A cross sectional content analysis evaluates chemotherapy health information quality and reliability on TikTok and...

Short-video platforms, specifically TikTok and Bilibili, are widely utilized for health information seeking in China. However, the quality of chemotherapy-related content on these channels remains unverified. This study compares the reliability and quality of chemotherapy videos across these two platforms, evaluating the impact of uploader identity, video characteristics, and user engagement metrics. A total of 188 chemotherapy-related videos were retrieved from TikTok (n = 89) and Bilibili (n = 99). Content quality and reliability were independently assessed using the Global Quality Scale (GQS) and modified DISCERN (mDISCERN). Intra-platform interactions, video length, and uploader profiles were cross-sectionally analyzed using localized stratified Spearman's rank correlation and fully adjusted multivariate Poisson regression models. Overall information quality was suboptimal. Significant baseline disparities were observed between platforms: while TikTok dominated in interaction metrics (P < .001), Bilibili achieved significantly higher reliability (mDISCERN, P < .001) and overall quality scores (GQS, P = .034). Certified oncologists were the primary contributors (46.28%), producing significantly higher-quality content than patients (P < .001). Localized stratified analysis exposed a platform-specific popularity paradox: within TikTok, GQS scores were significantly negatively correlated with likes (r = - .211, P = .047) and comments (r = - .269, P = .011), whereas this quality-engagement trade-off completely vanished within Bilibili, where mDISCERN positively aligned with collections (r = .221, P = .028) and shares (r = .249, P = .013). Fully adjusted Poisson regression confirmed that micro-level engagement counts and video length possessed no independent predictive capacity (P > .20), whereas the macro-platform identity of Bilibili emerged as a robust, standalone independent positive predictor of information reliability (RR = 1.388, 95% CI 1.069-1.801, P = .0138). A critical socio-technical divide exists within the digital health landscape, manifested as a localized popularity paradox heavily plaguing engagement-first networks like TikTok. Fully adjusted estimations reveal that the overarching macro-platform architecture itself, rather than micro-level clip features or standalone video length, serves as the independent determinant of clinical reliability. Platforms must transition from traffic-oriented metrics toward professional, quality-weighted recommendation system interventions to effectively bridge the gap between evidence-based clinical rigor and public digital accessibility.