Effects of Social Media Use on Youth and Adolescent Mental Health: A Scoping Review of Reviews
Bibliographic record
Abstract
Background: The impact of social media on adolescent mental health has become a critical area of research as social media usage has surged among youth. Despite extensive research, findings on this relationship remain inconsistent, with various studies reporting both negative and positive effects. This scoping review aims to clarify the multifaceted nature of this relationship by analyzing the recent literature. Objective: This review aims to analyze the current evidence regarding the effects of social media use on adolescent mental health, identify consistent patterns and discrepancies in the findings, identify gaps in our knowledge, and highlight opportunities for further research. Methods: A scoping review was conducted following Arksey and O’Malley’s five-stage approach. Searches were performed in PubMed, MEDLINE, Web of Science, and Scopus for articles published between July 2020 and July 2024. Inclusion criteria were systematic reviews, umbrella reviews, narrative reviews, and meta-analyses written in English focusing on youth/adolescents’ mental health and social media. The search strategy identified 1005 articles, of which 43 relevant articles survived the reviewer selection process, from which data were extracted and analyzed to inform this review. Results: The majority of studies linked social media use to adverse mental health outcomes, particularly depression and anxiety. However, the relationship was complex, with evidence suggesting that problematic use and passive consumption of social media were most strongly associated with adverse effects. In contrast, some studies highlighted positive aspects, including enhanced social support and reduced isolation. The mental health impact of social media use, specifically during the COVID-19 pandemic, was mixed, with the full range of neutral, negative, and positive effects reported. Conclusions: The nature of social media’s impact on adolescent mental health is highly individualistic and influenced by moderating factors. This review supports the notion that social media’s effects on adolescent mental health can be context specific and may be shaped by patterns of usage. A focus on longitudinal studies in future research will be useful for us to understand long-term effects and develop targeted interventions in this context. Enhancing digital literacy and creating supportive online environments are essential to maximizing the benefits of social media while mitigating its risks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".