Popularity Does Not Equate to Quality: A Cross-Sectional Analysis of Post-stroke depression Short Videos on Chinese Social Media

【Abstract】 Objective This study aimed to evaluate the content quality and reliability of short videos related to Post-stroke depression on three major social media platforms: TikTok (Douyin), Bilibili, and Kuaishou. Methods A cross-sectional study was conducted on November 7, 2025. Using newly registered accounts, we searched for the top 100 videos ranked by comprehensive algorithms for the keyword “Post-stroke depression”in Chinese on each platform, and a total of 300 videos were included for analysis. We categorized the basic characteristics, content types, and publishers of the videos, and assessed their quality and reliability using the Global Quality Score and the modified DISCERN instrument. Nonparametric tests, correlation analysis, and regression analysis were performed to explore the associations between video characteristics and their quality or reliability. Results Significant differences in video characteristics were observed among the three platforms (P<0.01). Bilibili videos were longer in duration, whereas Douyin and Kuaishou exhibited higher user engagement. All platforms were dominated by videos focusing on disease symptoms, uploaded primarily by individual professionals, among whom neurologists accounted for the largest proportion. Video quality and reliability differed across platforms: Bilibili scored significantly higher in quality than Kuaishou (P<0.01), and Douyin scored significantly higher in reliability than Kuaishou (P<0.01). Videos concerning disease symptoms and those uploaded by individual professionals achieved higher quality and reliability scores, while commercial or miscellaneous videos and content from non-professional institutions scored lower. Various user engagement metrics were positively correlated with each other, and video duration was negatively associated with the number of collections. However, neither video duration nor engagement indicators showed significant associations with quality or reliability (P>0.05). Conclusion Professional background and content type are key determinants of video quality, whereas current platform algorithms fail to prioritize high-quality content based on popularity.