Assessing the quality and reliability of short video platforms as sources of information on depression among Chinese adolescents: a comparative study with data from Douyin, Bilibili, WeChat, and Xiaohongshu APPs
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Abstract
Objective: This study aims to systematically evaluate and compare the quality, reliability, and content characteristics of short videos regarding adolescent depression on four major Chinese social media platforms: Douyin, Bilibili, WeChat, and Xiaohongshu.
Methods: A cross-sectional study was conducted by retrieving videos using specific keywords related to adolescent depression between January 1, 2023, and March 4, 2025. A total of 600 videos (150 from each platform) were screened. Two independent raters assessed the videos using the Global Quality Score (GQS), the Journal of the American Medical Association (JAMA) benchmarking criteria, and the modified DISCERN tool. Statistical analyses, including the Kruskal-Wallis test and Spearman correlation, were employed to determine differences across platforms and factors influencing video quality.
Results: Douyin exhibited significantly higher scores in GQS, JAMA benchmarks, and modified DISCERN compared to other platforms (P < 0.05). Content produced by individual health professionals (81% of the sample) and healthcare organizations demonstrated superior quality and reliability compared to non-professional users (P < 0.01). While Bilibili videos had the longest duration, they did not achieve the highest quality scores. Engagement metrics (likes, comments, and shares) showed a significant positive correlation with video quality scores (P < 0.05).
Conclusion: The quality of information concerning adolescent depression on short video platforms is inconsistent. Douyin and content created by medical professionals currently provide higher-quality information. Platforms should strengthen identity verification for content creators, and medical professionals are encouraged to utilize these platforms more effectively to disseminate accurate mental health knowledge.
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