Literature Review – Social Media Use and Adolescent Mental Health

Introduction

Public and policy concern about the mental health of Australian adolescents has intensified over the past decade, and much of that concern has settled on social media. The Australian Institute of Health and Welfare (AIHW, 2024) reports that psychological distress among people aged 15 to 24 has risen since the mid-2010s, and the Australian Bureau of Statistics (ABS, 2023) National Study of Mental Health and Wellbeing found that close to two in five people aged 16 to 24 had experienced a mental disorder in the previous twelve months. Against this backdrop, the Australian Government legislated a minimum age of 16 for social media accounts through the Online Safety Amendment (Social Media Minimum Age) Act 2024, a measure the eSafety Commissioner has been tasked with administering. Policy has moved quickly, and it is worth asking how firmly the underlying evidence supports the assumption that social media use harms adolescent wellbeing.

This review synthesises the international and Australian literature on the relationship between social media use and adolescent mental health. It is organised around five themes: the shift from screen time to quality of use; the mechanisms proposed to link use with distress; the directionality of associations and the strength of longitudinal evidence; vulnerable subgroups; and protective factors, including digital literacy. It closes by identifying gaps in the causal and measurement evidence and by considering the implications for Australian policy and youth services. The intention is to characterise the state of knowledge fairly rather than to amplify alarm, since the wellbeing of young people is poorly served by either moral panic or complacency.

Search strategy

Peer-reviewed literature was identified through PsycINFO, PubMed and Scopus using combinations of the terms “social media”, “social networking”, “adolescent”, “youth”, “depression”, “anxiety” and “wellbeing”. The search prioritised systematic reviews, meta-analyses and longitudinal cohort studies published between 2015 and 2025, since these designs speak most directly to questions of magnitude and causal order. Australian grey literature was drawn from the AIHW, the eSafety Commissioner and the Royal Children’s Hospital National Child Health Poll to ground the synthesis in local data. Studies of adults, and those examining screen media unrelated to social platforms, were excluded, and the reference lists of key reviews were hand-searched for additional sources.

From screen time to quality of use

Early research framed the question quantitatively, asking whether more time on screens predicted poorer wellbeing. Twenge and Campbell (2018), analysing large United States survey datasets, reported that adolescents who spent more time on screens tended to report lower psychological wellbeing, with the pattern most visible beyond a few hours of daily use. Such findings fuelled a dose-response narrative that translated readily into round-number guidance for parents, schools and markers of youth policy.

This narrative has since been substantially qualified. Orben and Przybylski (2019), applying specification curve analysis across three large datasets, found that digital technology use explained roughly 0.4 per cent of the variance in adolescent wellbeing, an association they characterised as trivially small and comparable in size to associations with wearing glasses or eating potatoes. The lesson was methodological as much as substantive: the way researchers select variables and covariates can generate thousands of defensible but divergent estimates, and selectively reported specifications had exaggerated the apparent harm.

Attention has consequently shifted from how much adolescents use social media to how and why they use it. Przybylski and Weinstein (2017) advanced a “Goldilocks hypothesis”, finding that moderate use was not harmful and that only high levels showed a modest negative association, implying that connected technologies are woven into ordinary adolescent life rather than being uniformly toxic. The active-passive distinction is central here: passively scrolling curated content appears more closely linked to negative affect than actively messaging friends or sustaining relationships. Australian data echo this nuance. The Royal Children’s Hospital (RCH) National Child Health Poll (2021) found that parents reported both benefits, such as connection and learning, and harms, such as sleep disruption, from their children’s device use, suggesting that quantity alone is a poor proxy for effect. Quality of use, not screen time in the abstract, is where the more recent literature locates the action.

Mechanisms linking use to mental health

If social media influences mental health, it must do so through identifiable pathways. Three mechanisms recur across the literature, and distinguishing them matters because each implies a different response.

Social comparison and feedback-seeking

Social platforms are engines of comparison. Nesi and Prinstein (2015) found that technology-based social comparison and feedback-seeking predicted depressive symptoms over time, particularly among adolescents who were less popular or already low in self-worth. The visual and metricised character of platforms, expressed through likes, follower counts and idealised imagery, invites upward comparison against unrepresentative standards. Valkenburg, Meier and Beyens (2022) situate this within a differential-susceptibility framework, in which the same content produces different effects depending on the user, so comparison harms some adolescents while leaving others largely unaffected.

Displacement of sleep and activity

A second mechanism is displacement: time and attention given to devices may crowd out sleep, physical activity and face-to-face interaction, each of which independently supports wellbeing. Night-time use and notification-driven arousal are repeatedly associated with shortened and disrupted sleep, and the RCH National Child Health Poll (2021) identified sleep interference as a leading parental concern. Because adolescent sleep is already biologically fragile, even modest displacement may carry a disproportionate cost for mood and daytime functioning.

Cyberbullying and online victimisation

Third, social media provides a vector for bullying that follows adolescents beyond the school gate. The eSafety Commissioner (2023) reports that a substantial minority of Australian young people encounter cyberbullying, hurtful content or unwanted contact, and that victimisation is associated with distress and with reluctance to disclose. Unlike much offline bullying, online victimisation can be persistent, public and difficult to escape, which may intensify its psychological impact.

Directionality and longitudinal evidence

Establishing that social media use and poor mental health co-occur is easier than establishing which precedes which. Most evidence remains cross-sectional and therefore cannot distinguish social media as a cause of distress from distress as a driver of social media use, the reverse-causation possibility in which already-struggling adolescents retreat online. Keles, McCrae and Grealish (2020), in a systematic review, found consistent but modest associations between social media use and depression, anxiety and psychological distress, while cautioning that the predominance of cross-sectional designs limited causal inference.

Longitudinal work offers firmer ground. Boers, Afzali, Newton and Conrod (2019), in a four-year study of adolescents, used within-person modelling, which controls for stable individual differences, and found that increases in an adolescent’s own social media use were associated with concurrent increases in depressive symptoms. Because the study drew on within-person methods and included investigators connected to Australian research programs, it is among the more persuasive demonstrations of a directional link, although the effects were small. Odgers and Jensen (2020), reviewing the field, conclude that associations are real but modest, heterogeneous and probably bidirectional, operating through reinforcing spirals rather than a single causal arrow. The honest summary is that social media is neither the principal driver of the recent decline in youth mental health nor causally inert; its contribution appears small on average and unevenly distributed across young people.

Vulnerable subgroups

Averages conceal important heterogeneity, and the differential-susceptibility perspective predicts that effects concentrate in identifiable groups. Adolescent girls report the strongest associations in several datasets, consistent with greater engagement in appearance-based comparison and in relational forms of aggression; AIHW (2024) data show higher rates of psychological distress and self-harm presentations among young women. Vannucci, Flannery and Ohannessian (2017) found that greater daily social media use was associated with higher anxiety, an association more pronounced for some individuals than others.

Adolescents with pre-existing mental health difficulties may also be more reactive, both more likely to seek out particular online environments and more affected by what they encounter there. At the same time, some marginalised groups, including LGBTQ+ young people and those in rural areas served unevenly by in-person services, may derive disproportionate benefit from online connection and identity-affirming community. Susceptibility, in other words, cuts both ways: the adolescents most at risk of harm from certain uses may also be those with the most to gain from others. This complicates blanket restrictions and argues for targeted, needs-based responses rather than uniform limits.

Protective factors and digital literacy

The literature increasingly frames social media as a modifiable environment rather than a fixed exposure. Parental mediation, particularly active mediation that involves conversation about online experiences rather than surveillance alone, is associated with better outcomes. Digital literacy, the capacity to evaluate content critically, manage privacy and recognise manipulative design, is a plausible protective skill and a focus of the eSafety Commissioner’s (2023) educational programs. Services matter too: headspace, Australia’s national youth mental health foundation, embeds attention to a young person’s online life within its early-intervention model (Rickwood et al., 2019), recognising that the digital and offline worlds of adolescence are inseparable. Positive, purposeful use, whether maintaining friendships, accessing help-seeking information or finding community, can support rather than erode wellbeing. Framing the task as building competence and healthy norms, rather than simply reducing exposure, aligns with the balance of evidence and avoids positioning young people as passive victims of technology.

Synthesis and gaps

Table 1 summarises eight representative studies spanning cross-sectional surveys, longitudinal cohorts and systematic and umbrella reviews. Read together, they support four conclusions: associations between social media use and poorer adolescent mental health are real but small on average; they are stronger for specific uses and specific young people than for screen time in general; longitudinal evidence points to modest, probably bidirectional effects; and the quality of measurement remains a serious limitation. Figure 1 maps how the reviewed mechanisms connect use to outcomes and where individual vulnerability and digital literacy moderate the pathway.

Two gaps stand out. The first is causality. Even the strongest within-person studies leave open the possibility of unmeasured confounding, and experimental or quasi-experimental designs remain scarce; the phased introduction of Australia’s under-16 restriction, if evaluated rigorously, could function as a natural experiment. The second is measurement. Most studies rely on self-reported screen time, which correlates poorly with logged usage, and treat “social media” as a single undifferentiated exposure despite obvious differences between platforms and between active and passive engagement. Objective usage data, experience-sampling methods and platform-specific analysis are needed, as is disaggregation by content type. Australian longitudinal data are particularly limited; much evidence is imported from the United States and United Kingdom, and although the AIHW (2024) and ABS (2023) provide robust prevalence figures, they do not supply the granular, repeated behavioural measures that the causal questions demand.

Implications for Australian policy and services

The evidence base sits awkwardly beside the confidence of recent policy. The Online Safety Amendment (Social Media Minimum Age) Act 2024, restricting accounts to those aged 16 and over, responds to genuine public concern and to real harms such as the cyberbullying documented by the eSafety Commissioner (2023). Yet the average effect sizes in the research literature are small, the mechanisms are use-specific, and some adolescents rely on online connection for support, so a uniform age threshold risks removing benefits along with harms and may displace use to less regulated spaces.

A defensible reading of the evidence supports several complementary measures. First, regulation should be paired with rigorous, independent evaluation, treating the age restriction as a testable intervention rather than a settled solution. Second, investment in digital literacy, delivered through schools and the eSafety Commissioner, targets the mechanisms of comparison, displacement and victimisation that the evidence implicates, and does so without foreclosing benefit. Third, clinical and youth services such as headspace should routinely incorporate a young person’s online life into assessment, given its entanglement with sleep, relationships and identity. Fourth, sustained Australian data collection through the AIHW, the ABS and instruments such as the RCH National Child Health Poll would reduce reliance on overseas findings and allow effects to be tracked as platforms and regulation change. The overarching implication is proportionate: policy is best framed around the quality and context of use, the young people most affected, and the skills that let adolescents navigate an online environment that is now a routine part of Australian adolescence.

Conclusion

The weight of evidence indicates that social media use is associated with adolescent mental health, but that the association is small on average, concentrated in particular uses and particular young people, and probably bidirectional rather than simply causal. The early framing around screen time has given way to a more precise concern with the quality of use and with the mechanisms, namely social comparison, the displacement of sleep and activity, and cyberbullying, through which both harm and benefit are transmitted. For Australia, where policy has moved decisively with the under-16 age restriction, the priorities are to evaluate that measure rigorously, to build digital literacy and protective norms, to embed online life within youth mental health services, and to invest in local longitudinal data. Treated as a modifiable environment rather than an unavoidable hazard, social media becomes a challenge that education, service design and proportionate regulation can realistically address.

References

Australian Bureau of Statistics. (2023). National study of mental health and wellbeing, 2020-2022. Australian Bureau of Statistics.

Australian Institute of Health and Welfare. (2024). Mental health of young Australians. Australian Institute of Health and Welfare.

Boers, E., Afzali, M. H., Newton, N., & Conrod, P. (2019). Association of screen time and depression in adolescence. JAMA Pediatrics, 173(9), 853-859.

eSafety Commissioner. (2023). The digital lives of young Australians. Australian Government.

Keles, B., McCrae, N., & Grealish, A. (2020). A systematic review: The influence of social media on depression, anxiety and psychological distress in adolescents. International Journal of Adolescence and Youth, 25(1), 79-93.

Nesi, J., & Prinstein, M. J. (2015). Using social media for social comparison and feedback-seeking: Gender and popularity moderate associations with depressive symptoms. Journal of Abnormal Child Psychology, 43(8), 1427-1438.

Odgers, C. L., & Jensen, M. R. (2020). Annual research review: Adolescent mental health in the digital age. Journal of Child Psychology and Psychiatry, 61(3), 336-348.

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

Przybylski, A. K., & Weinstein, N. (2017). A large-scale test of the Goldilocks hypothesis. Psychological Science, 28(2), 204-215.

Rickwood, D., Telford, N., Parker, A., Tanti, C., & McGorry, P. (2019). headspace: Australia’s national youth mental health foundation. Medical Journal of Australia, 210(S10), 25-30.

Royal Children’s Hospital. (2021). Screen time and young people. RCH National Child Health Poll. Royal Children’s Hospital Melbourne.

Twenge, J. M., & Campbell, W. K. (2018). Associations between screen time and lower psychological well-being among children and adolescents. Preventive Medicine Reports, 12, 271-283.

Valkenburg, P. M., Meier, A., & Beyens, I. (2022). Social media use and its impact on adolescent mental health: An umbrella review of the evidence. Current Opinion in Psychology, 44, 58-68.

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