Web-Based Video Platforms as Sources of Information on Body Image Dissatisfaction in Adolescents: Content and Quality Analysis of a Cross-Sectional Study
Notice bibliographique
Résumé
Background: Body image dissatisfaction among children and adolescents is a significant public health concern and is associated with numerous physical and mental problems. Social media platforms, including TikTok, BiliBili, and YouTube, have become popular sources of health information. However, the quality and reliability of content related to body image dissatisfaction have not been comprehensively evaluated. Objective: The primary goal of this study was to examine the quality and reliability of videos related to body image dissatisfaction on TikTok, BiliBili, and YouTube. Methods: The keywords "body image dissatisfaction" were searched on YouTube, TikTok, and BiliBili in November 2024. Videos were collected based on platform-specific sort filters, including the filter of "Most liked" on TikTok and the filter of "Most viewed" on BiliBili and YouTube. The top 100 videos on each platform were reviewed and screened in the study. After excluding videos that were (1) not in English or Chinese, (2) duplicates, (3) irrelevant, (4) no audio or visual, (5) contained advertisements, and (6) with a Global Quality Scale (GQS) score of 1, the final sample consisted of 64 videos, which formed the basis of our research and subsequent findings. Two reviewers (LL and JNY) screened, selected, extracted data, and evaluated all videos using the GQS, the Modified DISCERN (mDISCERN) scores, and the Modified Journal of the American Medical Association (mJAMA) benchmark criteria. Statistical analysis was performed using SPSS (version 28.0; IBM Corp). Results: In total, 64 videos were analyzed in the study, including 20 from TikTok, 13 from BiliBili, and 31 from YouTube. The median duration of the involved videos was 3.01 (IQR 1.00-5.94) minutes on TikTok, 3.52 (IQR 2.36-5.63) minutes on BiliBili, and 4.86 (IQR 3.10-6.93) minutes on YouTube. Compared with the other 2 platforms, BiliBili videos received higher likes and more comments. The majority of the videos (n=40, 62%) were uploaded by self-media. The quality of the videos on YouTube shows the highest overall scores. Videos uploaded by professional authors had significantly higher GQS, mDISCERN, and mJAMA scores compared to those uploaded by nonprofessionals. There was no significant correlation between video quality and the number of views or likes. However, the number of views and likes were significantly positively correlated. Furthermore, a significant correlation was found between the mJAMA, mDISCERN, and GQS scores. Conclusions: Web-based video platforms have become an important source for adolescents to access health information. However, the lack of a significant correlation between video quality and the number of likes and comments poses a challenge for users seeking reliable health information. It is suggested that the quality of the videos on health information would be taken into consideration in the recommendation algorithm on web-based video 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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».