Comments Under Dermatologists’ TikTok Videos on Atopic Dermatitis: A Content Analysis of Audience Interaction (Preprint)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».