Frontiers in public health · 2026
Machine learning prediction models for the popularization and dissemination of medical science popularization videos.
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Cheng Nuo, Wang Xiu-Ling, Zeng Xiao-Xue, Li Hui-Jun, Xu Chenxuan, Ma Yan-Ning, Gong Da-Xin, Yuan Yonghui, Zang Shuang, Zhang Guang-Wei
Аннотация
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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. Subsequently, four best-performing models were ultimately confirmed through internal and external validation, all of which were RF models, including "Thumb-Up" (AUC = 0.8802), "Collection" (AUC = 0.7685), "Share" (AUC = 0.7872), "Comment" (AUC = 0.8077). Weight analysis identified the video duration, video description length, and shooting in department office emerged as the most three crucial parameters across all four models.
CONCLUSION: This study demonstrates the significant impact of non-medical factors on the dissemination of medical science popularization videos, and shows that prediction models base on these factors can effectively forecast video spread and popularity. These findings may contribute to enhancing These findings, thereby advancing health education and strengthen public health literacy.
METHODS: 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.
RESULTS: 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. Subsequently, four best-performing models were ultimately confirmed through internal and external validation, all of which were RF models, including "Thumb-Up" (AUC = 0.8802), "Collection" (AUC = 0.7685), "Share" (AUC = 0.7872), "Comment" (AUC = 0.8077). Weight analysis identified the video duration, video description length, and shooting in department office emerged as the most three crucial parameters across all four models.
CONCLUSION: This study demonstrates the significant impact of non-medical factors on the dissemination of medical science popularization videos, and shows that prediction models base on these factors can effectively forecast video spread and popularity. These findings may contribute to enhancing These findings, thereby advancing health education and strengthen public health literacy.