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• Review Article
• Published: 07 May 2024

Mechanisms linking social media use to adolescent mental health vulnerability

• Amy Orben   ORCID: orcid.org/0000-0002-2937-4183 1 ,
• Adrian Meier   ORCID: orcid.org/0000-0002-8191-2962 2 ,
• Tim Dalgleish   ORCID: orcid.org/0000-0002-7304-2231 1 &
• Sarah-Jayne Blakemore 3 , 4  

Nature Reviews Psychology ( 2024 ) Cite this article

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• Psychiatric disorders
• Science, technology and society

Research linking social media use and adolescent mental health has produced mixed and inconsistent findings and little translational evidence, despite pressure to deliver concrete recommendations for families, schools and policymakers. At the same time, it is widely recognized that developmental changes in behaviour, cognition and neurobiology predispose adolescents to developing socio-emotional disorders. In this Review, we argue that such developmental changes would be a fruitful focus for social media research. Specifically, we review mechanisms by which social media could amplify the developmental changes that increase adolescents’ mental health vulnerability. These mechanisms include changes to behaviour, such as sharing risky content and self-presentation, and changes to cognition, such as modifications in self-concept, social comparison, responsiveness to social feedback and experiences of social exclusion. We also consider neurobiological mechanisms that heighten stress sensitivity and modify reward processing. By focusing on mechanisms by which social media might interact with developmental changes to increase mental health risks, our Review equips researchers with a toolkit of key digital affordances that enables theorizing and studying technology effects despite an ever-changing social media landscape.

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Introduction.

Adolescence is a period marked by profound neurobiological, behavioural and environmental changes that facilitate the transition from familial dependence to independent membership in society 1 , 2 . This critical developmental stage is also characterized by diminished well-being and increased vulnerability to the onset of mental health conditions 3 , 4 , 5 , particularly socio-emotional disorders such as depression, and eating disorders 4 , 6 (Fig. 1 ). Notable symptoms of socio-emotional disorders include heightened negative affect, mood dysregulation and an increased focus on distress or challenges concerning interpersonal relationships, including heightened sensitivity to peers or perceptions of others 6 . Although some risk factors for socio-emotional disorders do not necessarily occur in adolescence (including genetic predispositions, adverse childhood experiences and poverty 7 , 8 , 9 ), the unique developmental characteristics of this period of life can interact with pre-existing vulnerabilities, increasing the risk of disorder onset 10 .

Meta-analytic proportion of age of onset of anxiety (red), obsessive-compulsive disorder (purple), eating disorders (orange), personality disorders (green), schizophrenia (grey) and mood disorders (blue). The peak age of onset (dotted lines) is 5.5 and 15.5 years for anxiety, 14.5 years for obsessive-compulsive disorder, 15.5 years for eating disorders and 20.5 years for personality disorders, schizophrenia and mood disorders. Adapted from ref. 258 , CC BY 4.0 ( https://creativecommons.org/licenses/by/4.0/ ).

Over the past decade, declines in adolescent mental health have become a great concern 11 , 12 . The prevalence of socio-emotional disorders has increased in the adolescent age range (10–24 years 2 ) 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , leading to mounting pressures on child and adolescent mental health services 16 , 21 , 22 . This increase has not been as pronounced among other age groups when compared with adolescents 20 , 22 , 23 (measured in ref.  20 , ref.  22 and ref.  23 as age 12–25 years, 12–20 years and 18–25 years, respectively), even if some studies have found increases across the entire lifespan 24 , 25 . Although these trends might not be generalizable across the world 26 or to subclinical indicators of distress 15 , similar trends have been found in a range of countries 27 . Declines in adolescent mental health, especially socio-emotional problems, are consistent across datasets and researchers have argued that they are not solely driven by changes in social attitudes, stigma or reporting of distress 28 , 29 .

Concurrently, adolescents’ lives have become increasingly digital, with most young people using social media platforms throughout the day 30 . Ninety-five per cent of UK adolescents aged 15 years use social media 31 , and 50% of US adolescents aged 13–17 years report being almost constantly online 32 . The social media environment impacts adolescent and adult life across many domains (for example, by enabling social communication or changing the way news is accessed) and influences individuals, dyads and larger social systems 33 , 34 , 35 , 36 . Because social media is inherently social and relational 37 , it potentially overlaps and interacts with the developmental changes that make adolescents vulnerable to the onset of mental health problems 38 , 39 (Fig. 2 ). Thus, it has been intensely debated whether the increase in social media use during the past decade has a causal role in the decline of adolescent mental health 40 . Indeed, rapid changes to the environment experienced before and during adolescence might be a fruitful area to explore when examining current mental health trends 41 .

During adolescence, the interaction between genetic programming (yellow), social determinants (red) and environmental factors (blue), as well as the developmental changes discussed in this Review, increases the risk for onset of mental health conditions. Digital environments, mediated behaviours and experiences, and the impact that this technology has on society and economy more generally, are one aspect of the complex forces that might lead to the declines in adolescent mental health observed in the last decade. Adapted from ref. 259 , Springer Nature Limited.

Although there are many environmental changes that could be relevant, a substantial body of research has emerged to investigate the potential link between social media use and declines in adolescent mental health 42 , 43 using various research approaches, including cross-sectional studies 44 , longitudinal observational data analyses 45 , 46 , 47 and experimental studies 48 , 49 . However, the scientific results have been mixed and inconclusive (for reviews, see refs. 43 , 50 , 51 , 52 , 53 ), which has made it difficult to establish evidence-based recommendations, regulations and interventions aimed at ensuring that social media use is not harmful to adolescents 54 , 55 , 56 , 57 .

Many researchers attribute the mixed results to insufficient study specificity. For instance, the relationship between social media use and mental health varies notably across individuals 45 , 58 and developmental time windows 59 . Yet studies often examine adolescents without differentiating them based on age or developmental stage 60 , which prevents systematic accounts of individual and subgroup differences. Additionally, most studies only rely on self-reported measures of time spent on social media 61 , 62 , and overlook more nuanced aspects of social media use such as the nature of the activities 63 and the content or features that users engage with 52 . These factors need to be considered to unpack any broader relationships 35 , 64 , 65 , 66 . Furthermore, the measurement of mental health often conflates positive and negative mental health outcomes as well as various mental health conditions, which could all be differentially related to social media use 52 , 67 .

This research space presents substantial complexity 68 . There is an ever-increasing range of potential combinations of social media predictors, well-being and mental health outcomes and participant groups of varying backgrounds and demographics that can become the target of scientific investigation. However, the pressure to deliver policy and public-facing recommendations and interventions leaves little time to investigate comprehensively each of these combinations. Researchers need to be able to pinpoint quickly the research programmes with the maximum potential to create translational and real-world impact for adolescent mental health.

In this Review, we aim to delineate potential avenues for future research that could lead to concrete interventions to improve adolescent mental health by considering mechanisms at the nexus between pre-existing processes known to increase adolescent mental health vulnerability and digital affordances introduced by social media. First, we describe the affordance approach to understanding the effects of social media. We then draw upon research on adolescent development, mental health and social media to describe behavioural, cognitive and neurobiological mechanisms by which social media use might amplify changes during adolescent development to increase mental health vulnerability during this period of life. The specific mechanisms within each category were chosen because they have a strong evidence base showing that they undergo substantive changes during adolescent development, are implicated in mental health risk and can be modulated by social media affordances. Although the ways in which social media can also improve mental health resilience are not the focus of our Review and therefore are not reviewed fully here, they are briefly discussed in relation to each mechanism. Finally, we discuss future research focused on how to systematically test the intersection between social media and adolescent mental health.

Social media affordances

To study the impact of social media on adolescent mental health, its diverse design elements and highly individualized uses must be conceptualized. Initial research predominately related access to or time spent on social media to mental health outcomes 46 , 69 , 70 . However, social media is not similar to a toxin or nutrient for which each exposure dose has a defined link to a health-related outcome (dose–response relationship) 56 . Social media is a diverse environment that cannot be summarized by the amount of time one spends interacting with it 71 , 72 , and individual experiences are highly varied 45 .

Previous psychological reviews often focused on social media ‘features’ 73 and ‘affordances’ 74 interchangeably. However, these terms have distinct definitions in communication science and information systems research. Social media features are components of the technology intentionally designed to enable users to perform specific actions, such as liking, reposting or uploading a story 75 , 76 . By contrast, affordances describe the perceptions of action possibilities users have when engaging with social media and its features, such as anonymity (the difficulty with which social media users can identify the source of a message) and quantifiability (how countable information is).

The term ‘affordance’ came from ecological psychology and visuomotor research, and was described as mainly determined by human perception 77 . ‘Affordance’ was later adopted for design and human–computer interaction contexts to refer to the action possibilities that are suggested to the user by the technology design 78 . Communication research synthesizes both views. Affordances are now typically understood as the perceived — and therefore flexible — action possibilities of digital environments, which are jointly shaped by the technology’s features and users’ idiosyncratic perceptions of those features 79 .

Latent action possibilities can vary across different users, uses and technologies 79 . For example, ‘stories’ are a feature of Instagram designed to share content between users. Stories can also be described in terms of affordances when users perceive them as a way to determine how long their content remains available on the platform (persistence) or who can see that content (visibility) 80 , 81 , 82 , 83 , 84 . Low persistence (also termed ephemerality) and comparatively low visibility can be achieved through a technology feature (Instagram stories), but are not an outcome of technology use itself; they are instead perceived action possibilities that can vary across different technologies, users and designs 79 .

The affordances approach is particularly valuable for theorizing at a level above individual social media apps or specific features, which makes this approach more resilient to technological changes or shifts in platform popularity 79 , 85 . However, the affordances approach can also be related back to specific types of social media by assessing the extent to which certain affordances are ‘built into’ a particular platform through feature design 35 . Furthermore, because affordances depend on individuals’ perceptions and actions, they are more aligned than features with a neurocognitive and behavioural perspective to social media use. Affordances, similar to neurocognitive and behavioural research, emphasize the role of the user (how the technology is perceived, interpreted and used) rather than technology design per se. In this sense, the affordances approach is essential to overcome technological determinism of mental health outcomes, which overly emphasizes the role of technology as the driver of outcomes but overlooks the agency and impact of the people in question 86 . This flexibility and alignment with psychological theory has contributed to the increasing popularity of the affordance approach 35 , 73 , 74 , 85 , 87 and previous reviews have explored relevant social media affordances in the context of interpersonal communication among adults and adolescents 35 , 88 , 89 , adolescent body image concerns 73 and work contexts 33 . Here, we focus on the affordances of social media that are relevant for adolescent development and its intersection with mental health (Table  1 ).

Behavioural mechanisms

Adolescents often use social media differently to adults, engaging with different platforms and features and, potentially, perceiving or making use of affordances in distinctive ways 35 . These usage differences might interact with developmental characteristics and changes to amplify mental health vulnerability (Fig.  3 ). We examine two behavioural mechanisms that might govern the impact of social media use on mental health: risky posting behaviours and self-presentation.

Social media affordances can amplify the impact that common adolescent developmental mechanisms (behavioural, cognitive and neurobiological) have on mental health. At the behavioural level (top), affordances such as permanence and publicness lead to an increased impact of risk-taking behaviour on mental health compared with similar behaviours in non-mediated environments. At the cognitive level (middle), high quantifiability influences the effects of social comparison. At the neurobiological level (bottom), low synchronicity can amplify the effects of stress on the developing brain.

Risky posting behaviour

Sensation-seeking peaks in adolescence and self-regulation abilities are still not fully developed in this period of life 90 . Thus, adolescents often engage in more risky behaviours than other age groups 91 . Adolescents are more likely to take risks in situations involving peers 92 , 93 , perhaps because they are motivated to avoid social exclusion 94 , 95 . Whether adolescent risk-taking behaviour is inherently adaptive or maladaptive is debated. Although some risk-taking behaviours can be adaptive and part of typical development, others can increase mental health vulnerability. For example, data from a prospective UK panel study of more than 5,500 young people showed that engaging in more risky behaviours (including social and health risks) at age 16 years increases the odds of a range of adverse outcomes at age 18 years, such as depression, anxiety and substance abuse 96 .

Social media can increase adolescents’ engagement in risky behaviours both in non-mediated and mediated environments (environments in which the behaviour is executed in or through a technology, such as a mobile phone and social media). First, affordances such as quantifiability in conjunction with visibility and association (the degree with which links between people, between people and content or between a presenter and their audience can be articulated) can promote more risky behaviours in non-mediated environments and in-person social interactions. For example, posts from university students containing references to alcohol gain more likes than posts not referencing alcohol and liking such posts predicts an individual’s subsequent drinking habits 97 . Users expecting likes from their audience are incentivized to engage in riskier posting behaviour (such as more frequent or more extreme posts containing references to alcohol). The relationship between risky online behaviour and offline behaviour is supported by meta-analyses that found a positive correlation between adolescents’ social media use and their engagement in behaviours that might expose them to harm or risk of injury (for example, substance use or risky sexual behaviours) 98 . Further, affordances such as persistence and visibility can mean that risky behaviours in mediated and non-mediated environments remain public for long periods of time, potentially influencing how an adolescent is perceived by peers over the longer term 39 , 99 .

Adolescence can also be a time of more risky social media use. For most forms of semi-public and public social media use, users typically do not know who exactly will be able to see their posts. Thus, adolescents need to self-present to an ‘imagined audience’ 100 and avoid posting the wrong kind of content as the boundaries between different social spheres collapse (context collapse 101 ). However, young people can underestimate the risks of disclosing revealing information in a social media environment 102 . Affordances such as visibility, replicability (social media posts remain in the system and can be screenshotted and shared even if they are later deleted 39 ), association and persistence could heighten the risk of experiencing cyberbullying, victimization and online harassment 103 . For example, adolescents can forward privately received sexual images to larger friendship groups, increasing the risk of online harassment over the subject of the sexual images 104 . Further, low bandwidth (a relative lack of socio-emotional cues) and high anonymity have the potential to disinhibit interactions between users and make behaviours and reactions more extreme 105 , 106 . For example, anonymity was associated with more trolling behaviours during an online group discussion in an experiment with 242 undergraduate students 107 .

Thus, social media might drive more risky behaviours in both mediated and non-mediated contexts, increasing mental health vulnerability. However, the evidence is still not clear cut and often discounts adolescent agency and understanding. For example, mixed-methods research has shown that young people often understand the risks of posting private or sexual content and use social media apps that ensure that posts are deleted and inaccessible after short periods of time to counteract them 39 (even though posts can still be captured in the meantime). Future work will therefore need to investigate how adolescents understand and balance such risks and how such processes relate to social media’s impact on mental health.

Self-presentation and identity

The adolescent period is characterized by an abundance of self-presentation activities on social media 74 , where the drive to present oneself becomes a fundamental motivation for engagement 108 . These activities include disclosing, concealing and modifying one’s true self, and might involve deception, to convey a desired impression to an audience 109 . Compared with adults, adolescents more frequently take part in self-presentation 102 , which can encompass both realistic and idealized portrayals of themselves 110 . In adults, authentic self-presentation has been associated with increased well-being, and inauthentic presentation (such as when a person describes themselves in ways not aligned with their true self) has been associated with decreased well-being 111 , 112 , 113 .

Several social media affordances shape the self-presentation behaviours of adolescents. For example, the editability of social media profiles enables users to curate their online identity 84 , 114 . Editability is further enhanced by highly visible (public) self-presentations. Additionally, the constant availability of social media platforms enables adolescents to access and engage with their profiles at any time, and provides them with rapid quantitative feedback about their popularity among peers 89 , 115 . People receive more direct and public feedback on their self-presentation on social media than in other types of environment 116 , 117 . The affordances associated with self-presentation can have a particular impact during adolescence, a period characterized by identity development and exploration.

Social media environments might provide more opportunities than offline environments for shaping one’s identity. Indeed, public self-presentation has been found to invoke more prominent identity shifts (substantial changes in identity) compared with private self-presentation 118 , 119 . Concerns have been raised that higher Internet use is associated with decreased self-concept clarity. Only one study of 101 adolescents as well as adults reviewed in a 2021 meta-analysis 120 showed that the intensity of Facebook use (measured by the Facebook Intensity Scale) predicted a longitudinal decline in self-concept clarity 3 months later, but the converse was not the case and changes in self-concept clarity did not predict Facebook use 121 . This result is still not enough to show a causal relationship 121 . Further, the affordances of persistence and replicability could also curtail adolescents’ ability to explore their identity freely 122 .

By contrast, qualitative research has highlighted that social media enables adolescents to broaden their horizons, explore their identity and identify and reaffirm their values 123 . Social media can help self-presentation by enabling adolescents to elaborate on various aspects of their identity, such as ethnicity and race 124 or sexuality 125 . Social media affordances such as editability and visibility can also facilitate this process. Adolescents can modify and curate self-presentations online, try out new identities or express previously undisclosed aspects of their identity 126 , 127 . They can leverage social media affordances to present different facets of themselves to various social groups by using different profiles, platforms and self-censorship and curation of posts 128 , 129 . Presenting and exploring different aspects of one’s identity can have mental health implications for minority teens. Emerging research shows a positive correlation between well-being and problematic Internet use in transgender, non-binary and gender-diverse adolescents (age 13–18 years), and positive sentiment has been associated with online identity disclosures in transgender individuals with supportive networks (both adolescent and adult) 130 , 131 .

Cognitive mechanisms

Adolescents and adults might experience different socio-cognitive impacts from the same social media activity. In this section, we review four cognitive mechanisms via which social media and its affordances might influence the link between adolescent development and mental health vulnerabilities (Fig.  3 ). These mechanisms (self-concept development, social comparison, social feedback and exclusion) roughly align with a previous review that examined self-esteem and social media use 115 .

Self-concept development

Self-concept refers to a person’s beliefs and evaluations about their own qualities and traits 132 , which first develops and becomes more complex throughout childhood and then accelerates its development during adolescence 133 , 134 , 135 . Self-concept is shaped by socio-emotional processes such as self-appraisal and social feedback 134 . A negative and unstable self-concept has been associated with negative mental health outcomes 136 , 137 .

Perspective-taking abilities also develop during adolescence 133 , 138 , 139 , as does the processing of self-relevant stimuli (measured by self-referential memory tasks, which assess memory for self-referential trait adjectives 140 , 141 ). During adolescence, direct self-evaluations and reflected self-evaluations (how someone thinks others evaluate them) become more similar. Further, self-evaluations have a distinct positive bias during childhood, but this positivity bias decreases in adolescence as evaluations of the self are integrated with judgements of other people’s perspectives 142 . Indeed, negative self-evaluations peak in late adolescence (around age 19 years) 140 .

The impact of social media on the development of self-concept could be heightened during adolescence because of affordances such as personalization of content 143 (the degree to which content can be tailored to fit the identity, preferences or expectations of the receiver), which adapts the information young people are exposed to. Other affordances with similar impacts are quantifiability, availability (the accessibility of the technology as well as the user’s accessibility through the technology) and public visibility of interactions 89 , which render the evaluations of others more prominent and omnipresent. The prominence of social evaluation can pose long-term risks to mental health under certain conditions and for some users 144 , 145 . For example, receiving negative evaluations from others or being exposed to cyberbullying behaviours 146 , 147 can, potentially, have heightened impact at times of self-concept development.

A pioneering cross-sectional study of 150 adolescents showed that direct self-evaluations are more similar to reflected self-evaluations, and self-evaluations are more negative, in adolescents aged 11–21 years who estimate spending more time on social media 148 . Further, longitudinal data have shown bidirectional negative links between social media use and satisfaction with domains of the self (such as satisfaction with family, friends or schoolwork) 47 .

Although large-scale evidence is still unavailable, these findings raise the interesting prospect that social media might have a negative influence on perspective-taking and self-concept. There is less evidence for the potential positive influence of social media on these aspects of adolescent development, demonstrating an important research gap. Some researchers hypothesize that social media enables self-concept unification because it provides ample opportunity to find validation 89 . Research has also discussed how algorithmic curation of personalized social media feeds (for example, TikTok algorithms tailoring videos viewed to the user’s interests) enables users to reflect on their self-concept by being exposed to others’ experiences and perspectives 143 , an area where future research can provide important insights.

Social comparison

Social comparison (thinking about information about other people in relation to the self 149 ) also influences self-concept development and becomes particularly important during adolescence 133 , 150 . There are a range of social media affordances that can amplify the impact of social comparison on mental health. For example, quantifiability enables like or follower counts to be easily compared with others as a sign of status, which facilitates social ranking 151 , 152 , 153 , 154 . Studies of older adolescents and adults aged, on average, 20 years have also found that the number of likes or reactions received predict, in part, how successful users judge their self-presentation posts on Facebook 155 . Furthermore, personalization enables the content that users see on social media to be curated so as to be highly relevant and interesting for them, which should intensify comparisons. For example, an adolescent interested in sports and fitness content will receive personalized recommendations fitting those interests, which should increase the likelihood of comparisons with people portrayed in this content. In turn, the affordance of association can help adolescents surround themselves with similar peers and public personae online, enhancing social comparison effects 63 , 156 . Being able to edit posts (via the affordance of editability) has been argued to contribute to the positivity bias on social media: what is portrayed online is often more positive than the offline experience. Thus, upward comparisons are more likely to happen in online spaces than downward or lateral comparisons 157 . Lastly, the verifiability of others’ idealized self-presentations is often low, meaning that users have insufficient cues to gauge their authenticity 158 .

Engaging in comparisons on social media has been associated with depression in correlational studies 159 . Furthermore, qualitative research has shown that not receiving as many positive evaluations as expected (or if positive evaluations are not provided quickly enough) increases negative emotions in children and adolescents aged between age 9 and 19 years 39 . This result aligns with a reinforcement learning modelling study of Instagram data, which found that the likes a user receives on their own posts become less valuable and less predictive of future posting behaviour if others in their network receive more likes on their posts 160 . Although this study did not measure mood or mental health, it shows that the value of the likes are not static but inherently social; their impact depends on how many are typically received by other people in the same network.

Among the different types of social comparison that adolescents engage in (comparing one’s achievements, social status or lifestyle), the most substantial concerns have been raised about body-related comparisons. One review suggested that social media affordances create a ‘perfect storm’ for body image concerns that can contribute to both socio-emotional and eating disorders 73 . Social media affordances might increase young people’s focus on other people’s appearances as well as on their own appearance by showing idealized, highly edited images, providing quantified feedback and making the ability to associate and compare oneself with peers constantly available 161 , 162 . The latter puts adolescents who are less popular or receive less social support at particular risk of low self-image and social distress 35 .

Affordances enable more prominent and explicit social comparisons in social media environments relative to offline environments 158 , 159 , 163 , 164 , 165 . However, this association could have a positive impact on mental health 164 , 166 . Initial evidence suggests beneficial outcomes of upward comparisons on social media, which can motivate behaviour change and yield positive downstream effects on mental health 164 , 166 . Positive motivational effects (inspiration) have been observed among young adults for topics such as travelling and exploring nature, as well as fitness and other health behaviours, which can all improve mental health 167 . Importantly, inspiration experiences are not a niche phenomenon on social media: an experience sampling study of 353 Dutch adolescents (mean age 13–15 years) found that participants reported some level of social media-induced inspiration in 33% of the times they were asked to report on this over the course of 3 weeks 168 . Several experimental and longitudinal studies show that inspiration is linked to upward comparison on social media 157 , 164 , 166 . However, the positive, motivating side of social comparison on social media has only been examined in a few studies and requires additional investigation.

Social feedback

Adolescence is also a period of social reorientation, when peers tend to become more important than family 169 , peer acceptance becomes increasingly relevant 170 , 171 , 172 and young people spend increasing amounts of time with peers 173 . In parallel, there is a heightened sensitivity to negative socio-emotional or self-referential cues 140 , 174 , higher expectation of being rejected by others 175 and internalization of such rejection 142 , 176 compared with other phases in life development. A meta-analysis of both adolescents and adults found that oversensitivity to social rejection is moderately associated with both depression and anxiety 177 .

Social media affordances might amplify the potential impact of social feedback on mental health. Wanting to be accepted by peers and increased susceptibility to social rewards could be a motivator for using social media in the first place 178 . Indeed, receiving likes as social reward activated areas of the brain (such as the nucleus accumbens) that are also activated by monetary reward 179 . Quantifiability amplifies peer acceptance and rejection (via like counts), and social rejection has been linked to adverse mental health outcomes 170 , 180 , 181 , 182 . Social media can also increase feelings of being evaluated, the risk of social rejection and rumination about potential rejection due to affordances such as quantifiability, synchronicity (the degree to which an interaction happens in real time) and variability of social rewards (the degree to which social interaction and feedback occur on variable time schedules). For example, one study of undergraduate students found that active communication such as messaging was associated with feeling better after Facebook use; however, this was not the case if the communication led to negative feelings such as rumination (for example, after no responses to the messages) 183 .

In a study assessing threatened social evaluation online 184 , participants were asked to record a statement about themselves and were told their statements would be rated by others. To increase the authenticity of the threat, participants were asked to rate other people’s recordings. Threatened social evaluation online in this study decreased mood, most prominently in people with high sensitivity to social rejection. Adolescents who are more sensitive to social rejection report more severe depressive symptoms and maladaptive ruminative brooding in both mediated and non-mediated social environments, and this association is most prominent in early adolescence 185 . Not receiving as much online social approval as peers led to more severe depressive symptoms in a study of American ninth-grade adolescents (between age 14 and 15 years), especially those who were already experiencing peer victimization 153 . Furthermore, individuals with lower self-esteem post more negative and less positive content than individuals with higher self-esteem. Posted negative content receives less social reward and recognition from others than positive content, possibly creating a vicious cycle 186 . Negative experiences pertaining to social exclusion and status are also risk factors for socio-emotional disorders 180 .

The impact of social media experiences on self-esteem can be very heterogeneous, varying substantially across individuals. As a benefit, positive social feedback obtained via social media can increase users’ self-esteem 115 , an association also found among adolescents 187 . For instance, receiving likes on one’s profile or posted photographs can bolster self-esteem in the short term 144 , 188 . A study linking behavioural data and self-reports from Facebook users found that receiving quick responses on public posts increased a sense of social support and decreased loneliness 189 . Furthermore, a review of reviews consistently documented that users who report more social media use also perceive themselves to have more social resources and support online 52 , although this association has mostly been studied among young adults using social network sites such as Facebook. Whether such social feedback benefits extend to adolescents’ use of platforms centred on content consumption (such as TikTok or Instagram) is an open question.

Social inclusion and exclusion

Adolescents are more sensitive to the negative emotional impacts of being excluded than are adults 170 , 190 . It has been proposed that, as the importance of social affiliation increases during this period of life 134 , 191 , 192 , adolescents are more sensitive to a range of social stimuli, regardless of valence 193 . These include social feedback (such as compliments or likes) 95 , 194 , negative socio-emotional cues (such as negative facial expressions or social exclusion) 174 and social rejection 172 , 185 . By contrast, social inclusion (via friendships in adolescence) is protective against emotional disorders 195 and more social support is related to higher adolescent well-being 196 .

Experiencing ostracism and exclusion online decreases self-esteem and positive emotion 197 . This association has been found in vignette experiments where participants received no, only a few or a lot of likes 198 , or experiments that used mock-ups of social media sites where others received more likes than participants 153 . Being ostracized (not receiving attention or feedback) or rejected through social media features (receiving dislikes and no likes) is also associated with a reduced sense of belonging, meaningfulness, self-esteem and control 199 . Similar results were found when ostracism was experienced over messaging apps, such as not receiving a reply via WhatsApp 200 .

Evidence on whether social media also enables adolescents to experience positive social inclusion is mostly indirect and mixed. Some longitudinal surveys have found that prosocial feedback received on social media during major life events (such as university admissions) helps to buffer against stress 201 . Adult participants of a longitudinal study reported that social media offered more informational support than offline contexts, but offline contexts more often offered emotional or instrumental support 202 . Higher social network site use is, on average, associated with a perception of having more social resources and support in adults (for an overview of meta-analyses, see ref. 52 ). However, most of these studies have not investigated social support among adolescents, and it is unclear whether early findings (for example, on Facebook or Twitter) generalize to a social media landscape more strongly characterized by content consumption than social interaction (such as Instagram or TikTok).

Still, a review of social media use and offline interpersonal outcomes among adolescents documents both positive (sense of belonging and social capital) and negative (alienation from peers and perceived isolation) correlates 203 . Experience sampling research on emotional support among young adults has further shown that online social support is received and perceived as effective, and its perceived effectiveness is similar to in-person social support 204 . Social media use also has complex associations with friendship closeness among adolescents. For example, one experience sampling study found that greater use of WhatsApp or Instagram is associated with higher friendship closeness among adolescents; however, within-person examinations over time showed small negative associations 205 .

Neurobiological mechanisms

The long-term impact of environmental changes such as social media use on mental health might be amplified because adolescence is a period of considerable neurobiological development 95 (Fig.  3 ). During adolescence, overall cortical grey matter declines and white matter increases 206 , 207 . Development is particularly protracted in brain regions associated with social cognition and executive functions such as planning, decision-making and inhibiting prepotent responses. The changes in grey and white matter are thought to reflect axonal growth, myelination and synaptic reorganization, which are mechanisms of neuroplasticity influenced by the environment 208 . For example, research in rodents has demonstrated that adolescence is a sensitive period for social input, and that social isolation in adolescence has unique and more deleterious consequences for neural, behavioural and mental health development than social isolation before puberty or in adulthood 206 , 209 . There is evidence that brain regions involved in motivation and reward show greater activation to rewarding and motivational stimuli (such as appetitive stimuli and the presence of peers) in early and/or mid adolescence compared with other age groups 210 , 211 , 212 , 213 , 214 .

Little is known about the potential links between social media and neurodevelopment due to the paucity of research investigating these associations. Furthermore, causal chains (for example, social media increasing stress, which in turn influences the brain) have not yet been accurately delineated. However, it would be amiss not to recognize that brain development during adolescence forms part of the biological basis of mental health vulnerability and should therefore be considered. Indeed, the brain is proposed to be particularly plastic in adolescence and susceptible to environmental stimuli, both positive and negative 208 . Thus, even if adults and adolescents experienced the same affective consequences from social media use (such as increases in peer comparison or stress), these consequences might have a greater impact in adolescence.

A cross-sectional study (with some longitudinal elements) suggested that habitual checking of social media (for example, checking for rewards such as likes) might exacerbate reward sensitivity processes, leading to long-term hypersensitization of the reward system 215 . Specifically, frequently checking social media was associated with reduced activation in brain regions such as the dorsolateral prefrontal cortex and the amygdala in response to anticipated social feedback in young people. Brain activation during the same social feedback task was measured over subsequent years. Upon follow-up, anticipating feedback was associated with increased activation of the same brain regions among the individuals who checked social media frequently initially 215 . Although longitudinal brain imaging measurements enabled trajectories of brain development to be specified, the measures of social media use were only acquired once in the first wave of data collection. The study therefore cannot account for confounds such as personality traits, which might influence both social media checking behaviours and brain development. Other studies of digital screen use and brain development have found no impact on adolescent functional brain organization 216 .

Brain development and heightened neuroplasticity 208 render adolescence a particularly sensitive period with potentially long-term impacts into adulthood. It is possible that social media affordances that underpin increased checking and reward-seeking behaviours (such as quantifiability, variability of social rewards and permanent availability of peers) might have long-term consequences on reward processing when experienced during adolescence. However, this suggestion is still speculative and not backed up by evidence 217 .

Stress is another example of the potential amplifying effect of social media on adolescent mental health vulnerability due to neural development. Adolescents show higher stress reactivity because of maturational changes to, and increased reactivity in, the hypothalamic–pituitary–adrenal axis 218 , 219 . Compared with children and adults, adolescents experience an increase in self-consciousness and associated emotional states such as self-reported embarrassment and related physiological measures of arousal (such as skin conductance), and heightened neural response patterns compared with adults, when being evaluated or observed by peers 220 . Similarly, adolescents (age 13–17 years) show higher stress responses (higher levels of cortisol or blood pressure) compared with children (age 7–12 years) when they perform in front of others or experience social rejection 221 .

Such changes in adolescence might confer heightened risk for the onset of mental health conditions, especially socio-emotional disorders 6 . Both adolescent rodents and humans show prolonged hypothalamic–pituitary–adrenal activation after experiencing stress compared with conspecifics of different ages 218 , 219 . In animal models, stress during adolescence has been shown to result in increased anxiety levels in adulthood 222 and alterations in emotional and cognitive development 223 . Furthermore, human studies have linked stress in adolescence to a higher risk of mental health disorder onset 218 and reviews of cross-species work have illustrated a range of brain changes due to adolescent stress 224 , 225 .

There is still little conclusive neurobiological evidence about social media use and stress, and a lack of understanding about which affordances might be involved (although there has been a range of work studying digital stress; Box  1 ). Studies of changes in cortisol levels or hypothalamic–pituitary–adrenal functioning and their relation to social media use have been mixed and inconclusive 226 , 227 . These results could be due to the challenge of studying stress responses in adolescents, particularly as cortisol fluctuates across the day and one-point readings can be unreliable. However, the increased stress sensitivity during the adolescent developmental period might mean that social media use can have a long-term influence on mental health due to neurobiological mechanisms. These processes are therefore important to understand in future research.

Box 1 Digital stress

Digital stress is not a unified construct. Thematic content analyses have categorized digital stress into type I stressors (for example, mean attacks, cyberbullying or shaming) and type II stressors (for example, interpersonal stress due to pressure to stay available) 260 . Other reviews have noted its complexity, and categorized digital stress into availability stress (stress that results from having to be constantly available), approval anxiety (anxiety regarding others’ reaction to their own profile, posts or activities online), fear of missing out (stress about being absent from or not experiencing others’ rewarding experiences) and communication overload (stress due to the scale, intensity and frequency of online communication) 261 .

Digital stress has been systematically linked to negative mental health outcomes. Higher digital stress was longitudinally associated with higher depressive symptoms in a questionnaire study 262 . Higher social media stress was also longitudinally related to poorer sleep outcomes in girls (but not boys) 263 . Studies and reviews have linked cyberbullying victimization (a highly stressful experience) to decreased mental health outcomes such as depression, and psychosocial outcomes such as self-esteem 103 , 146 , 147 , 264 , 265 . A systematic review of both adolescents and adults found a medium association ( r  = 0.26–0.34) between different components of digital stress and psychological distress outcomes such as anxiety, depression or loneliness, which was not moderated by age or sex (except for connection overload) 266 . However, the causal structure giving rise to such results is still far from clear. For example, surveys have linked higher stress levels to more problematic social media use and fear of missing out 267 , 268 .

Thus, the impact of digital stress on mental health is probably complex and influenced by the type of digital stressor and various affordances. For example, visibility and availability increase fear of negative public evaluation 269 and high availability and a social norm of responding quickly to messages drive constant monitoring in adolescents due to a persistent fear of upsetting friends 270 .

A range of relevant evidence from qualitative and quantitative studies documents that adolescents often ruminate about online interactions and messages. For example, online salience (constantly thinking about communication, content or events happening online) was positively associated with stress on both between-person and within-person levels in a cross-sectional quota sample of adults and three diary studies of young adults 271 , 272 . Online salience has also been associated with lower well-being in a pre-registered study of momentary self-reports from young adults with logged online behaviours. However, this study also noted that positive thoughts were related to higher well-being 273 . Furthermore, although some studies found no associations between the amount of communication and digital stress 272 , a cross-sectional study found that younger users’ (age 14–34 years and 35–49 years) perception of social pressure to be constantly available was related to communication load (measured by questions about the amount of use, as well as the urge to check email and social media) and Internet multitasking, whereas this was not the case for older users aged 50–85 years 274 . By contrast, communication load and perceived stress were associated only among older users.

Summary and future directions

To help to understand the potential role of social media in the decline of adolescent mental health over the past decade, researchers should study the mechanisms linking social media, adolescent development and mental health. Specifically, social media environments might amplify the socio-cognitive processes that render adolescents more vulnerable to mental health conditions in the first place. We outline various mechanisms at three levels of adolescent development — behavioural, cognitive and neurobiological — that could be involved in the decline of adolescent mental health as a function of social media engagement. To do so, we delineate specific social media affordances, such as quantification of social feedback or anonymity, which can also have positive impacts on mental health.

Our Review sets out clear recommendations for future research on the intersection of social media and adolescent mental health. The foundation of this research lies in the existing literature investigating the underlying processes that heighten adolescents’ risk of developing socio-emotional disorders. Zooming in on the potential mechanistic targets impacted by social media uses and affordances will produce specific research questions to facilitate controlled and systematic scientific inquiry relevant for intervention and translation. This approach encourages researchers to pinpoint the mechanisms and levels of explanation they want to include and will enable them to identify what factors to additionally consider, such as participants’ age 60 , the specific mental health outcomes being measured, the types of social media being examined and the populations under study 52 , 228 . Targeted and effective research should prioritize the most promising areas of study and acknowledge that all research approaches have inherent limitations 229 . Researchers must embrace methodological diversity, which in turn will facilitate triangulation. Surveys, experience sampling designs in conjunction with digital trace data, as well as experimental or neuroimaging paradigms and computational modelling (such as reinforcement learning) can all be used to address research questions comprehensively 230 . Employing such a multi-method approach enables the convergence of evidence and strengthens the reliability of findings 231 .

Mental health and developmental research can also become more applicable to the study of social media by considering how studies might already be exploring features of the digital environment, such as its design features and perceived affordances. Many cognitive neuroscience studies that investigate social processes and mental health during adolescence necessarily design tasks that can be completed in controlled experimental or brain scanning environments. Consequently, they tend to focus on digitally mediated interactions. However, researchers conceptualize and generalize their results to face-to-face interactions. For example, it is common across the discipline to not explicitly describe the interactions under study as being about social processes in digital environments (such as studies that assess social feedback based on the number of ‘thumbs up’ or ‘thumbs down’ received in social media 232 ). Considering whether cognitive neuroscience studies include key affordances of mediated (or non-mediated) environments, and discussing these in published papers, will make studies searchable within the field of social media research, enabling researchers to broaden the impact of their work and systematically specify generalizations to offline environments 233 .

To bridge the gap between knowledge about mediated and non-mediated social environments, it is essential to directly compare the two 233 . It is often assumed that negative experiences online have a detrimental impact on mental health. However, it remains unclear whether this mechanism is present in both mediated and non-mediated spaces or whether it is specific to the mediated context. For instance, our Review highlights that the quantification of social feedback through likes is an important affordance of social media 160 . Feedback on social media platforms might therefore elicit a greater sense of certainty because it is quantified compared with the more subjective and open-to-interpretation feedback received face to face 151 . Conducting experiments in which participants receive feedback that is more or less quantified and uncertain, specifically designed to compare mediated and non-mediated environments, would provide valuable insights. Such research efforts could also establish connections with computational neuroscience studies demonstrating that people tend to learn faster from stimuli that are less uncertain 234 .

We have chosen not to make recommendations concerning interventions targeting social media use to improve adolescent mental health for several reasons. First, we did not fully consider the bidirectional interactions between environment and development 35 , 235 , or the factors modulating adolescents’ differential susceptibility to the effects of social media 45 , 58 . For example, mental health status also influences how social media is used 47 , 58 , 59 , 236 , 237 (Box  2 ). These bidirectional interactions could be addressed using network or complexity science approaches 238 . Second, we do not yet know how the potential mechanisms by which social media might increase mental health vulnerability compare in magnitude, importance, scale and ease and/or cost of intervention with other factors and mechanisms that are already well known to influence mental health, such as poverty or loneliness. Last, social media use will probably interact with these predictors in ways that have not been delineated and can also support mental health resilience (for example, through social support or online self-help programmes). These complexities should be considered in future research, which will need to pinpoint not just the existence of mechanisms but their relative importance, to identify policy and intervention priorities.

Our Review has used a broad definition of mental health. Focusing on specific diagnostic or transdiagnostic symptomatology might reveal different mechanisms of interest. Furthermore, our Review is limited to mechanisms related to behaviour and neurocognitive development, disregarding other levels of explanation (such as genetics and culture) 34 , and also studying predominately Western-centric samples 239 . Mechanisms do not operate solely in linear pathways but exist within networks of interacting risk and resilience factors, characterized by non-linear and complex dynamics across diverse timescales 9 . Mechanisms and predisposing factors can interact and combine, amplifying mental health vulnerability. Mental health can be considered a dynamic system in which gradual changes to external conditions can have substantial downstream consequences due to system properties such as feedback loops 240 , 241 , 242 . These consequences are especially prominent in times of change and pre-existing vulnerability, such as adolescence 10 .

Indeed, if social media is a contributing factor to the current decline in adolescent mental health, as is commonly assumed, then it is important to identify and investigate mechanisms that are specifically tailored to the adolescent age range and make the case for why they matter. Without a thorough examination of these mechanisms and policy analysis to indicate whether they should be a priority to address, there is insufficient evidence to support the hypothesis that social media is the primary — or even just an influential and important — driver of mental health declines. Researchers need to stop studying social media as monolithic and uniform, and instead study its features, affordances and outcomes by leveraging a range of methods including experiments, questionnaires, qualitative research and industry data. Ultimately, this comprehensive approach will enhance researchers’ ability to address the potential challenges that the digital era poses on adolescent mental health.

Box 2 Effects of mental health on social media use

Although a lot of scientific discussion has focused on the impact of social media use on mental health, cross-sectional studies cannot differentiate between whether social media use is influencing mental health or mental health is influencing social media use, or a third factor is influencing both 51 . It is likely that mental health status influences social media use creating reinforcing cycles of behaviour, something that has been considered in the communication sciences literature under the term ‘transactional media effects’ 58 , 236 , 237 . According to communication science models, media use and its consequences are components of reciprocal processes 275 .

There are similar models in mental health research. For example, people’s moods influence their judgements of events, which can lead to self-perpetuating cycles of negativity (or positivity); a mechanism called ‘mood congruency’ 276 . Behavioural studies have also shown that people experiencing poor mental health behave in ways that decrease their opportunity to experience environmental reward such as social activities, maintaining poor mental health 277 , 278 . Although for many people these behaviours are a form of coping (for example, by avoiding stressful circumstances), they often worsen symptoms of mental health conditions 279 .

Some longitudinal studies found that a decrease in adolescent well-being predicted an increase in social media use 1 year later 47 , 59 . However, other studies have found no relationships between well-being and social media use over long-term or daily time windows 45 , 46 . One reason behind the heterogeneity of the results could be that how mental health impacts social media use is highly individual 45 , 280 .

Knowledge on the impact of mental health on social media use is still in its infancy and studies struggle to reach coherent conclusions. However, findings from the mental health literature can be used to generate hypotheses about how aspects of mental health might impact social media use. For example, it has been repeatedly found that young people with anxiety or eating disorders engage in more social comparisons than individuals without these disorders 281 , 282 , and adolescents with depression report more unfavourable social comparisons on social media than adolescents without depression 283 . Similar results have been found for social feedback seeking (for example, reassurance), including in social media environments 159 . Specifically, depressive symptoms were more associated with social comparison and feedback seeking, and these associations were stronger in women and in adolescents who were less popular. Individuals from the general population with lower self-esteem post more negative and less positive content than individuals with higher self-esteem, which in turn is associated with receiving less positive feedback from others 185 . There are therefore a wide range of possible ways in which diverse aspects of mental health might influence specific facets of how social media is used — and, in turn, how it ends up impacting the user.

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Acknowledgements

A.O. and T.D. were funded by the Medical Research Council (MC_UU_00030/13). A.O. was funded by the Jacobs Foundation and a UKRI Future Leaders Fellowship (MR/X034925/1). S.-J.B. is funded by Wellcome (grant numbers WT107496/Z/15/Z and WT227882/Z/23/Z), the MRC, the Jacobs Foundation, the Wellspring Foundation and the University of Cambridge.

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Social media harms teens’ mental health, mounting evidence shows. what now.

Understanding what is going on in teens’ minds is necessary for targeted policy suggestions

Most teens use social media, often for hours on end. Some social scientists are confident that such use is harming their mental health. Now they want to pinpoint what explains the link.

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By Sujata Gupta

February 20, 2024 at 7:30 am

In January, Mark Zuckerberg, CEO of Facebook’s parent company Meta, appeared at a congressional hearing to answer questions about how social media potentially harms children. Zuckerberg opened by saying: “The existing body of scientific work has not shown a causal link between using social media and young people having worse mental health.”

But many social scientists would disagree with that statement. In recent years, studies have started to show a causal link between teen social media use and reduced well-being or mood disorders, chiefly depression and anxiety.

Ironically, one of the most cited studies into this link focused on Facebook.

Researchers delved into whether the platform’s introduction across college campuses in the mid 2000s increased symptoms associated with depression and anxiety. The answer was a clear yes , says MIT economist Alexey Makarin, a coauthor of the study, which appeared in the November 2022 American Economic Review . “There is still a lot to be explored,” Makarin says, but “[to say] there is no causal evidence that social media causes mental health issues, to that I definitely object.”

The concern, and the studies, come from statistics showing that social media use in teens ages 13 to 17 is now almost ubiquitous. Two-thirds of teens report using TikTok, and some 60 percent of teens report using Instagram or Snapchat, a 2022 survey found. (Only 30 percent said they used Facebook.) Another survey showed that girls, on average, allot roughly 3.4 hours per day to TikTok, Instagram and Facebook, compared with roughly 2.1 hours among boys. At the same time, more teens are showing signs of depression than ever, especially girls ( SN: 6/30/23 ).

As more studies show a strong link between these phenomena, some researchers are starting to shift their attention to possible mechanisms. Why does social media use seem to trigger mental health problems? Why are those effects unevenly distributed among different groups, such as girls or young adults? And can the positives of social media be teased out from the negatives to provide more targeted guidance to teens, their caregivers and policymakers?

“You can’t design good public policy if you don’t know why things are happening,” says Scott Cunningham, an economist at Baylor University in Waco, Texas.

Increasing rigor

Concerns over the effects of social media use in children have been circulating for years, resulting in a massive body of scientific literature. But those mostly correlational studies could not show if teen social media use was harming mental health or if teens with mental health problems were using more social media.

Moreover, the findings from such studies were often inconclusive, or the effects on mental health so small as to be inconsequential. In one study that received considerable media attention, psychologists Amy Orben and Andrew Przybylski combined data from three surveys to see if they could find a link between technology use, including social media, and reduced well-being. The duo gauged the well-being of over 355,000 teenagers by focusing on questions around depression, suicidal thinking and self-esteem.

Digital technology use was associated with a slight decrease in adolescent well-being , Orben, now of the University of Cambridge, and Przybylski, of the University of Oxford, reported in 2019 in Nature Human Behaviour . But the duo downplayed that finding, noting that researchers have observed similar drops in adolescent well-being associated with drinking milk, going to the movies or eating potatoes.

Holes have begun to appear in that narrative thanks to newer, more rigorous studies.

In one longitudinal study, researchers — including Orben and Przybylski — used survey data on social media use and well-being from over 17,400 teens and young adults to look at how individuals’ responses to a question gauging life satisfaction changed between 2011 and 2018. And they dug into how the responses varied by gender, age and time spent on social media.

Social media use was associated with a drop in well-being among teens during certain developmental periods, chiefly puberty and young adulthood, the team reported in 2022 in Nature Communications . That translated to lower well-being scores around ages 11 to 13 for girls and ages 14 to 15 for boys. Both groups also reported a drop in well-being around age 19. Moreover, among the older teens, the team found evidence for the Goldilocks Hypothesis: the idea that both too much and too little time spent on social media can harm mental health.

“There’s hardly any effect if you look over everybody. But if you look at specific age groups, at particularly what [Orben] calls ‘windows of sensitivity’ … you see these clear effects,” says L.J. Shrum, a consumer psychologist at HEC Paris who was not involved with this research. His review of studies related to teen social media use and mental health is forthcoming in the Journal of the Association for Consumer Research.

Cause and effect

That longitudinal study hints at causation, researchers say. But one of the clearest ways to pin down cause and effect is through natural or quasi-experiments. For these in-the-wild experiments, researchers must identify situations where the rollout of a societal “treatment” is staggered across space and time. They can then compare outcomes among members of the group who received the treatment to those still in the queue — the control group.

That was the approach Makarin and his team used in their study of Facebook. The researchers homed in on the staggered rollout of Facebook across 775 college campuses from 2004 to 2006. They combined that rollout data with student responses to the National College Health Assessment, a widely used survey of college students’ mental and physical health.

The team then sought to understand if those survey questions captured diagnosable mental health problems. Specifically, they had roughly 500 undergraduate students respond to questions both in the National College Health Assessment and in validated screening tools for depression and anxiety. They found that mental health scores on the assessment predicted scores on the screenings. That suggested that a drop in well-being on the college survey was a good proxy for a corresponding increase in diagnosable mental health disorders. 

Compared with campuses that had not yet gained access to Facebook, college campuses with Facebook experienced a 2 percentage point increase in the number of students who met the diagnostic criteria for anxiety or depression, the team found.

When it comes to showing a causal link between social media use in teens and worse mental health, “that study really is the crown jewel right now,” says Cunningham, who was not involved in that research.

A need for nuance

The social media landscape today is vastly different than the landscape of 20 years ago. Facebook is now optimized for maximum addiction, Shrum says, and other newer platforms, such as Snapchat, Instagram and TikTok, have since copied and built on those features. Paired with the ubiquity of social media in general, the negative effects on mental health may well be larger now.

Moreover, social media research tends to focus on young adults — an easier cohort to study than minors. That needs to change, Cunningham says. “Most of us are worried about our high school kids and younger.” 

And so, researchers must pivot accordingly. Crucially, simple comparisons of social media users and nonusers no longer make sense. As Orben and Przybylski’s 2022 work suggested, a teen not on social media might well feel worse than one who briefly logs on. 

Researchers must also dig into why, and under what circumstances, social media use can harm mental health, Cunningham says. Explanations for this link abound. For instance, social media is thought to crowd out other activities or increase people’s likelihood of comparing themselves unfavorably with others. But big data studies, with their reliance on existing surveys and statistical analyses, cannot address those deeper questions. “These kinds of papers, there’s nothing you can really ask … to find these plausible mechanisms,” Cunningham says.

One ongoing effort to understand social media use from this more nuanced vantage point is the SMART Schools project out of the University of Birmingham in England. Pedagogical expert Victoria Goodyear and her team are comparing mental and physical health outcomes among children who attend schools that have restricted cell phone use to those attending schools without such a policy. The researchers described the protocol of that study of 30 schools and over 1,000 students in the July BMJ Open.

Goodyear and colleagues are also combining that natural experiment with qualitative research. They met with 36 five-person focus groups each consisting of all students, all parents or all educators at six of those schools. The team hopes to learn how students use their phones during the day, how usage practices make students feel, and what the various parties think of restrictions on cell phone use during the school day.

Talking to teens and those in their orbit is the best way to get at the mechanisms by which social media influences well-being — for better or worse, Goodyear says. Moving beyond big data to this more personal approach, however, takes considerable time and effort. “Social media has increased in pace and momentum very, very quickly,” she says. “And research takes a long time to catch up with that process.”

Until that catch-up occurs, though, researchers cannot dole out much advice. “What guidance could we provide to young people, parents and schools to help maintain the positives of social media use?” Goodyear asks. “There’s not concrete evidence yet.”

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The Loneliness Curve

New research suggests people tend to be lonelier in young adulthood and late life. But experts say it doesn’t have to be that way.

By Christina Caron

When Surgeon General Vivek Murthy went on a nationwide college tour last fall, he started to hear the same kind of question time and again: How are we supposed to connect with one another when nobody talks anymore?

In an age when participation in community organizations , clubs and religious groups has declined, and more social interaction is happening online instead of in person, some young people are reporting levels of loneliness that, in past decades, were typically associated with older adults.

It’s one of the many reasons loneliness has become a problem at both the beginning and end of our life span. In a study published last Tuesday in the journal Psychological Science, researchers found that loneliness follows a U-shaped curve: Starting from young adulthood, self-reported loneliness tends to decline as people approach midlife only to rise again after the age of 60, becoming especially pronounced by around age 80.

While anyone can experience loneliness, including middle-aged adults , people in midlife may feel more socially connected than other age groups because they are often interacting with co-workers, a spouse, children and others in their community — and these relationships may feel stable and satisfying, said Eileen K. Graham, an associate professor of medical social sciences at the Northwestern University Feinberg School of Medicine and the lead author of the study.

As people get older, those opportunities can “start to fall away,” she said. In the study, which looked at data waves spanning several decades, starting as early as the 1980s and ending as late as 2018, participants at either end of the age spectrum were more likely to agree with statements such as: “I miss having people around me” or “My social relationships are superficial.”

“We have social muscles just like we have physical muscles,” Dr. Murthy said. “And those social muscles weaken when we don’t use them.”

When loneliness goes unchecked, it can be dangerous to our physical and mental health, and has been linked to problems like heart disease, dementia and suicidal ideation.

Dr. Graham and other experts on social connection said there were small steps we could take at any age to cultivate a sense of belonging and social connection.

Do a relationship audit.

“Don’t wait until old age to discover that you lack a good-quality social network,” said Louise Hawkley, a research scientist who studies loneliness at NORC, a social research organization at the University of Chicago . “The longer you wait, the harder it gets to form new connections.”

Studies suggest that most people benefit from having a minimum of four to six close relationships, said Julianne Holt-Lunstad, a professor of psychology and neuroscience and the director of the Social Connection and Health Lab at Brigham Young University.

But it’s not just the quantity that matters, she added, it’s also the variety and the quality.

“Different relationships can fulfill different kinds of needs,” Dr. Holt-Lunstad said. “Just like you need a variety of foods to get a variety of nutrients, you need a variety of types of people in your life.”

Ask yourself: Are you able to rely on and support the people in your life? And are your relationships mostly positive rather than negative?

If so, it’s a sign that those relationships are beneficial to your mental and physical well-being, she said.

Join a group.

Research has shown that poor health, living alone and having fewer close family and friends account for the increase in loneliness after about age 75.

But isolation isn’t the only thing that contributes to loneliness — in people both young and old, loneliness stems from a disconnect between what you want or expect from your relationships and what those relationships are providing.

If your network is shrinking — or if you feel unsatisfied with your relationships — seek new connections by joining a community group, participating in a social sports league or volunteering , which can provide a sense of meaning and purpose, Dr. Hawkley said.

And if one type of volunteering is not satisfying, do not give up, she added. Instead try another type.

Participating in organizations that interest you can offer a sense of belonging and is one way to accelerate the process of connecting in person with like-minded people.

Cut back on social media.

Jean Twenge, a social psychologist and the author of “Generations,” found in her research that heavy social media use is linked to poor mental health — especially among girls — and that smartphone access and internet use “ increased in lock step with teenage loneliness .”

Instead of defaulting to an online conversation or merely a reaction to someone’s post, you can suggest bonding over a meal — no phones allowed.

And if a text or social media interaction is getting long or involved, move to real-time conversation by texting, “Can I give you a quick call?” Dr. Twenge said.

Finally, Dr. Holt-Lunstad suggested asking a friend or family member to go on a walk instead of corresponding online. Not only is taking a stroll free, it also has the added benefit of providing fresh air and exercise.

Take the initiative.

“Oftentimes when people feel lonely, they may be waiting for someone else to reach out to them,” Dr. Holt-Lunstad said. “It can feel really hard to ask for help or even just to initiate a social interaction. You feel very vulnerable. What if they say no?”

Some people might feel more comfortable contacting others with an offer to help, she added, because it helps you focus “outward instead of inward.”

Small acts of kindness will not only maintain but also solidify your relationships, the experts said.

For example, if you like to cook, offer to drop off food for a friend or family member, Dr. Twenge said.

“You’ll not only strengthen a social connection but get the mood boost that comes from helping,” she added.

Christina Caron is a Times reporter covering mental health. More about Christina Caron

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Persistent depressive disorder is underdiagnosed, and many who suffer from it have never heard of it. Here is what to know .

If you notice drastic shifts in your mood during certain times of the year, you could have seasonal affective disorder. Here are answers to your top questions about the condition .

How much anxiety is too much? Here is how to establish whether you should see a professional about it .

Science journalism in South Africa: social media is helping connect with new readers

Associate professor, University of the Western Cape

Disclosure statement

Sisanda Nkoala receives funding from the National Research Foundation. The training to undertake this study was funded, in part, by the Institute of Pan African Thought and Conversation at the University of Johannesburg, where she was a Junior Fellow in 2022 - 2023.

University of Western Cape provides support as a hosting partner of The Conversation AFRICA.

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Science journalism is about taking complex academic research and turning it into easy-to-read content that is still accurate. Aside from informing readers through evidence-based journalism, it aims to make the wider public curious about scientific research.

This article is an example of science journalism. As a media professor I conducted a study on how social media sites like X (formerly known as Twitter) can help science communication reach new audiences in South Africa. This article is a version of that study written in a way that people other than just academics can understand.

In my study I analysed the X feeds of three South African science journalism platforms: Bhekisisa , which focuses on health news; SciBraai , which specialises in local science news; and The Conversation Africa , which carries articles written by academics from a range of disciplines that are edited by journalists.

My study’s analysis found that X offers unique advantages for communicating science. It allows interaction. It makes it possible to simplify complex information into easy-to-understand formats. And it supports multiple languages and media. This creates a more dynamic and inclusive way to share scientific knowledge.

I concluded from my findings that social media can significantly enhance the reach and understanding of African science journalism.

The challenges

Media scholars have highlighted a crucial problem in the global south. The dominance of English in science communication leads to a neglect of vital issues because it excludes the majority of the population. In South Africa, for example, only 9% of people speak English as a first language. Studies illustrate how this leaves audiences feeling there’s a lack of relevant science content available.

Added to this is the enduring legacy of colonialism that reinforces the idea of science as an exclusive activity for a select few. These historical undercurrents shape science communicators’ challenges.

Read more: How to read and learn from scientific literature, even if you're not an expert

Effective science journalism is made more complex by the need to frame scientific developments in ways that are culturally relevant to African readers. Added to this is a historical reliance on foreign sources in science news content that’s published in the global south. This undermines the development of work tailored to African contexts.

There’s no shortage of obstacles. Limited funding for science journalism and a lack of access to digital technologies in many parts of Africa pose the final hurdles to spreading reliable scientific information.

This is where social media comes in.

My study shows that in South Africa X offers a glimmer of transformation for science communication.

I chose to investigate its advantages in a real-world setting by analysing three science journalism outlets. The three platforms I chose go beyond traditional science journalism by choosing to focus on research that is relevant to African audiences. This fills a critical gap, as science news most often has a western bias.

The Conversation Africa is part of a global network of not-for-profit outlets where academics collaborate with journalists and editors to make research accessible. Content is available for any other platform to freely republish, extending its reach, as is the case with SciBraai.

Bhekisisa is an award-winning health journalism centre focused on solutions-based reporting. It combines journalistic expertise with updates on medical research and it uses multimedia formats.

SciBraai specialises in local science news and scientists. It uses a multilingual approach (the English, isiZulu and Afrikaans languages) to enhance its reach

They also focus strongly on social media. And their orientation towards using clear language and visuals, as well as incorporating a multilingual approach, is an effective way of making knowledge accessible.

For example, Bhekisisa creates graphics and multimedia formats to explain complex medical developments. These visuals act as entry points into complex topics for a diverse audience.

Read more: Five golden rules for effective science communication – perspectives from a documentary maker

For its part, SciBraai is challenging the dominance of English in science communication. It incorporates isiZulu, the most widely spoken language in South Africa, into its feed on X.

The outlets align with research that defines good science journalism. This is journalism that tries to keep the public informed about science, helps explain the implications of scientific research and supports related decision-making in a society.

Some argue that science journalism in the global south suffers from a lack of resources and a neglect of local scientific inquiry. But the practices I observed in these three outlets offer a more nuanced perspective.

It’s important to note that I wasn’t measuring how popular or successful the X accounts of these outlets are. I was interested in analysing how their use of social media aligns with the fundamental goals of science communication. To do this I downloaded all tweets from the three feeds and obtained all tweets that mention the accounts. The posts were analysed using content analysis , a research method that identifies trends and patterns in communications.

The pitfalls

My study highlights that X is not merely a broadcast tool for science journalism but can be a platform to cultivate dialogue. The ability of science communicators to respond to questions, address misconceptions and incorporate feedback from the public strengthens the trust between the scientific community and broader society. This empowers audiences to become active participants rather than passive recipients of information.

While these findings are encouraging, challenges remain in how the potential for direct audience engagement is only sometimes fully realised.

And social media comes with its own pitfalls. The spread of misinformation threatens the effectiveness of science communication efforts on X. Social media cannot replace robust investment in science journalism training, infrastructure, and widening access to technology throughout Africa.

X presents a tool for reaching audiences, not a solution. When wielded strategically, it can empower science communicators to diversify and deepen their impact.

• Science communication
• Science journalism
• African social media
• South African media
• The Conversation Africa
• Social media abuse
• African journalism
• South African journalism

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• Mohammad Ali Zare Chahooki 1 ,
• Mahdi Yazdian-Dehkordi 1 &
• Fereshte Rahmanian 2  

BMC Research Notes volume  17 , Article number:  135 ( 2024 ) Cite this article

Metrics details

Due to the limitations of Twitter, the expansion of Telegram channels, and the Telegram API’s easy use, Telegram comments have become prevalent. Telegram is one of the most popular social networks, unlike Twitter, which has no restrictions on sending messages, and experts can share their opinions and media. Some of these channels, managed by influencers of large companies, are very influential in the behavior of the market on various stocks, including cryptocurrencies. In this research, the opinion collection of 10 famous Telegram channels regarding the analysis of cryptocurrencies has been extracted. The sentiments of these opinions have been analyzed using the HDRB model. HDRB is a hybrid model of RoBERTa deep neural network, BiGRU, and attention layer used for sentiment analysis (SA). Analyzing the sentiments of these opinions is very important for understanding the future behavior of the market and managing the stock portfolio. The opinions of this dataset, published by experts in the field of cryptocurrencies, are precious, unlike the opinions that are extracted only by using the hashtag of the names of cryptocurrencies. On the other hand, the dataset related to cryptocurrencies, which has the opinions of experts and the polarity of their feelings, is very rare.

Data description

The dataset of this research is the sentiments of more than ten popular Telegram channels regarding a wide range of cryptocurrencies. These comments were collected through the Telegram API from December 2023 to March 2024. This data set contains an Excel file containing the text of the comments, the date of comment creation, the number of views, the compound score, the sentiment score, and the type of sentiment polarity. These opinions cover influencer analysis on a wide range of cryptocurrencies. Also, two Word files, one containing the description of the dataset columns and the other Python code for extracting comments from Telegram channels, are included in this dataset.

Peer Review reports

Behavioral economics has proven that analyzing market behavior effectively predicts stock price trends [ 1 ]. It has also been shown in sate-of-arts [ 2 , 3 , 4 ] that sentiment analysis of comments on social networks such as X (former Twitter), Telegram, Reddit, and Facebook can effectively help predict the price trend of cryptocurrencies. In this article, Telegram comments of more than ten popular cryptocurrency-related channels have been extracted using the Telegram API from December 2023 to March 2024. Unlike the previous dataset [ 5 , 6 , 7 ] that are extracted only through the hashtag of the names of cryptocurrencies, this dataset contains the analysis of experts in cryptocurrencies, which is very effective on investors’ decisions in buying, selling, or holding cryptocurrencies. Unlike most existing datasets, this dataset covers a wide range of ciphers. Also, in addition to the main text of Telegram comments, this data set includes the number of views of each comment, the date of publication, and the polarity and the polarity score of the comments. After the extraction and preprocessing of Telegram comments, the polarity of these comments is determined by the HRDB model [ 8 ]. This model uses the RoBERTa pre-trained neural network as the backbone for transfer learning. Then, the extracted knowledge is injected into the deep neural network of BiGRU by combining the attention layer to determine the polarity of emotions. The main goal of this research is to provide a dataset of Telegram comments for the sentiment analysis of passwords, which has many applications in the training of neural networks for research in the field of passwords. This dataset helps researchers analyze the opinions of Telegram channels on a wide range of cryptocurrencies. The introduced data package includes an Excel table containing the Telegram monitoring set and two Word files. The Word files contain the descriptions of the columns of the main Table and Python code to extract comments from Telegram channels.

This data package has three files. An Excel file contains the opinions of over ten popular Telegram channels about cryptocurrencies. The monitoring of these Telegram channels covers a wide range of cryptocurrencies from December 2023 to March 2024. It was collected through the Telegram API, and the code for extracting these comments is available in the Word package file. After extracting the comments, the operations were performed on them, including equalization, removing stop words, and lemmatization. Then, these data are injected into the HDRB model, described in detail in the research of Kia et al. [ 8 ], along with its implementation method. HDRB is a hybrid model based on transfer deep learning that uses the RoBERTa as a backbone and feature extractor and BiGRU deep neural network and attention layer to obtain sentiment polarity and text aspects. This dataset package and Python codes for pre-processing and extracting Telegram comments are listed in Table ( 1 ).

The information of Dataset 1 is (1) text, (2) date, (3) views, (4) scores, (5) compound, and (6) sentiment_type. In the mentioned features, “text” is the preprocessed Telegram comment, “date” column shows the time and date of publication of the comment, “views” shows the number of people’s views of a comment, " scores” shows the percentage of positive, negative, and neutral polarities. These percentages were obtained with the HDRB model [ 8 ], “compound” shows the sum of all polarities in a normalized form between − 1 (most extreme negative) and + 1 (most extreme positive), and " sentiment_type” It shows the type of tweet polarity (positive, negative, or neutral). Researchers can easily change the number of polarities by using compound values—for example, strongly positive, positive, neutral, negative, and strongly negative.

Limitations

There are no limitations in the datasets, and the Telegram channels used in the datasets to extract Telegram’s comments are public.

Data availability

You can access the data package, including the dataset and other required files, for free through the link below. Link: https://data.mendeley.com/datasets/3733zt5bs6/1 .

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Jahanbin K, Chahooki MAZ, Rahmanian F, Yazdian M, Dehkordi. Comments on Telegram channels related to cryptocurrencies along with sentiments. data Mendeley. 2024;1. https://doi.org/10.17632/3733zt5bs6.1 .

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Acknowledgements

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Department of Computer Engineering, Yazd University, Yazd, Iran

Kia Jahanbin, Mohammad Ali Zare Chahooki & Mahdi Yazdian-Dehkordi

Department of Information Technology, Islamic Azad University Branch of Kerman, Kerman, Iran

Fereshte Rahmanian

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Contributions

Kia Jahanbin, Mohammad Ali Zare Chahooki, Mehdi Yazdian Dehkordi, and Fereshte Rahmanian contributed to conceptualizing and implementing sentiment analysis models and drafted the manuscript. Kia Jahanbin and Fereshte Rahmanian contributed to data collection and storage.

Corresponding author

Correspondence to Mohammad Ali Zare Chahooki .

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The Telegram channels used in the datasets to extract Telegram’s comments are public.

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Jahanbin, K., Chahooki, M.A.Z., Yazdian-Dehkordi, M. et al. Database comments on Telegram channels related to cryptocurrencies with sentiments. BMC Res Notes 17 , 135 (2024). https://doi.org/10.1186/s13104-024-06778-9

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DOI : https://doi.org/10.1186/s13104-024-06778-9

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Teens and Video Games Today

85% of u.s. teens say they play video games, and about four-in-ten do so daily. teens see both positive and negative sides of video games – from problem-solving and making friends to harassment and sleep loss, table of contents.

• Who plays video games?
• How often do teens play video games?
• What devices do teens play video games on?
• Social media use among gamers
• Teen views on how much they play video games and efforts to cut back
• Are teens social with others through video games?
• Do teens think video games positively or negatively impact their lives?
• Why do teens play video games?
• Bullying and violence in video games
• Appendix A: Detailed charts
• Acknowledgments
• Methodology

Pew Research Center conducted this analysis to better understand teens’ use of and experiences with video games.

The Center conducted an online survey of 1,453 U.S. teens from Sept. 26 to Oct. 23, 2023, through Ipsos. Ipsos recruited the teens via their parents, who were part of its KnowledgePanel . The KnowledgePanel is a probability-based web panel recruited primarily through national, random sampling of residential addresses. The survey was weighted to be representative of U.S. teens ages 13 to 17 who live with their parents by age, gender, race and ethnicity, household income, and other categories.

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

Here are the questions used for this analysis , along with responses, and  its methodology .

There are long-standing debates about the impact of video games on youth. Some credit them for helping young people form friendships and teaching them about teamwork and problem-solving . Others say video games expose teenagers to violent content, negatively impact their sleep and can even lead to addiction.

With this in mind, Pew Research Center surveyed 1,423 U.S. teens ages 13 to 17 about their own video game habits – from how often they play to the friends they’ve made and whether it gets in the way of them doing well in school or getting a good night’s sleep. 1

Key findings from the survey

• Video games as a part of daily teen life: 85% of U.S. teens report playing video games, and 41% say they play them at least once a day. Four-in-ten identify as a gamer.
• Gaming as a social experience: 72% of teens who play video games say that a reason why they play them is to spend time with others. And some have even made a friend online from playing them – 47% of teen video game players say they’ve done this.
• Helpful with problem-solving, less so for sleep: Over half of teens who play video games say it has helped their problem-solving skills, but 41% also say it has hurt their sleep.
• Bullying is a problem: 80% of all teens think harassment over video games is a problem for people their age. And 41% of those who play them say they’ve been called an offensive name when playing.
• Boys’ and girls’ experiences differ: Most teen boys and girls play video games, but larger shares of boys identify as gamers (62% vs. 17%) and play every day (61% vs. 22%). Boys who play them are also more likely to experience positive things from it, like making friends, and more troubling things like harassment.

Jump to read about: Who plays video games | Socializing over video games | Views about video games’ impact | Harassment and violence in video games      

Playing video games is widespread among teens. The vast majority of U.S. teens (85%) say they play them. Just 15% say they never do, according to the survey conducted Sept. 26-Oct. 23, 2023.

In addition to asking whether teens play video games, we also wanted to learn whether they consider themselves gamers. Overall, four-in-ten U.S. teens think of themselves as gamers. Just under half of teens (45%) play video games but do not think of themselves as gamers.

Nearly all boys (97%) say they play video games, compared with about three-quarters of teen girls. There is a substantial gap by gender in whether teens identify as gamers: 62% of teen boys do, compared with 17% of girls. 2

By gender and age

Younger teen girls are more likely than older girls to say they play video games: 81% of girls ages 13 to 14 compared with 67% of those ages 15 to 17. But among boys, nearly all play video games regardless of age. 

Similar shares of teens play video games across different racial and ethnic groups and among those who live in households with different annual incomes. Go to Appendix A for more detail on which teens play video games and which teens identify as gamers.

We also asked teens how often they play video games. About four-in-ten U.S. teens say they play video games daily, including 23% who do so several times a day.

Another 22% say they play several times a week, while 21% play them about once a week or less.

Teen boys are far more likely than girls to say they play video games daily (61% vs. 22%). They are also much more likely to say they play them several times a day (36% vs. 11%).

By whether someone identifies as a gamer

About seven-in-ten teens who identify as gamers (71%) say they play video games daily. This drops to 30% among those who play them but aren’t gamers.

By household income

Roughly half of teens living in households with an annual income of less than $30,000 (53%) say they play video games at least daily. This is higher than those in households with an annual income of $30,000 to $74,999 (42%) and $75,000 or more (39%).

Go to Appendix A to see more details about who plays video games and identifies as a gamer by gender, age, race and ethnicity, and household income.

Most teens play video games on a gaming console or a smartphone. When asked about five devices, most teens report playing video games on a gaming console (73%), such as PlayStation, Switch or Xbox. And 70% do so on a smartphone. Fewer – though still sizable shares – play them on each of the following:

• 49% say they play them on a desktop or laptop computer
• 33% do so on a tablet  
• 24% play them on a virtual reality (VR) headset such as Oculus, Meta Quest or PlayStation VR

Many teens play video games on multiple devices. About a quarter of teens (27%) do so on at least four of the five devices asked about, and about half (49%) play on two or three of them. Just 8% play video games on one device.

Teen boys are more likely than girls to play video games on four of the five devices asked about – all expect tablets. For instance, roughly nine-in-ten teen boys say they ever play video games on a gaming console, compared with 57% of girls. Equal shares of teen boys and girls play them on tablets.  

Teens who consider themselves gamers are more likely than those who play video games but aren’t gamers to play on a gaming console (95% vs. 78%), desktop or laptop computer (72% vs. 45%) or a virtual reality (VR) headset (39% vs. 19%). Similar shares of both groups play them on smartphones and tablets.

One way that teens engage with others about video games is through online platforms. And our survey findings show that teen gamers stand out for their use of two online platforms that are known for their gaming communities – Discord and Twitch :

• 44% of teen gamers say they use Discord, far higher than video game players who don’t identify as gamers or those who use the platform but do not play video games at all. About three-in-ten teens overall (28%) use Discord.
• 30% of teens gamers say they use Twitch. About one-in-ten other teens or fewer say the same; 17% of teens overall use the platform.

Previous Center research shows that U.S. teens use online platforms at high rates .

Teens largely say they spend the right amount of time playing video games. When asked about how much time they spend playing them, the largest share of teens (58%) say they spend the right amount of time. Far fewer feel they spend too much (14%) or too little (13%) time playing them.

Teen boys are more likely than girls to say they spend too much time playing video games (22% vs. 6%).

By race and ethnicity

Black (17%) and Hispanic (18%) teens are about twice as likely than White teens (8%) to say they spend too little time playing video games. 3

A quarter of teens who consider themselves gamers say they spend too much time playing video games, compared with 9% of those who play video games but don’t identify as gamers. Teen gamers are also less likely to think they spend too little time playing them (19% vs. 10%).

Fewer than half of teens have reduced how much they play video games. About four-in-ten (38%) say they have ever chosen to cut back on the amount of time they spend playing them. A majority (61%) report that they have not cut back at all.

This share is on par with findings about whether teenagers have cut back with their screen time – on social media or their smartphone.

Although boys are more likely to say they play video games too much, boys and girls are on par for whether they have ever cut back. About four-in-ten teen boys (39%) and girls (38%) say that they have ever cut back.

And gamers are as likely to say they have cut back as those who play video games but don’t identify as gamers (39% and 41%).

A main goal of our survey was to ask teens about their own experiences playing video games. For this section of the report, we focus on teens who say they play video games.

Socializing with others is a key part of the video game experience. Most teens who play video games do so with others, and some have developed friendships through them.

About nine-in-ten teen video game players (89%) say they play them with other people, in person or online. Far fewer (11%) play them only on their own.

Additionally, about half (47%) report that they have ever made a friend online because of a video game they both play. This equals 40% of all U.S. teens who have made a friend online because of a video game.

These experiences vary by:  

• Gender: Most teen boy and girl video game players play them with others, though it’s more common among boys (94% vs. 82%). Boys who play video games are much more likely to say they have made a friend online because of a video game (56% vs. 35%).
• Race and ethnicity: Black (55%) and Hispanic (53%) teen video game players are more likely than White teen video game players (43%) to say they have made a friend online because of them.
• Whether someone identifies as a gamer: Nearly all teen gamers report playing video games with others (98%). Fewer – though still most – of those who play video games but aren’t gamers (81%) also play them with others. And about seven-in-ten (68%) say they have made a friend online because of a video game, compared with 29% of those who play them but don’t identify as gamers.

Teens who play video games are particularly likely to say video games help their problem-solving skills. More than half of teens who play video games (56%) say this.

Additionally, more think that video games help, rather than hurt, three other parts of their lives that the survey asked about. Among teens who play video games:

• Roughly half (47%) say it has helped their friendships
• 41% say it has helped how they work with others
• 32% say it has helped their mental health

No more than 7% say playing video games has hurt any of these.

More teens who play video games say it hurts, rather than helps, their sleep. Among these teens, 41% say it has hurt how much sleep they get, while just 5% say it helps. And small shares say playing video games has impacted how well they do in school in either a positive or a negative way.

Still, many teens who play video games think playing them doesn’t have much an impact in any of these areas. For instance, at least six-in-ten teens who play video games say it has neither a positive nor a negative impact on their mental health (60%) or their school performance (72%). Fewer (41%) say this of their problem-solving skills.

Teen boys who play video games are more likely than girls to think playing them has helped their problem-solving skills, friendships and ability to work with others. For instance, 55% of teen boys who play video games say this has helped their friendships, compared with 35% of teen girls.

As for ways that it may hurt their lives, boys who play them are more likely than girls to say that it has hurt the amount of sleep they get (45% vs. 37%) and how well they do in school (21% vs. 11%). 

Teens who consider themselves gamers are more likely than those who aren’t gamers but play video games to say video games have helped their friendships (60% vs. 35%), ability to work with others (52% vs. 32%), problem-solving skills (66% vs. 47%) and mental health (41% vs. 24%).

Gamers, though, are somewhat more likely to say playing them hurt their sleep (48% vs. 36%) and how well they do in school (20% vs. 14%).

By whether teens play too much, too little or the right amount

Teens who report playing video games too much stand out for thinking video games have hurt their sleep and school performance. Two-thirds of these teens say it has hurt the amount of sleep they get, and 39% say it hurt their schoolwork. Far fewer of those who say they play the right amount (38%) or too little (32%) say it has hurt their sleep, or say it hurt their schoolwork (12% and 16%).

Teens who play video games say they largely do so to be entertained. And many also play them to be social with and interact with others. Teens who play video games were asked about four reasons why they play video games. Among those who play video games:

• Nearly all say fun or entertainment is a major or minor reason why they play video games – with a large majority (87%) saying it’s a major reason.
• Roughly three-quarters say spending time with others is a reason, and two-thirds say this of competing with others. Roughly three-in-ten say each is a major reason.
• Fewer – 50% – see learning something as a reason, with just 13% saying it’s a major reason.

While entertainment is by far the most common reason given by teens who play video games, differences emerge across groups in why they play video games.

Teens who identify as gamers are particularly likely to say each is major reason, especially when it comes to competing against others. About four-in-ten gamers (43%) say this is a major reason, compared with 13% of those who play video games but aren’t gamers.

Teen boys who play video games are more likely than girls to say competing (36% vs. 15%), spending time with others (36% vs. 27%) and entertainment (90% vs. 83%) are major reasons they play video games.

Black and Hispanic teens who play video games are more likely than White teens to say that learning new things and competing against others are major reasons they play them. For instance, 29% of Black teen video game players say learning something new is a major reason, higher than 17% of Hispanic teen video game players. Both are higher than the 7% of White teen video game players who say the same.

Teens who play video games and live in lower-income households are especially likely to say competing against others and learning new things are major reasons. For instance, four-in-ten teen video game players who live in households with an annual income of less than $30,000 say competing against others is a major reason they play. This is higher than among those in households with annual incomes of $30,000 to $74,999 (29%) and $75,000 or more (23%).

Cyberbullying can happen in many online environments, but many teens encounter this in the video game world.

Our survey finds that name-calling is a relatively common feature of video game life – especially for boys. Roughly four-in-ten teen video game players (43%) say they have been harassed or bullied while playing a video game in one of three ways: 

• 41% have been called an offensive name
• 12% have been physically threatened
• 8% have been sent unwanted sexually explicit things

Teen boys are particularly likely to say they have been called an offensive name. About half of teen boys who play video games (48%) say this has happened while playing them, compared with about a third of girls (32%). And they are somewhat more likely than girls to have been physically threatened (15% vs. 9%).

Teen gamers are more likely than those who play video games but aren’t gamers to say they been called and offensive name (53% vs. 30%), been physically threatened (17% vs. 8%) and sent unwanted sexually explicit things (10% vs. 6%).

Teens – regardless of whether they’ve had these experiences – think bullying is a problem in gaming. Eight-in-ten U.S. teens say that when it comes to video games, harassment and bullying is a problem for people their age. This includes 29% who say it is a major problem.

It’s common for teens to think harassment while playing video games is a problem, but girls are somewhat more likely than boys to say it’s a major problem (33% vs. 25%).

There have also been decades-long debates about how violent video games can influence youth behavior , if at all – such as by encouraging or desensitizing them to violence. We wanted to get a sense of how commonly violence shows up in the video games teens are playing.

Just over half of teens who play video games (56%) say at least some of the games they play contain violence. This includes 16% who say it’s in all or most of the games they play.

Teen boys who play video games are far more likely than girls to say that at least some of the games they play contain violence (69% vs. 37%).

About three-quarters of teen gamers (73%) say that at least some of the games they play contain violence, compared with 40% among video game players who aren’t gamers.   

• Throughout this report, “teens” refers to those ages 13 to 17. ↩
• Previous Center research of U.S. adults shows that men are more likely than women to identify as gamers – especially the youngest adults. ↩
• There were not enough Asian American respondents in the sample to be broken out into a separate analysis. As always, their responses are incorporated into the general population figures throughout the report. ↩

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