Prevalence and Patterns of Social Media Use in Early Adolescents
Bibliographic record
Abstract
OBJECTIVE: To describe patterns of social media use, including underage use (under 13 years) and sex differences, in a diverse, national sample of early adolescents in the US. METHODS: We analyzed the social media use data in the Adolescent Brain Cognitive Development Study (2019-2021, Year 3), which includes a national sample of early adolescents in the US. Specifically, using Chi-square and t-tests, we compared social media use patterns across demographic characteristics stratified by age and sex. RESULTS: In the sample of 10,092 11-to-15-year-old adolescents, 69.5% had at least one social media account; among social media users, the most common platforms were TikTok (67.1%), YouTube (64.7%), and Instagram (66.0%). A majority (63.8%) of participants under 13 years (minimum age requirement) reported social media use. Under-13 social media users had an average of 3.38 social media accounts, with 68.2% having TikTok accounts and 39.0% saying TikTok was the social media site they used the most. Females reported higher use of TikTok, Snapchat, Instagram, and Pinterest, while males reported higher use of YouTube and Reddit. Additionally, 6.3% of participants with social media accounts reported having a secret social media account hidden from their parents' knowledge. CONCLUSIONS: Our findings reveal a high prevalence of underage social media use in early adolescence. These findings can inform current policies and legislation aimed at more robust age verification measures, minimum age requirements, and the enhancement of parental controls on social media. Clinicians can counsel about the potential risks of early adolescent social media use.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".