Social media platforms generate billions of dollars in revenue from U.S. youth: Findings from a simulated revenue model
Bibliographic record
Abstract
Social media platforms are suspected to derive hefty profits from youth users who may be vulnerable to negative mental health outcomes, including depression, anxiety, and eating disorders. Platforms, however, are not required to make these data publicly available, which may limit the abilities of researchers and policymakers to adequately investigate and regulate platform practices. This study aimed to estimate the number of U.S.-based child (0-12 years old) and adolescent (13-17 years old) users and the annual advertising revenue generated from youth across six major platforms. Data were drawn from public survey and market research sources conducted in 2021 and 2022. A simulation analysis was conducted to derive estimates of the number of users and the annual advertising revenue per age group and overall (ages 0-17 years) for 2022. The findings reveal that, across six major social media platforms, the 2022 annual advertising revenue from youth users ages 0-17 years is nearly $11 billion. Approximately 30-40% of the advertising revenue generated from three social media platforms is attributable to young people. Our findings highlight the need for greater transparency from social media platforms as well as regulation of potentially harmful advertising practices that may exploit vulnerable child and adolescent social media users.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".