Analyzing Social Media Policies on Muscle-Building Drugs and Dietary Supplements
Bibliographic record
Abstract
BACKGROUND: Use of legal and illegal muscle-building drugs and dietary supplements has been linked to many adverse health and social outcomes. Research has shown that social media use is associated with the use of these drugs and dietary supplements; however, it remains unknown whether social media companies have specific policies related to the content and advertising of muscle-building drugs and dietary supplements on their platforms. Therefore, this study aimed to assess the content and advertising policies of eight popular social media companies related to muscle-building drugs and dietary supplements. METHODS: Content and advertising policies for YouTube, TikTok, Instagram, Snapchat, Facebook, Twitter, Twitch, and Reddit were analyzed in November 2022 to determine whether there were any provisions related to legal (e.g., whey protein) and illegal (e.g., anabolic-androgenic steroids) muscle-building drugs and dietary supplements. Policies were classified as either none, restricted, or prohibited. RESULTS: All eight social media platforms had explicit policies prohibiting user-generated content and advertising of illicit drugs and substances (e.g., anabolic-androgenic steroids). User-generated content and advertising policies related to legal muscle-building dietary supplements across the platforms varied; however, none of the eight social media companies had a specific policy regarding user content. CONCLUSIONS: Findings underscore the need for stronger social media content and advertising policies related to legal muscle-building dietary supplements.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".