Supplementary file 1_Machine learning prediction models for the popularization and dissemination of medical science popularization videos.docx

Objective<p>To summarize the current production and release trends of medical science popularization videos, analyze the effect of non-medical factors on their spread, and develop dissemination-prediction models using machine learning (ML) algorithms.</p>Methods<p>We identified a sample of medical science popularization videos on TikTok (n = 566), Bilibili (n = 50), Xiaohongshu (n = 54), and International TikTok (n = 46) platforms.

Thirty six non-medical features were annotated as predictor variables, with “Thumb-Up,” “Comment,” “Share” and “Collection” as outcomes. Fifteen algorithms models were constructed for each outcome using the TikTok dataset and validated on the remaining three datasets, with model performance evaluated by area under the curve (AUC) and Brier score.</p>Results<p>In the quantitative analysis of the 4 outcomes, we identified significant disparities among different videos, “Thumb-Up” with a range from 0 to 2.72 million, “Collection” with 1 to 1.36 million, “Share” with 1 to 898 thousand, and “Comment” with 0 and 200 thousand.