Quality and Perception of Attention-Deficit/Hyperactivity Disorder Content on TikTok: Cross-Sectional Study
Notice bibliographique
Résumé
Background: Social media platforms are increasingly used for both sharing and seeking health-related information online. TikTok has become one of the most widely used social networking platforms. One health-related topic trending on TikTok recently is attention-deficit/hyperactivity disorder (ADHD). However, the accuracy of health-related information on TikTok remains a significant concern. Misleading information about ADHD on TikTok can increase stigmatization and lead to false "self-diagnosis," pathologizing of normal behavior, and overuse of care. Objective: This study aims to investigate the quality and usefulness of popular TikTok videos about ADHD and to explore how this content is perceived by the viewers based on an in-depth analysis of the video comments. Methods: We scraped data from the 125 most liked ADHD-related TikTok videos uploaded between July 2021 and November 2023 using a commercial scraping software. We categorized videos based on the usefulness of their content as "misleading," "personal experience," or "useful" and used the Patient Education Materials Assessment Tool for Audiovisual Materials to evaluate the video quality regarding understandability and actionability. By purposive sampling, we selected 6 videos and analyzed the content of 100 randomly selected user comments per video to understand the extent of self-identification with ADHD behavior among the viewers. All qualitative analyses were carried out independently by at least 2 authors; the disagreement was resolved by discussion. Using SPSS (version 27; IBM Corp), we calculated the interrater reliability between the raters and the descriptive statistics for video and creator characteristics. We used one-way ANOVA to compare the usefulness of the videos. Results: We assessed 50.4% (63/125) of the videos as misleading, 30.4% (38/125) as personal experience, and 19.2% (24/125) as useful. The Patient Education Materials Assessment Tool for Audiovisual Materials scores for all videos for understandability and actionability are 79.5% and 5.1%, respectively. With a score of 92.3%, useful videos scored significantly higher for understandability than misleading and personal experience videos (P<.001). For actionability, there was no statistically significant difference depending on the videos' usefulness (P=.415). Viewers resonated with the ADHD-related behaviors depicted in the videos in 220 out of 600 (36.7%) of the comments and with ADHD in 32 out of 600 (5.3%) of the comments. Self-attribution of behavioral patterns varied significantly, depending on the usefulness of the videos, with personal experience videos showing the most comments on self-attribution of behavioral patterns (102/600, 17% of comments; P<.001). For self-attribution of ADHD, we found no significant difference depending on the usefulness of the videos (P=.359). Conclusions: The high number of misleading videos on ADHD on TikTok and the high percentage of users who self-identify with the symptoms and behaviors presented in these videos can potentially increase misdiagnosis. This highlights the need to critically evaluate health information on social media and for health care professionals to address misconceptions arising from these platforms.
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 ».