JMIR Publications

ABSTRACT

Background:

Stroke, a leading global cerebrovascular disease, remains a major public health concern in China due to its persistently high incidence rate, further compounded by low public health literacy. TikTok and Bilibili, as leading Chinese video-sharing platforms, serve as primary sources for stroke-related information due to their accessible health content. However, comprehensive evaluations of video quality and reliability are limited. This study assesses the informational quality of stroke-related videos on TikTok and Bilibili in China.

Objective:

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

Methods:

In March 2025, stroke-related videos were retrieved from TikTok and Bilibili using the search term “卒中” (Chinese for stroke). Eligible videos were analyzed for metadata and content indicators after excluding duplicates, advertisements, and irrelevant content. Two independent 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.

Results:

Among the 306 stroke-related videos from Bilibili (n=157) and TikTok (n=149), popular science education content predominated (n=204, 66.7%). Bilibili videos were primarily categorized as professional lectures (52.9%), while most TikTok videos were popular science education (93.3%). TikTok videos demonstrated significantly higher median likes and comments (p < 0.001), more recent publication dates (p < 0.001), and shorter durations compared to Bilibili (p < 0.001). No significant differences were observed in median GQS (4) or mDISCERN scores (3) between platforms (p > 0.05). Videos produced by professional teams (Certified Medical Professionals/Nonprofit Science Communicators) exhibited significantly higher GQS and PEMAT-A/V scores than those created by Independent Content Creators (p < 0.05). Subgroup analysis of content typology revealed that popular science education videos achieved significantly higher PEMAT-A/V actionability scores than professional lectures (p < 0.001), while videos addressing treatment options scored lowest in GQS (p < 0.05). Strong positive correlations were identified among user engagement parameters (likes, shares, comments; ρ = 0.81–0.90, p < 0.001), but only weak correlations were found between engagement and quality scores (ρ < 0.3). Machine learning modeling (AUC = 0.577) identified video duration (importance score: 0.151) and uploader subscriber count (importance score: 0.130) as key predictors of content quality.

Conclusions:

The quality of stroke-related videos on TikTok and Bilibili remains suboptimal. Videos uploaded by certified physicians and institutions were commended more, given their demonstrated knowledge accuracy, even when navigating highly circulated stroke videos. Machine learning models of basic video parameters cannot effectively predict the content quality (AUC=0.58), but video duration and uploader subscriber count may be potential predictors.