hemphill2014). Adolescence lies at the nexus of susceptibility to peer influence, concern for social reward, and engagement with digital peer contexts. Some features of digital media and online social networks make them particularly powerful conduits for peer influence: This is articulated in Nesi, Choukas-Bradley, and Prinstein’s transformation framework (Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018a, Reference Nesi, Choukas-Bradley and Prinstein2018b), which asserts that traditional peer relations constructs are transformed via the features of social media.

We know from decades of research that adolescents tend to be similar to their peers (homophily), with support for similarly minded peers choosing one another as friends (selection) as well as social influence by adolescents on their peers’ attitudes and behavior (socialization). The classic question of whether peer similarity is driven by selection or socialization (e.g., Kandel, Reference Kandel1978) is equally relevant in the digital age. That is, are the many associations seen here between peers’ online behaviors and adolescents’ own online and offline behaviors a result of selection (i.e., choosing people with shared interests and behaviors) or socialization (i.e., peer influence)? Although peer socialization processes are the most frequent intervention target for preventing externalizing and health risk behaviors (Henneberger et al., Reference Henneberger, Mushonga and Preston2020), selection is often also at play, and it can be difficult to disentangle the two and their influences (Gallupe et al., Reference Gallupe, McLevey and Brown2019; Samek et al., Reference Samek, Goodman, Erath, McGue and Iacono2016). Selection and socialization processes are often mutually influential, such that youth select into antisocial networks and then they reinforce each other over time (Brechwald & Prinstein, Reference Brechwald and Prinstein2011). Modern statistical methods like social network analysis and stochastic actor-partner modeling have allowed for scholars to parse the two more finely than ever before, and in fact, selection has been shown to be a stronger explanation for peer similarity in substance use behaviors than socialization effects (Rebellon, Reference Rebellon2012).

In some ways, digital media is well-suited to help us better understand homophily, as online communication and social networks leave behind digital traces of the selection and socialization processes that we suspect are at work. Ehrenreich and colleagues (Reference Ehrenreich, Meter, Jouriles and Underwood2019) used adolescent text messages over the course of high school, which were coded for antisocial content, to delve deeper into this very question. They found that those youth who were engaging in more externalizing behaviors (a combination of aggression and rule breaking) at each grade were more likely to be exchanging antisocial text messages (about substance use and rule breaking) with a larger proportion of their peers in the subsequent grade (evidence of a selection effect), but the proportion of antisocial dyads did not predict next-grade externalizing (lack of support for a socialization effect). Interestingly, they did find some evidence of a socialization effect when they homed in specifically on the first year of high school, such that the proportion of peers exchanging antisocial texts in the 9th grade was associated with one’s own rule-breaking behaviors a year later. A study using social network analysis showed that both selection and socialization processes were relevant to adolescent substance use: Teens tended to select friends with similar social media use and substance use behaviors, but exposure to photos of substance use online also seemed to socialize adolescents’ later smoking behavior (Huang, Soto, et al., Reference Huang, Soto, Fujimoto and Valente2014).

Peer Socialization

Although studies of digital media and traditional peer interactions suggest that selection is likely more important than it is often given credit for, socialization is still relevant to understanding peer processes in externalizing behavior. Adolescent susceptibility to peer influence is evolutionarily driven (Ellis et al., Reference Ellis, Del Giudice and Dishion2012) and evident even in their neurobiology (e.g., Chein et al., Reference Chein, Albert, O’Brien, Uckert and Steinberg2011); adolescence is a period in which youth are keenly motivated for social affiliation (including romantic), and thus highly motivated to seek social approval. We review several forms of peer influence/socialization here.

Identity

Adolescent online and offline experiences are increasingly interwoven and often indistinguishable into what Granic and colleagues (Reference Granic, Morita and Scholten2020) call “hybrid realities” that are both important for the attainment of developmental tasks like identity development. The Media Practice Model asserts that adolescents choose to interact with media in ways that are most consistent with their identity (or what they aspire for their identity to be; Brown, Reference Brown2000). We must consider, then, that adolescents’ online engagement in and depiction of risk-taking and externalizing behaviors (e.g., sexting, depictions of substance use, cyber-aggression) are best understood through the lens of identity development and intentional self-presentation.

This thesis is supported by evidence that adolescents engage in sexting and cybersex in ways that are consistent with sexual identity exploration and development (Eleuteri et al., Reference Eleuteri, Saladino and Verrastro2017) and that depictions of alcohol use online are related to one’s identity as a “drinker” (Thompson & Romo, Reference Thompson and Romo2016; Westgate & Holliday, Reference Westgate and Holliday2016). This is also consistent with research in college students that suggests that depictions of substance use in highly visible areas (i.e., a profile or cover photo, which may seem more tied to identity) are more strongly tied to alcohol use and binge drinking than depictions elsewhere on social media (e.g., in a status update or a photo post; Moreno et al., Reference Moreno, Cox, Young and Haaland2015).

Health Information

Social media can be a powerful tool in disseminating public health information to adolescents, particularly given the omnipresence of social media in the lives of youth. Even before the advent of social media, the Internet was the primary source of health information for adolescents, especially those with few alternative accurate sources of information and for sensitive topics (Borzekowski et al., Reference Borzekowski, Fobil and Asante2006; Gray et al., Reference Gray, Klein, Noyce, Sesselberg and Cantrill2005). More recently, a number of qualitative studies with adolescents have confirmed that social media and text messaging are accessible and appealing sources of public health information (e.g., sexual health), though youth are also wary of potentially inaccurate or uncredible online sources (and have encountered barriers like inadvertently opening pornographic content; Selkie et al., Reference Selkie, Benson and Moreno2011). In a study of African American and Latinx youth, Stevens et al. (Reference Stevens, Gilliard-Matthews, Dunaev, Todhunter-Reid, Brawner and Stewart2017) found that social media was an important source of sexual health information, and that participants felt social media was a more credible source than internet searches. Further, exposure to sexual health information on social media was significantly associated with reductions in sexual risk-taking behaviors (Stevens et al., Reference Stevens, Gilliard-Matthews, Dunaev, Todhunter-Reid, Brawner and Stewart2017).

Delivery of Prevention Messaging

In addition to health information, social media can also be utilized to convey prevention messages to adolescents. Another qualitative study with US adolescents found that teens differentiate between social media platforms when engaging with drug prevention content and are highly conscious of how their peers might perceive their behavior (Dunn et al., Reference Dunn, Pearlman, Beatty and Florin2018). Consequently, participants reported reading and liking prevention content, but were not likely to share it with their peers or create antidrug content themselves. Participants in this study recommended using short and humorous videos on platforms away from adult eyes, where teens might feel more comfortable, and the authors thus conclude that it is crucial to involve adolescents in creating effective prevention messaging on social media.

Numerous studies have found that internet-based interventions can reduce risk-taking behavior, albeit with small effects. Adolescent women who participated in a web-based drug prevention intervention were less likely to use drug and alcohol six months after the intervention than their peers in the control group. Further, participants in the intervention group also saw increases in understanding of normative beliefs and self-efficacy (Schwinn et al., Reference Schwinn, Schinke and Di Noia2010). A text-based intervention study of youth seen in the emergency department for drinking-related outcomes found that youth in the intervention group engaged in fewer binge-drinking episodes and drank fewer drinks per day than their peers in the control group at the three-months post-test (Suffoletto et al., Reference Suffoletto, Kristan and Callaway2014).

A 2014 systematic review of 11 intervention studies that examined social media and text messaging as a mechanism for sexual health education concluded that these mediums can increase knowledge of STI prevention and may reduce risky sexual behaviors (Jones et al., Reference Jones, Eathington, Baldwin and Sipsma2014). For example, a Facebook-based intervention saw small gains in condom use among adolescents in the intervention group at two months, though this difference diminished by the six-month follow-up (Bull et al., Reference Bull, Levine, Black, Schmiege and Santelli2012).

Online Support

Although many studies have documented the benefits of online support groups (using a variety of modalities including social media, text messaging, and internet browser) for adolescents with health problems (e.g., cancer, asthma, type I diabetes), very few studies have analyzed the efficacy of online support groups as strategy to reduce adolescents’ externalizing and risk-taking behaviors (Selkie et al., Reference Selkie, Benson and Moreno2011). We do know that adolescent participants report utilizing anonymous online chat rooms to discuss sensitive topics (e.g., drug and alcohol use), and that these anonymous interactions can yield feelings of emotional support (Gray et al., Reference Gray, Klein, Noyce, Sesselberg and Cantrill2005).

Research with adults suggests that online support communities could also be a useful tool in mitigating risk-taking and externalizing behaviors in adolescents. Indeed, studies of adults suggest that web-based support through Adult Child of Alcoholic (ACoA) online support groups afford desired anonymity, accessibility, and support from any location or at any time of day (Haverfield & Theiss, Reference Haverfield and Theiss2014). Likewise, a 2020 study of adults in an online recovery group found that the social support offered through the online group interactions seemed to reduce social isolation and the risk of drug addiction alongside helping build “recovery capital” to aid in maintaining sobriety (Bliuc et al., Reference Bliuc, Best, Moustafa and Moustafa2020).

While further research with adolescent populations is needed to investigate the potential and efficacy of online support groups in mitigating risk-taking behaviors, we can likely assume that the affordances of online support (i.e., accessibility and anonymity) will also be prized by young people. The need for accessible and high-quality recovery and support services has never been as salient as it is today when most substance abuse recovery and mental health programs have been pushed online due to the COVID-19 pandemic.

Assessing Problematic Digital Media Use

There are several measures of various types of problematic digital media use with strong psychometric properties. Although most have been validated with adult samples, we review three that have been developed for adolescents and are specific to social media use. One measurement that has been used to assess problematic digital media use is the Bergen Social Media Addiction Scale (BSMAS), previously known as the Bergen Facebook Addiction Scale (Andreassen et al., Reference Andreassen, Torbjørn, Brunborg and Pallesen2012). This scale assesses how social media is used rather than the social media platform specifically (Lin et al., Reference Lin, Broström, Nilsen, Griffiths and Pakpour2017) and social media use is assessed over the past year (Watson et al., Reference Watson, Prosek and Giordano2020). The BSMAS is comprised of 18 items that assess 6 symptoms of addiction: salience, mood modification, withdrawal symptoms, tolerance, conflict, and relapse (Andreassen et al., Reference Andreassen, Torbjørn, Brunborg and Pallesen2012).

The BSMAS’ Cronbach’s alpha is 0.83 (Andreassen et al., Reference Andreassen, Torbjørn, Brunborg and Pallesen2012), suggesting strong internal consistency. Regarding convergent validity, this scale associated with the Addictive Tendencies Scale, the Facebook Attitudes Scale, and the Online Sociability Scale (Andreassen et al., Reference Andreassen, Torbjørn, Brunborg and Pallesen2012).

The Addictive Patterns of Use (APU) Scale is another reliable and valid measure that can be used to screen for smartphone addiction (Domoff, Foley, & Ferkel, Reference Domoff, Borgen, Robinson and Knox2020). The scale consists of nine items that ask adolescents to rate their frequency of symptoms of addictive phone use (Domoff, Foley, & Ferkel, Reference Domoff, Borgen, Robinson and Knox2020), based on criteria for internet gaming disorder from the DSM-5, adapted to smartphones. Items include “During the last year, how often have there been times when all you could think about was using your phone?” and “Have you experienced serious conflicts with family, friends, or partner because of your phone use?” (Domoff, Foley, & Ferkel, Reference Domoff, Borgen, Robinson and Knox2020). In addition to completing the nine items, adolescents are asked to list the features of their phone that they use the most, allowing researchers to identify the types of apps or smartphone functions that may be most problematic. Recently, additional research further supports the validity of APU, with this measure associating with media use (e.g., TV viewing frequency; Domoff, Sutherland, et al., Reference Domoff, Sutherland, Yokum and Gearhardt2020a) and other dysregulated behaviors (e.g., food addiction, dysregulated eating; Domoff, Sutherland, et al., Reference Domoff, Sutherland, Yokum and Gearhardt2020b).

Finally, the Social Media Disorder (SMD) Scale (van den Eijnden et al., Reference van den Eijnden, Lemmens and Valkenburg2016) similarly uses criteria of internet gaming disorder, but applied to social media use, to assess symptoms of dysregulated social media use. The developers recognize nine criteria to define disordered social media use within the adolescent population: preoccupation, tolerance, withdrawal, relapse, mood modification, external consequences, deception, displacement, and conflict (van den Eijnden et al., Reference van den Eijnden, Lemmens and Valkenburg2016). This scale is made up of 27 items, 3 items for each of the 9 criteria listed previously; a short version that consist of 9 items was also developed that selected the highest loading items on each of the 9 criteria (van den Eijnden et al., Reference van den Eijnden, Lemmens and Valkenburg2016). A cut-off score for disordered use was identified as endorsement of at least five of the nine criteria on the scale (van den Eijnden et al., Reference van den Eijnden, Lemmens and Valkenburg2016). Positive correlations between social media disorder symptoms on this scale and depressive mood, hyperactivity, and inattention have been demonstrated (van den Eijnden et al., Reference van den Eijnden, Lemmens and Valkenburg2016).

Rates of problematic social media use (or high scores on measures of “addictive” or disordered social media use) tend to fall around 7%, across 29 countries (Boer et al., Reference Boer, van den Eijnden and Boniel-Nissim2020; consistent with gaming disorder rates, Gentile et al., Reference Gentile, Bailey and Bavelier2017). That is, based on data from countries in Europe, the Middle East, and North America, approximately 7% of adolescent social media users experience impairment due to their use (Boer et al., Reference Boer, van den Eijnden and Boniel-Nissim2020), such as trouble sleeping/poor quality sleep (e.g., Vernon et al., Reference Vernon, Modecki and Barber2016) and poorer academic functioning (Domoff, Foley, & Ferkel, Reference Domoff, Borgen, Robinson and Knox2020). Given how recently problematic social media use measures were developed, there is limited research on whether this prevalence has changed over time. However, in terms of how the COVID-19 pandemic is impacting rates, early evidence suggests that burden caused by COVID-19 is associated with greater addictive social media use (Brailovskaia & Margraf, Reference Brailovskaia and Margraf2021) and some evidence that problematic social media use has increased in some samples from before to during the pandemic (among adolescents in Italy; Muzi et al., Reference Muzi, Sansò and Pace2021). Future research should prioritize examining longitudinal trajectories of problematic social media use, particularly given drastic increases in media use during the COVID-19 pandemic.

Preventing Problematic Social Media Use

Due to the burgeoning interest in social media and smartphone use among adolescents, there has been a vast amount of research highlighting correlates of social media use overall. However, there has been limited research investigating correlates or contributors to problematic social media use. Many researchers have hypothesized that there is a relationship between problematic social media use and adverse mental health symptoms, with the most consistent research supporting links via disrupted sleep and shortened sleep duration (e.g., Vernon et al., Reference Vernon, Modecki and Barber2016). There have also been various studies outlining demographic factors and social factors that are associated with dysregulated social media use. Across the studies described, it is critical to note that we focus on dysregulated use (often called problematic in subsequent research) and not amount of social media use. The research on duration or amount of social media use and various correlates is mixed and inconsistent (Odgers et al., Reference Odgers, Schueller and Ito2020), and is too indiscriminate to adequately capture the scope of adolescents’ social media interactions. It is also important to note that, unless specified, most research is correlational and should not be inferred as causal.

Internalizing symptoms, such as depressive and anxiety symptoms, correlate with disordered social media use. Bányai et al. (Reference Bányai, Zsila and Király2017) conducted a longitudinal study assessing how problematic social media use and depressive symptoms were related. It was found that both problematic social media use and depressive symptoms grew over a two-year span and that changes in problematic use correlated with changes in depressive symptoms (Bányai et al., Reference Bányai, Zsila and Király2017). Another study found direct associations between problematic social media use and depressive symptoms and indirect associations between problematic social media use and self-esteem (Kircaburun et al., Reference Kircaburun, Griffiths and Billieux2019). It has also been found that those with a higher baseline of depressive symptoms showed a sharper incline in problematic use (Raudsepp & Kais, Reference Raudsepp and Kais2019).

Various demographic factors such as gender and age have shown differing associations with problematic social media use. Gender has been found to have an impact on how social media impacts adolescents. That is, for boys, anxiety was a predictor for higher social media use while for girls, problematic social media use associates with depression (Oberst et al., Reference Oberst, Wegmann, Stodt and Brand2017). For adolescent girls, it is suggested that problematic social media use and depressive symptoms work in a cyclical fashion, whereas depressive symptoms exacerbate problematic social media use, which then further worsens depressive symptoms (Kuss & Griffiths, Reference Kuss and Griffiths2017). This suggests a possibility that adolescent girls with depressive symptoms may struggle to identify adequate coping techniques and instead use social media to ineffectively manage their symptoms (Gámez-Guadix, Reference Gámez-Guadix2014). Another study found that younger adolescents and female adolescents had higher levels of problematic social media use (Kircaburun et al., Reference Kircaburun, Griffiths and Billieux2019). Additionally, the type of social media behavior plays a role in how it impacts the social media user. There is a relationship between passive social media use (e.g., scrolling, low social interaction) and anxiety and depression symptoms, while active social media use (e.g., commenting, liking, communicating with peers) was related to lower symptoms of depression and anxiety in adolescents (Thorisdottir et al., Reference Thorisdottir, Sigurvinsdottir, Asgeirsdottir, Allegrante and Sigfusdottir2019).

Several social factors have been shown to relate to problematic use in adolescents. Social norms and friends’ social media use frequency was directly associated with frequency of social media use, leading to an association with problematic use (Marino et al., Reference Marino, Gini, Angelini, Vieno and Spada2020). Another study found that social connectedness and general belongingness were indirectly related to problematic social media use (Kircaburun et al., Reference Kircaburun, Griffiths and Billieux2019). Fear of missing out and perceived academic competence predicted addiction to social media among high school students in one study (Tunc-Aksan & Akbay, Reference Tunc-Aksan and Akbay2019).

Regarding protective factors, self-esteem has been shown to be a moderator of problematic social media use and depression in adolescents (Wang et al., Reference Wang, Wang and Wu2018). It has been proposed that adolescents with higher levels of self-esteem feel more confident in coping with adversity and are therefore less likely to have depression and subsequent problematic social media use (Wang et al., Reference Wang, Wang and Wu2018). For girls who use Facebook actively and have perceived online social support have shown to benefit from social media use, and perceived online social support was found to have a negative association with adolescent girls’ depressed moods (Frison & Eggermont, Reference Frison and Eggermont2016).

Treating Problematic Digital Media Use

Measures and Consistency of Terminology

Assessing problematic digital media use has proven to be a difficult task because of the inconsistency in terminology and conceptualization of “problematic.” We argue that problematic should not be defined by amount of use – instead, clinicians should screen for dysregulated use (“addictive”), risky use (i.e., while driving, intimate/private interactions with unknown individuals), and antisocial use (cyber-bullying, trolling, etc.) routinely with each adolescent. An additional limitation is that screening tools, such as the APU (Domoff, Foley, & Ferkel, Reference Domoff, Borgen, Robinson and Knox2020) and PMUM (Domoff, Harrison, et al., Reference Domoff, Borgen, Foley and Maffett2019), do not yet have clinical cut-off scores, necessitating their validation in clinical samples to better identify youth at risk.

Research Design

As research in the area of problematic digital media use continues to grow, many limitations in this area of investigation have come evident. One of the primary limitations is accurate reporting of digital media use, particularly among adolescents. Research suggests that individuals of every age find it difficult to accurately report how much time they are spending using digital media each day (Ohme, Reference Ohme2020). While accurate reporting of screen time is important for research, it is even more important for researchers to measure how adolescents are using digital media and what daily activities the use is interfering with, as those are the primary concerns when determining problematic use.

To get around the limitations of adolescent self-report, some researchers are beginning to use technology to track technology use. Passive sensing technology in smartphones is gaining traction as a convenient way to measure adolescent behavioral patterns like app usage or interactions on social media, in addition to physical health indicators such as movement and sleep (see Trifan et al., Reference Trifan, Oliveira and Oliveira2019 for a review of passive sensing research). The first validated passive sensing app that measures adolescents’ mobile device use (e.g., type of app used, duration, timing of use) has recently been supported as feasible to use and acceptable to adolescents and their parents (Domoff et al., Reference Domoff, Banga and Borgen2021). This app, eMoodie, has ecological momentary assessment (EMA) capacity and uses gamification principles to foster completion of surveys and EMA on adolescents’ mobile devices (see www.emoodie.com for more information). Using research designs that include objective, accurate measures of problematic digital media use will bring researchers closer to the goal of determining etiology and planning treatment.

Clinical Trials

Another area for improvement in this area of research is increased implementation of clinical trial studies. As the conceptualization and assessment of problematic digital media use expands, opportunities for clinical trial research will become more feasible. One of the few RCTs that has been conducted concerning treatment for problematic digital media use in adolescents was primarily aimed at internet addiction (Du et al., Reference Du, Jiang and Vance2010). While the study provided evidence for using CBT to treat internet addiction in adolescents, they identified their limitation of only including participants without comorbid disorders. Anecdotally, problematic digital media use commonly occurs among adolescents who have been diagnosed with other mental health disorders. In order for clinical trials to be generalizable to clinic settings, samples should include adolescents who have comorbid disorders. Additionally, it is important that clinical trials include broader types of problematic digital media use, instead of only internet addiction. The lack of treatment options for these adolescents, in addition to the growing prevalence of problematic digital media use, indicate the need for increased clinical trial research.

Sample Demographics and Diversity

Research into problematic digital media use and internet/social media addiction is being propelled forward by the growing need for identification and resource development. This is most apparent within the growing population of youth who are native to the digital social networking world as well as among those learning to incorporate these new dimensions of their virtual selves into their social networking immigrant lifestyles (Prensky, Reference Prensky2001). Future investigation should seek to address the research limitations of clinical studies in order to maximize generalizability, while also parsing out what may be facilitating differential susceptibility for risks or rewards related to social media usage. In examining the limitations of samples, there is a need for validation of aforementioned screeners and assessment in non-WEIRD (Western, educated, industrialized, rich, and democratic) populations; further, problematic social media research has quite limited samples in terms of racial/ethnic diversity and across socioeconomic strata. Given that lower-income youth and racially/ethnically diverse youth have higher rates of digital media use (and may have different risks related to social media use; e.g., harassment, victimization), future research must address this major limitation of social media research.

Causality

Although there is consistent evidence in the literature for a negative relationship between digital media use and sleep, the direction of this relationship is less than clear. The vast majority of the existing studies are based on cross-sectional designs, making it impossible to draw conclusions about the direction of the relationship (Exelmans & van den Bulck, Reference Exelmans and van den Bulck2019). Although it is generally assumed that the use of digital media deteriorates sleep, it could also be that the relationship is reversed in that adolescents who sleep less tend to use more digital media. For example, adolescents who do not sleep well might use digital media as a means to cope with stress and insomnia, or because they are depleted and do not have the capacities for regulating their media use efficiently. For example, university students used more social media on days they had slept less during the previous night (Mark et al., Reference Mark, Wang, Niiya and Reich2016). Similarly, sleep-deprived children watched more TV during the day in an experimental study (Hart et al., Reference Hart, Hawley and Davey2017).

Findings like this cast doubt on the idea that there is a simple cause-and-effect relationship between digital media and sleep. Recent advancement in media effects theories conceptualize media use and effects as reciprocal, evolving dynamically over time (Slater, Reference Slater2007). In the case of media use and sleep this could mean that adolescents suffering from sleep problems are more likely to use more media that in turn may further deteriorate their sleep. This dynamic and reciprocal nature for smartphone use and sleep is understudied as it demands assessing use and effects over longer time periods in the natural environment of adolescents. One two-wave study found some evidence by showing that media use and sleep times were reciprocally related in adolescents over a one-year period (Poulain et al., Reference Poulain, Vogel, Buzek, Genuneit, Hiemisch and Kiess2019). Understanding the nature of the relationship between digital media use and sleep is of key importance for our understanding of the effects of digital media and for intervention and prevention programs.

Individual Responses and Potential Facilitating Effects

Recent theoretical advances in media effects research stress the importance of individual susceptibilities to media effects (Beyens et al., Reference Beyens, Pouwels, van Driel, Keijsers and Valkenburg2020; Valkenburg & Peter, Reference Valkenburg and Peter2013). Also, for the relationship between sleep and digital media, individual differences are likely to be of importance. First, individuals differ in how they use digital media before sleep. For example, Scott and Woods (Reference Scott and Woods2018) showed that adolescents with higher levels of fear of missing out tended to use social media longer before sleep time and were more cognitively aroused before falling asleep. Thus individualized usage patterns might lead to varying effects. This is also important because not all evening media diets might be problematic. Some adolescents might use their smartphones in a way that benefits their sleep by actually decreasing their aroused state. This assumption builds on established media effects paradigms that argue that media are used to regulate arousal levels and to establish physiological homeostasis (Zillmann, Reference Zillmann1988). For example, people can use apps to seek out social support, relax, and regulate sensory stimulation (Harrison et al., Reference Harrison, Vallina, Couture, Wenhold and Moorman2019). Research has shown that some people report that they use media in bed to wind down from the day (Eggermont & van den Bulck, Reference Eggermont and van den Bulck2006). However, little research has investigated whether digital media can be used in ways that benefit adolescents’ sleep. Understanding such effects could help to educate adolescents to use their smartphones in more beneficial ways.

A second reason why it is important to study individual differences is that, while uniform effects of some content are possible, adolescents likely differ in their individual responses to digital media content. For example, one study found that adolescents who used social media more frequently slept less well than those who used social media less frequently. However, this effect disappeared when social media stress was taken into account, showing that only those respondents who experienced high levels of stress from their social media use suffered from sleep problems (van der Schuur et al., Reference van der Schuur, Baumgartner and Sumter2019). Moreover, this study showed that social media use was more problematic for the sleep of girls and early adolescents. Similarly, others found that only those who were more emotionally invested in their social media use slept less well (Woods & Scott, Reference Woods and Scott2016), and that physiological reactions to violent game play differed depending on previous game experience (Ivarsson et al., Reference Ivarsson, Anderson, Åkerstedt and Lindblad2013). Investigating these individual responses to smartphone use is crucial to understand why specific content is problematic for some adolescents but not for others.

Improved Measurement

The vast majority of existing studies relied on self-reports of media use and/or sleep. Self-reports for media use and sleep have been shown to be unreliable and it is thus likely that existing studies suffer from substantial measurement errors. Luckily recent developments in digital media and sleep tracking make it easier to assess digital media use as well as sleep unobtrusively and objectively. For example, there is a multitude of commercially available sleep trackers available with some studies showing promising results using them. We therefore hope that future research will try to combine self-reports with more objective measures for both digital media use and sleep. Assessing the complexity of digital media use objectively will be a crucial step to move beyond investigating screen time toward understanding differential effects of specific content (Carter et al., Reference Carter, Rees, Hale, Bhattacharjee and Paradkar2016; Hale et al., Reference Hale, Li, Hartstein and LeBourgeois2019; Scott & Woods, Reference Scott and Woods2019).

Adolescence and SITB

Understanding and addressing SITB is of major public health importance. Suicide is the second leading cause of death among young people between the ages of 10 and 24 globally (Curtin et al., Reference Curtin, Warner and Hedegaard2016). Among US-based adolescent populations, lifetime prevalence of suicidal thoughts and behaviors is between 3.1% and 8.8% for suicide attempts and between 19.8% and 24.0% for suicidal ideation, with a marked increase in both suicidal ideation and behavior between the ages of 12 and 17 (Nock et al., Reference Nock, Borges and Bromet2008). Rates of NSSI – “the deliberate, self-inflicted damage of body tissue without suicidal intent and for purposes not socially or culturally sanctioned” (International Society for the Study of Self-Injury, 2018) – range from 17% to 37% among adolescents and young adults (Jacobson & Gould, Reference Jacobson and Gould2007; Swannell et al., Reference Swannell, Martin, Page, Hasking and St John2014).

Self-injurious thoughts and behaviors typically emerge in early- to mid-adolescence, with average age of onset for NSSI between 13 and 15 (Gillies et al., Reference Gillies, Christou and Dixon2018), and mid- to late-adolescence for suicidal thoughts and behaviors (Nock et al., Reference Nock, Green and Hwang2013). Older adolescents and young adults are more likely to die by suicide (Cha et al., Reference Cha, Franz, Guzmán, Glenn, Kleiman and Nock2018), when compared to younger adolescents – a pattern consistent with the idea that risk of engagement in serious suicide-related behaviors increases over time as experience of trauma and/or distress accumulates and interacts with bio-psycho-social developmental changes in ways that enhance vulnerability to cognitive and emotional challenges (Steinberg, Reference Steinberg2010). Adolescence is also characterized by a highly social orientation, increased propensity for risk taking, and individuation/identity formation – each of which may interact with social media use in ways that amplify, or increase susceptibility to, potential media effects.

The Role of Social Media and SITB

Three decades of experience with, and empirical study of, unidirectional media affirms the potency of media influence on behavior, particularly for adolescents and children (Brown et al., Reference Brown, L’Engle, Pardun, Guo, Kenneavy and Jackson2006). The empirical link between exposure to violent media content and child and adolescent aggression was central to early media concerns and resulted in coordinated policy responses (US Senate, 2000). More recent efforts to understand the effects of social media on youth mental health retain a heightened focus on the potential adverse effects, such as: cybervictimization (John et al., Reference John, Glendenning and Marchant2018; Massing-Schaffer & Nesi, Reference Massing-Schaffer and Nesi2020), internet addiction (Jasso-Medrano & López-Rosales, Reference Jasso-Medrano and López-Rosales2018), and exposure to graphic self-injury and suicidal content (Arendt et al., Reference Arendt, Scherr and Romer2019). It is thus not surprising that there are serious concerns about the impact that social media may have on individuals who bring preexisting vulnerabilities to online exchanges, such as SITB-vulnerable young people.

While attention to each of these domains has translated into research on social media effects of value to professionals, researchers, and platform designers, it has not yet led to robust understanding of the precise risks that social media pose to youth mental health – largely due to the number of contingencies that require disentangling and a need for methodological innovation (Whitlock & Masur, Reference Whitlock and Masur2019). Moreover, while concern about the impact of social media on youth continues to be a regular feature of public worry and headlines, it is also recognized that social media offers important support to users, including SITB-vulnerable individuals, by (1) facilitating social connection (Duggan et al., Reference Duggan, Heath, Lewis and Baxter2012), (2) extending the reach of prevention/intervention efforts (Thorn et al., Reference Thorn, Hill and Lamblin2020), (3) linking young people who are already engaging in SITB with much needed information and support (Lavis & Winter, Reference Lavis and Winter2020; Lewis & Michal, Reference Lewis and Michal2016), and (4) increasing public awareness of SITB and reducing stigma (Li et al., Reference Li, Huang, Jiao, O’Dea, Zhu and Christensen2018; Nathan & Nathan, Reference Nathan and Nathan2020). A balanced and nuanced approach that takes into account both the risk and benefits of social media for SITB outcomes is needed to effectively consider the many factors that likely mediate and moderate social media effects.

Brief Overview of Methods Used to Study the Relationship Between SITB and Social Media

A brief historical overview on the methodological approaches most commonly used in social media and SITB research is both helpful in contextualizing the risks and benefits and in surfacing methodological frontiers in this domain. In general, SITB-focused research aims have (1) described online content and activity related to SITB, (2) explored the relationship between online activity and SITB, and (3) identified risks germane to intervention efforts. While these efforts have laid the theoretical and empirical foundations necessary for inferring and anticipating risks and benefits and for understanding key mechanisms, they have been less effective in surfacing and disentangling clear causal relationships between social media use and SITB behaviors or in describing the moderating role preexisting SITB vulnerability plays in these relationships.

In general, research documenting potential effects of SITB-related content and exchange has been more straightforward to generate than research aimed at understanding causal relationships between online activity and SITB; in part because the latter requires innovative methods that balance privacy and ethical concerns with the need for cross-ecological and granular approaches capable of disentangling effects. Moreover, because the nature of communication technologies is so dynamic, the research methods required to understand effects must also be dynamic. Most early work focused on content and thematic analyses to investigate common themes in online discussions about self-injury and suicide (Rodham et al., Reference Rodham, Gavin and Miles2007; Whitlock et al., Reference Whitlock, Powers and Eckenrode2006). Surveys were (and still are) used to assess motives for social media use and to understand the perceived effects of use (Lewis & Michal, Reference Lewis and Michal2016).

Recent advances in the application of computational methods to social media research have paved the way for investigation of links between online activities and SITB risk, largely through tracking patterns in linguistic and behavioral markers (De Choudhury et al., Reference De Choudhury, Kiciman, Dredze, Coppersmith and Kumar2016; Du et al., Reference Du, Zhang and Luo2018). Ecological momentary assessments (EMA), or diary methodologies, have been used to understand the relationship between social media use and outcomes related to mental health. For example, EMA methods were used to understand what behaviors young people engage in instead of self-injury (Fitzpatrick et al., Reference Fitzpatrick, Kranzler, Fehling, Lindqvist and Selby2020). Longitudinal studies have begun yielding results, but even these are limited by challenges in disentangling between- from within-effects of media use, understanding risks and benefits accrued to vulnerable subgroups, and the way that both developmental stage and specific social media affordances interact with social media use (Schemer et al., Reference Schemer, Masur, Geiß, Müller and Schäfer2020). In sum, research focused on the intersection of SITB and social media use has evolved from a focus on more static content in online communities (precursors to social media) to more dynamic interactions between user behaviors, content, and offline markers over time. While important methodological challenges remain, much has been learned; this is the focus of the following sections.

Exposure to Suicide and Self-Injury Content

As with traditional media, at least some research documents a link between exposure to suicidal and self-injury social media content and increased risk for SITB experiences. Exposure to digital SITB-related content is not infrequent – in one study, 25% of young people were exposed to suicide stories through social media (Dunlop et al., Reference Dunlop, More and Romer2011). This is concerning because increased exposure to self-injury-related content has been associated with decreased aversion to self-injury and to future suicidal ideation in past work (Franklin et al., Reference Franklin, Fox and Franklin2016) and because habituation to SITB content may reduce barriers to, and increase the acquired capability for, suicide (Massing-Schaffer & Nesi, Reference Massing-Schaffer and Nesi2020). Moreover, such risks may not diminish over time. For example, in a study of effects of exposure to self-harm content on Instagram, researchers found that lifetime exposure to self-harm content was associated with increased SITB risk. Furthermore, exposure was related to an increase in self-harm behaviors, suicidal ideation, and hopelessness one month later, even when controlling for preexisting SITB vulnerability (Arendt et al., Reference Arendt, Scherr and Romer2019).

While it is possible that well-moderated sites could minimize harm resulting from unregulated exposure to triggering content, empirical evidence suggests that even with site moderation individuals can be exposed to triggering graphic or emotional images or text (Baker & Lewis, Reference Baker and Lewis2013; Lewis & Michal, Reference Lewis and Michal2016), including tips on concealment, suicidal ideation, or plans (Dyson et al., Reference Dyson, Hartling and Shulhan2016). Indeed, in the aforementioned Instagram study, only 20% of those who reported seeing self-harm content intentionally searched for it (Arendt et al., Reference Arendt, Scherr and Romer2019). Further, some studies indicate that a subgroup of individuals access online communities in order to sustain or trigger self-injury and share maladaptive techniques (Lewis & Seko, Reference Lewis and Seko2016; Whitlock et al., Reference Whitlock, Powers and Eckenrode2006).

Awareness of the potential for social media content to have harmful effects has led to an increase in moderation efforts, often by platform developers themselves. Popular social media platforms like Instagram, for example, have built in “sensitivity screens” (i.e., trigger warnings) that are meant to shield content related to self-injury and other harmful behaviors enabling users to view content if they clear the shield (Carman, Reference Carman2019). However, even these efforts require empirical study since, in this case, evidence suggests that use of trigger warnings to decrease risk of SITB-related harm has relatively limited effects on distress (Sanson et al., Reference Sanson, Strange and Garry2019) and may increase anticipatory anxiety in some cases (Gainsburg & Earl, Reference Gainsburg and Earl2018). Effects of what a user does in response to a trigger warning is also less intuitive than it might seem. For example, a study focused on self-injury related activity on TalkLife, a mobile peer-support app, showed that choosing to dismiss a trigger warning and view self-injury content was related both to greater intentions to injure and greater ability to resist injuring within a week’s time (Kruzan et al., Reference Kruzan, Whitlock and Bazarova2021). Notably, posting triggering content was related to increased odds of both self-injury thoughts and behaviors. In sum, more work is needed to explicate both the factors that contribute to effects related to exposure to SITB content and the potential protective value of moderation efforts, like trigger warnings.

The Downside of Social Connection on Social Media: Normalization and Narrative Reinforcement

The fact that self-injury and suicide-related posts so frequently co-occur with themes of loneliness underscores the important role that social connection plays in mental health and well-being (Cavazos-Rehg et al., Reference Cavazos-Rehg, Krauss and Sowles2017). Indeed, the promise of rich social connection is one of the factors that makes participation in social media so appealing. However, empirical evidence suggests that the “social” part of social media is simultaneously a risk and a protective factor for SITB. While the perceived and actual social support that comes from social media’s ability to connect young people struggling with self-injury and suicide can be beneficial and SITB-protective, regular exposure to SITB content and association with other individuals struggling with SITB may expose vulnerable adolescents to communities where self-injury is normalized or encouraged, even if not overtly or consciously (Rodham et al., Reference Rodham, Gavin and Miles2007; Whitlock et al., Reference Whitlock, Powers and Eckenrode2006). This “normalization effect” is commonly seen in studies of online communication about self-injury where young people discuss self-injury thoughts and behaviors in detail and often minimize the severity of self-injury and its consequences (Dyson et al., Reference Dyson, Hartling and Shulhan2016). Moreover, the tendency for individuals to co-construct and then reinforce foundational narratives, sometimes termed “narrative reinforcement,” that essentially justifies the need for and use of SITB-linked activities, can lead to desensitization and normalization of behavior, especially when self-injury is depicted as painless and effective (Whitlock et al., Reference Whitlock, Lader and Conterio2007).

Even when a user is trying to minimize exposure to triggering content, most studies show that it is common for pro-recovery messages and encouragement to occur alongside pro-self-injury posts and comments, such as advice on how to injure safely and how to conceal wounds (Lavis & Winter, Reference Lavis and Winter2020; Whitlock et al., Reference Whitlock, Powers and Eckenrode2006). This may not only normalize self-injury, but may also trigger SITB-impulses or discourage use of alternative coping strategies or professional help seeking (Dyson et al., Reference Dyson, Hartling and Shulhan2016; Smithson et al., Reference Smithson, Sharkey and Hewis2011). In sum, while the emotional support received through social media sites can positively influence the recovery process, this support may detract from the severity of the behavior, potentially slowing the change process (Dyson et al., Reference Dyson, Hartling and Shulhan2016).

Contagion: Spread and Scale of Social Media Messages

The idea that exposure to a behavior through media may be “contagious” is a subject of long-standing research interest. Research shows both an increase in the number of SITB themes in on- and offline media, and concomitant concern that such content may contribute to onset or maintenance of SITB among vulnerable individuals, mostly likely through social modelling (Jarvi et al., Reference Jarvi, Jackson, Swenson and Crawford2013). While the adverse impact of SITB social media content on individuals with existing vulnerabilities is intuitive, recent work suggests that even individuals without existing vulnerabilities may be at risk of adverse outcomes from SITB-related themes in social media. For example, there is evidence that viewing suicide-cluster-related posts (e.g., vigils, memorials), online news articles related to suicide, and watching the Netflix series 13 Reasons Why (which features suicidal content) is associated with increased odds of suicidal ideation and attempts, among students both with and without prior self-injury history (Swedo et al., Reference Swedo, Beauregard and de Fijter2020). This study did not control for other known risk factors, like depression or anxiety, and it cannot rule out the possibility that other important preexisting vulnerabilities exist, but it does suggest that even individuals without prior self-injury history are adversely affected by some media content. This possibility is also implicit in research that finds an over 14% increase in population-based suicide trends for young people between 10 and 19 (Niederkrotenthaler et al., Reference Niederkrotenthaler, Stack and Till2019) and “excess” hospitalizations for suicide attempts among young people (Cooper et al., Reference Cooper, Bard, Wallace, Gillaspy and Deleon2018) following the release of 13 Reasons Why.

In a similar vein, research reveals that individuals who post suicidal content are more tightly clustered in friend, or reposting groups, than users who do not post suicide-related content. This supports the idea that individuals tend to gravitate to like-minded others online in ways that may heighten likelihood of narrative reinforcement, and concomitantly, risk of spread among those most vulnerable (Colombo et al., Reference Colombo, Burnap, Hodorog and Scourfield2016). However, the authors also note that re-tweeting behavior connects users whose posts contain suicidal ideation with users whose posts do not, providing evidence for the potential of contagion across diverse networks.

Cyberbullying

Bullying is a long-standing source of stress for young people and this holds as true in online social settings as it does in offline social settings (John et al., Reference John, Glendenning and Marchant2018). Cyberbullying, a term used to describe bullying that occurs online, is also associated with heightened risk for SITB. Notably, it is not just the victims of cyberbullying who are at elevated SITB risk. A recent meta-analysis shows that youth victims of cyberbullying are over twice likely to engage in self-harm, to report a suicide attempt, and to report suicidal thoughts, when compared to nonvictims (John et al., Reference John, Glendenning and Marchant2018). Even one episode of cybervictimization increases risk of suicidal ideation (Hirschtritt et al., Reference Hirschtritt, Ordóñez, Rico and LeWinn2015). Moreover, the risk of SITB after a cyberbullying incident increases significantly among individuals with existing vulnerabilities. Indeed, in a study of adolescents presenting to Canadian emergency departments for mental health complaints, those reporting histories of cybervictimization were over 11 times more likely to report suicidal ideation (Alavi et al., Reference Alavi, Reshetukha and Prost2017). Also, being both a victim and perpetrator of cyberbullying doubles the risk of reporting suicidal thoughts when compared to those who have one of these experiences (Bonanno & Hymel, Reference Bonanno and Hymel2013; John et al., Reference John, Glendenning and Marchant2018).

Heavy Social Media Use

Research has also shown that risk of NSSI and SITB increases with heavy social media use (Lee et al., Reference Lee, Park, Han, Kim, Chun and Park2016; Twenge & Campbell, Reference Twenge and Campbell2019). Indeed, in a study of Canadian high school students, those who spent more than two hours a day on social media had were five times more likely to experience suicidal ideation when compared to peers reporting fewer than two hours of social media use a day (Sampasa-Kanyinga & Lewis, Reference Sampasa-Kanyinga and Lewis2015). Similarly, adolescents who report heavy digital media use are twice as likely to report suicidal thoughts, suicide plans, and suicide attempts when compared to light users, according to a large survey study (Twenge & Campbell, Reference Twenge and Campbell2019). And, in a recent review of seven studies researchers documented a direct association between heavy social media/internet use and suicide attempts (Sedgwick et al., Reference Sedgwick, Epstein, Dutta and Ougrin2019).

Interestingly, some studies show that some social media use is better than no use (Kim, Reference Kim2012; Lee et al., Reference Lee, Park, Han, Kim, Chun and Park2016). These findings are consistent with broader literature on social media use and well-being that suggests curvilinear relationships between social media use and well-being with benefits derived from some use, versus no use, and risks increasing most significantly from low or moderate to heavy use (Kim, Reference Kim2012; Przybylski & Weinstein, Reference Przybylski and Weinstein2017; Twenge & Campbell, Reference Twenge and Campbell2019). Specifically, risks increase most significantly from low (<1 hour a day) or moderate to heavy use (>5 hours a day) (Twenge & Campbell, Reference Twenge and Campbell2019). One explanatory theory is that time spent on social media displaces other activities that could be beneficial for mental health, such as physical activity, in-person social interaction, and sleep – all risk factors for suicide (Porras-Segovia et al., Reference Porras-Segovia, Pérez-Rodríguez and López-Esteban2019; Sedgwick et al., Reference Sedgwick, Epstein, Dutta and Ougrin2019; Verkooijen et al., Reference Verkooijen, de Vos and Bakker-Camu2018).

Social Support and Connectedness

One of the primary perceived benefits of social media use is the exchange of social support not bounded by time or geography. This is important because social support is known to buffer effects of negative life events, enhance mental health and well-being (Cutrona & Suhr, Reference Cutrona and Suhr1992), decrease feelings of isolation, lead to sense of purpose, and to promote feelings of acceptance or being understood (Daine et al., Reference Daine, Hawton, Singaravelu, Stewart, Simkin and Montgomery2013). Opportunities for social support through social media can be powerful for young people with SITB, since stigma is often an impediment to offline help and support seeking. Online environments allow for anonymity and carry few clear social penalties for candid sharing, which makes such environments particularly attractive to individuals concerned about disclosing SITB-related behaviors or impulses to people in their offline lives (Duggan et al., Reference Duggan, Heath, Lewis and Baxter2012). And, since social support is a critical protective factor for SITB (Joiner et al., Reference Joiner, Ribeiro and Silva2012), social exchange in social media forums offers a promising alternative to offline sharing.

It is thus unsurprising that empirical evidence suggests that young people with SITB histories use the Internet more often than their peers (De Riggi et al., Reference De Riggi, Lewis and Heath2018; Memon et al., Reference Memon, Sharma, Mohite and Jain2018) and that it is a preferred means for seeking and receiving help (Frost & Casey, Reference Frost and Casey2016). For example, youth with suicidal ideation are more likely to report online-only friendships, relative to those without suicidal ideation, and these friendships appear to buffer the harmful effects of relational victimization and stress (Massing-Schaffer et al., Reference Massing-Schaffer and Nesi2020). Nearly one-third of young people with a history of self-injury had reported online help seeking in one study – and those who sought help online were more distressed and suicidal than those who had not (Frost & Casey, Reference Frost and Casey2016). Additionally, adolescents with more recent NSSI have higher levels of online support seeking, compared to those with past or no NSSI history (De Riggi et al., Reference De Riggi, Lewis and Heath2018). Even when individuals have a strong support system offline, they may have trouble accessing support in times when they need it (Kruzan et al., Reference Kruzan, Whitlock and Bazarova2021; Lavis & Winter, Reference Lavis and Winter2020). The immediate nature of social support exchange on social media may be important for individuals who struggle with SITB given that intense urges are commonly cited as a key barrier to behavior change (Kruzan & Whitlock, Reference Kruzan and Whitlock2019) and findings showing that young people frequently look for, and receive, emotional support online when they are experiencing an urge (Lewis & Michal, Reference Lewis and Michal2016; Rodham et al., Reference Rodham, Gavin and Miles2007).

Not all social support is equal, however. While some work suggests that young people perceive benefits from participation (Brown et al., Reference Brown, Fischer, Goldwich and Plener2020; Lewis & Michal, Reference Lewis and Michal2016), others note the “mundane” or safe nature of the advice, which leads to questions of actual utility (Smithson et al., Reference Smithson, Sharkey and Hewis2011). The availability and immediate accessibility of such support is nonetheless quite appealing – as is the fact that support is exchanged among peers with shared experience and experiential knowledge (Marchant et al., Reference Marchant, Hawton and Stewart2017; Thoits, Reference Thoits2011). Research consistently documents a preference for peer versus professional support for NSSI and the tendency for young people to confide SITB in peers versus others in their social network (De Riggi et al., Reference De Riggi, Lewis and Heath2018), something social media facilitates organically.

The question of whether such peer support is helpful for SITB outcomes remains nascent. Early work showed positive associations between social support received and decreased self-injury behaviors (Murray & Fox, Reference Murray and Fox2006), but research directly connecting social support through social media use to its effects on SITB outcomes is limited. One experimental study varying exposure to hopeful or hopeless YouTube videos, found that hopeful messages were associated with increased positive attitudes toward recovery, suggesting shifts in recovery-oriented subjective norms (Lewis et al., Reference Lewis, Seko and Joshi2018). Interestingly, there were no attitudinal changes in those viewing hopeless messages.

Self-Knowledge and Expression

Beyond the use of social media as a source of social support is its role in facilitating self-expression and exploration. Being able to connect and provide mutual support, narrate experiences, and self-reflect, while also maintaining autonomy and anonymity, are all identified as clear benefits to social media use among individuals with SITB history (Coulson et al., Reference Coulson, Bullock and Rodham2017; Rodham et al., Reference Rodham, Gavin, Lewis, St. Denis and Bandalli2013). Indeed, self-oriented motivations such as understanding NSSI experience or expressing oneself through narrative description or other forms of creative expression are potent motives of online activity (Seko et al., Reference Seko, Kidd, Wiljer and McKenzie2015). Insight gleaned through sharing one’s story and encountering resonance in others’ stories is important in recovery and is associated with active information seeking, increased self-efficacy, and enhanced self-awareness (Kruzan & Whitlock, Reference Kruzan and Whitlock2019). Since young people frequently provide advice to others online (Seko et al., Reference Seko, Kidd, Wiljer and McKenzie2015; Whitlock et al., Reference Whitlock, Powers and Eckenrode2006), it is also possible that seeing oneself as a valued mentor to others with shared struggles may increase commitment to recovery processes. Online self-presentation and expression can assist in developing self-understanding, and be associated with beneficial shifts in self-perceptions (Kruzan & Won, Reference Kruzan and Won2019; Valkenburg, Reference Valkenburg2017).

Exchange of Resources and Information

Use of social media to both identify and exchange coping techniques is also common and potentially beneficial (Duggan et al., Reference Duggan, Heath, Lewis and Baxter2012) for individuals navigating self-injury or suicidal thoughts and urges (Lavis & Winter, Reference Lavis and Winter2020; Lewis & Michal, Reference Lewis and Michal2016). Tips on how to reduce the urge or replace self-injury behaviors are also highly salient. For example, in a study of three different social media sites (Reddit, Instagram, Twitter) researchers found a rich exchange of coping advice related to visual, distraction, and sensory techniques effective in reducing urges (Lavis & Winter, Reference Lavis and Winter2020). There is also evidence that topics related to professional help seeking for SITB are a feature of some online exchange (Lavis & Winter, Reference Lavis and Winter2020), but whether this is common remains unclear since there is work suggesting that online exchange does not lead to increased professional help seeking (R. C. Brown et al., Reference Brown, Fischer, Goldwich and Plener2020) and because this line of inquiry remains underexplored.

The power of social media exchange to alter offline behavior does open opportunity for development of more formal intervention. Online peers may be uniquely positioned to provide advice on treatment and coping strategies, and this advice may be easier to digest, and apply, when coming from someone who has “been there” (Naslund et al., Reference Naslund, Aschbrenner, Marsch and Bartels2016). Such exchange can be considered a unique and potent form of expertise (Marchant et al., Reference Marchant, Hawton and Stewart2017) that can be leveraged to deliver coping- and recovery-supportive messages and resources. Since not all resources exchanged through social media are evidence-based, and some can be harmful or depict self-injury as an effective coping strategy (Lewis & Baker, Reference Lewis and Baker2011; Seko & Lewis, Reference Seko and Lewis2018), it is crucial that the nature of naturally occurring exchange is understood and mitigated when potentially harmful.

Mental Health History

Just as prior mental health history has the potential to moderate the effects of social media use on SITB outcomes, it can also mediate this relationship. In some work, the relationship between heavy social media use and NSSI was mediated by factors such as suicidality, anxiety, and affective and psychotic disorders (Mészáros et al., Reference Mészáros, Győri, Horváth, Szentiványi and Balázs2020).

Affect and Intentions

Emotional affect and motives for use are also likely mediators of the relationship between social media and SITB. The connection between NSSI and affect is well established, and may be particularly important in understanding interactions that lead to risks or benefits of social media use, since both NSSI (Klonsky, Reference Klonsky2007) and social media use can be ways to modulate emotion (Rideout & Fox, Reference Rideout and Fox2018). Indeed, young people can deliberately seek out uplifting, distressing, or neutral messages that reflect, and may impact, their own affective state. While few studies have examined the role of mood in the relationship between SITB and social media use, young people with lived NSSI experience often discuss mood as part of their use of social media and related technologies (Seko et al., Reference Seko, Kidd, Wiljer and McKenzie2015).

Interactional Factors

In addition to the amount of use, the way someone uses social media is consistently connected to mental health outcomes (Verduyn et al., Reference Verduyn, Ybarra, Résibois, Jonides and Kross2017). This trend holds for SITB-related studies, as well, but the patterns of effects are not entirely intuitive. In a cross-sectional study of the association between SITB (both NSSI and suicidal thoughts and behavior) and social media use type among Norwegian university students, researchers found that active public social media use (e.g., posting, commenting) was associated with increased odds of NSSI ideation and behaviors and suicide attempts, whereas social private use (e.g., messaging friends) was associated with reduced odds of all NSSI and suicide outcomes (Kingsbury et al., Reference Kingsbury, Reme and Skogen2021). Passive nonsocial use (e.g., reading news) was associated with decreased odds of NSSI ideation, NSSI, and suicidal ideation, and active nonsocial use (e.g., for studies) was associated with decreased odds of suicide attempt. In parallel with the broader literature on social media effects on well-being, these findings suggest a nuanced relationship that differs by types of engagement.

Social Comparison Processes

Social comparison is a primary mechanism through which social media use impacts mental health and well-being (Appel et al., Reference Appel, Gerlach and Crusius2016; Kruzan & Won, Reference Kruzan and Won2019; Wang et al., Reference Wang, Wang, Gaskin and Hawk2017). Upward social comparison – wherein individuals compare themselves to those who are perceived as better off – has been associated with reductions in self-esteem, increased negative affect, and envy (Appel et al., Reference Appel, Gerlach and Crusius2016; Wang et al., Reference Wang, Wang, Gaskin and Hawk2017). Consonant with this general trend, Kingsbury et al., (Reference Kingsbury, Reme and Skogen2021) found that the presence of social comparison is associated with increased odds for all NSSI and suicidal outcomes. However, social comparison processes may look slightly different on social media sites or forums that are structured almost entirely around conversations about SITB (e.g., TalkLife) where the general positivity bias documented in mainstream social media does not exist. In light of its influence, the role of social comparison for SITB risk in social media should be explored further.

Opportunities: Amplifying the Beneficial Potential of Social Media

In addition to the opportunities inherent in the nature of the technology’s design, such as the possibility for enhanced social connection and belonging, there are unique opportunities for: (1) identification/detection, (2) intervention, (3) prevention, and (4) awareness/stigma reduction.

Challenges: Minimizing the Negative Potential of Social Media

Some of the challenges of studying and understanding the relationship between social media use and SITB outcomes are broadly related to (1) creating and maintaining a safe environment, (2) methodological innovation, and (3) privacy and ethical considerations.