Information Regarding Dermatology as Seen on the Social Media Platform TikTok
Bibliographic record
Abstract
Introduction: With Americans spending over two hours daily on social media, platforms like TikTok have become prevalent sources for healthcare information. This study evaluates the quality and quantity of dermatology-related content on TikTok. Methods: In November 2022, TikTok was searched using dermatology-related keywords. Accounts with ≥50% dermatology content were selected. An engagement score was calculated for each account, and the top 10 were further analyzed using DISCERN criteria. The Kruskal-Wallis and Mann-Whitney U tests, along with a two-tailed independent t-test, were employed for statistical analysis. Results: Out of 22,411 videos, 17,085 (76.3%) were informational. Dermatologists led in paid sponsorship videos (65% of 502 videos). Significant differences in engagement scores were found across different provider types, with medical clinics and aestheticians receiving the highest scores. Dermatologist-run accounts had higher views and comments but similar overall DISCERN scores to non-dermatologist accounts. However, dermatologists better referenced treatment uncertainty and explanations, while non-dermatologists more frequently discussed treatment risks. Discussion: The substantial engagement with dermatology content on TikTok highlights its role as a significant information source, albeit with generally low educational quality. Given the high consumer trust in TikTok, dermatologists face an ethical obligation to improve the accuracy and quality of their online content to counteract potential misinformation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 teacher head, 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".