Cross-platform analysis of atrial fibrillation scientific videos: using composite index and a basic assessment scale.

Background: Currently, video platforms were filled with many low-quality, uncensored scientific videos, and patients who utilize the Internet to gain knowledge about specific diseases are vulnerable to being misled and possibly delaying treatment as a result. Therefore, a large sample survey on the content quality and popularity of online scientific videos was of great significance for future targeted reforms. Objective: This study utilized normalization data analysis methods and a basic assessment scale, providing a new aspect for future research across multiple platforms with large sample sizes and for the development of video content quality assessment scales. Methods: This cross-sectional study analyzed a sample of 331 videos retrieved from YouTube, BiliBili, TikTok, and Douyin on June 13, 2024. In the analysis of atrial fibrillation scientific videos across four social media platforms, comprehensive metrics and a basic scoring scale revealed associations between platforms, creators, and the popularity and content quality of the videos. Data analysis employed principal component analysis, normalization data processing, non-parametric tests, paired t-tests, and negative binomial regression. Results: Analysis of the user engagement data using a composite index revealed a significant difference in the popularity of videos from publishers with a medical background (z = -4.285, Conclusion: This analysis of atrial fibrillation information on multiple social media platforms found that people prefer videos from publishers with a medical background. However, it appeared that these publishers did not sufficiently create high-quality, suitable videos for the public, and the platforms seemed to lack a rigorous censorship system and policy support for high-quality content. Moreover, the normalized data processing method and the basic assessment scale that we attempted to use in this study provided new ideas for future large-sample surveys and content quality review.