Comments Under Dermatologists’ TikTok Videos on Atopic Dermatitis: A Content Analysis of Audience Interaction (Preprint)
Bibliographic record
Abstract
Background: TikTok is one of the fastest-growing social media platforms in the world. It has become an important space for sharing information on a wide range of topics, including medical conditions such as atopic dermatitis (AD). Advice on skin conditions has become popular on TikTok, and most previous research in this area focuses on the credibility of the information being shared. However, little research has focused specifically on physician-created videos and their audience engagement and interaction. Objective: Our study aimed to (1) characterize the audience's online response to board-certified physicians' TikTok content related to AD according to established protocols and (2) better understand the interactions that happen among members of the audience in the comment section of this content. Methods: In December 2023, searches were conducted for the terms "atopic dermatitis" and "eczema" on 3 unique TikTok accounts to identify videos created about AD by board-certified dermatologists. A total of 28 final videos were analyzed and classified into the following categories: (1) explanation of disease, (2) recommendation, (3) debunking misinformation, and (4) informal or anecdotal. The top 50 original comments on each of the 28 videos were collected and classified into one of the following categories: (1) "positive personal experience," (2) "negative personal experience," (3) "neutral personal experience," (4) "requesting advice," (5) "learning," (6) "appreciative reaction," (7) "critical reaction," (8) "giving advice," (9) "humor," (10) "tagging another user," and (11) "off-topic." Replies to comments were also analyzed and grouped into similar categories. Results: Video category did not have a significant impact on engagement rate (P>.99). Across all video categories, comments that involved personal experiences or sharing information made up a larger percentage than those that were critical or off-topic (P<.001). Of the comments related to personal experience, the percentage of negative personal experience comments was significantly higher than that of positive personal experience comments (P=.001). Among replies to comments, "recommendation" and "emotional support" replies were significantly more common than other types of replies (P<.001). Conclusions: Our study suggests that videos created by dermatologists on TikTok are generally well received regardless of video style or category. The comment sections appear to provide transient supportive environments where users connect over shared challenges and exchange personal experiences and recommendations. There is a gap in dermatologist-produced TikTok content involving darker skin tones.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".