Quality Analysis of Stroke-Related Videos on Video Platforms: Cross-Sectional Study

Stroke has become a global public health problem due to its high incidence, disability, and mortality. In China, TikTok and Bilibili, as mainstream video-sharing platforms, serve as key sources of getting stroke-related information for people, yet their videos' content quality and reliability remain insufficiently evaluated.

This cross-sectional study aimed to analyze the content and quality of stroke-related videos on Chinese video-sharing platforms.

In March 2025, stroke-related videos were retrieved from TikTok and Bilibili using the search term "" (Chinese for stroke). Eligible videos were analyzed for metadata and content indicators. Researchers assessed video quality using validated tools: the Global Quality Scale (GQS), modified DISCERN (mDISCERN), and Patient Education Materials Assessment Tool (PEMAT). Statistical analyses were performed with Python, including descriptive statistics, group comparisons (Kruskal-Wallis tests), and Spearman's rank correlation to evaluate variable associations, with all P values adjusted for multiple comparisons using the Bonferroni method. A binary classification predictive model was developed using the random forest algorithm, accompanied by feature importance analysis.

Among the stroke-related videos from Bilibili (n=157) and TikTok (n=149), popular science education content predominated (204/306, 66.7%). Bilibili videos were primarily categorized as professional lectures (83/157, 52.9%), while most TikTok videos were popular science education (139/149, 93.3%). TikTok videos demonstrated significantly higher median likes and comments (P<.001) and shorter durations compared to Bilibili (P<.001). No significant differences were observed in median GQS (4) or mDISCERN scores (3) between platforms (P>.05). Videos produced by professional teams exhibited significantly higher GQS and PEMAT-A/V scores than those created by independent content creators (P<.05). Popular science education videos achieved higher PEMAT-A/V actionability scores than professional lectures (P<.001), while videos addressing treatment options scored lowest in GQS (P<.05). Strong positive correlations were identified among user engagement parameters (likes, shares, comments; ρ=0.81-0.90, P<.001), but only weak correlations were found between engagement and quality scores (ρ<0.3). Machine learning modeling (AUC=0.58) identified video duration (importance score: 0.15) and uploader subscriber count (importance score: 0.13) as key predictors of content quality.

The quality of stroke-related videos on TikTok and Bilibili remains suboptimal. Content uploaded by certified physicians and institutions received higher GQS/mDISCERN scores, confirming that medical authority is a key quality indicator. Our exploratory random-forest model, which used only basic metadata (duration, likes, subscriber count), achieved an area under the curve of 0.58, indicating that surface engagement metrics alone are insufficient to discriminate high- from low-quality material. Consequently, future screening algorithms should incorporate content-based features (eg, captions, medical keywords, visual cues) and creator credentials rather than relying solely on readily available interaction parameters. Multi-platform, larger-scale datasets are warranted to develop clinically useful prediction tools.