Accepted for/Published in: Journal of Medical Internet Research

Date Submitted:

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closed for review but you can still tweet
  • Nuo C, Xiu-Ling W, Yang M, Hui-Jun L, Yan-Ning M, Yong-Hui Y, Da-Xin G, Shuang Z, Guang-Wei Z
  • Machine learning prediction models for the popularization and dissemination of medical science popularization videos
  • Journal of Medical Internet Research
  • DOI: 10.2196/11848
  • PMID: 30303485
  • PMCID: 6352016
Preprints Accepted Manuscript

Machine learning prediction models for the popularization and dissemination of medical science popularization videos

Abstract

background

Medical science popularization short videos have become a powerful tool for widely disseminating medical knowledge and information, with non-medical factors significantly affecting their dissemination and attractiveness.

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=286), Bilibili (n=50), Xiaohongshu (n=54), and International TikTok (n=24) platforms. Thirty-four non-medical features were annotated as predictor variables, while four dissemination metrics—“Thumb-Up”, “Comment”, “Share” and “Collection” counts—were recorded as outcome indicators. Thirteen algorithms were employed to train prediction models for each outcome using the TikTok dataset and validated on the remaining three datasets, with model performance evaluated by area under the curve (AUC).

results

In the quantitative analysis of the 4 outcome indicators, we identified significant disparities among different videos. The number of “Thumb-Up” range from 0 to 2.72 million, the number of “Collection” range from 1 to 0.14 million, the number of “Share” range from 4 to 898 thousand, the number of “Comment” range from 0 and 200 thousand. Subsequently, four best-performing models were ultimately confirmed through internal and external validation, including “Thumb-Up” RF Model (AUC=0.7331), “Collection” RF Model (AUC=0.7439), “Share” RF Model (AUC=0.7077), “Comment” RF Model (AUC=0.7960). As revealed by weight analysis, the video duration, title and description length, shooting location emerged and body language as the most five crucial parameters across all four models.

conclusions

The ML models developed in this study demonstrate strong predictive capacity in assessing the influence of non-medical factors on the dissemination of medical science popularization videos. The identified weights of influencing factors offer valuable guidance for optimizing video preparation. This study contributes to enhancing the reach and public acceptance of medical science popularization videos, thereby advancing health education and fostering greater public awareness and competence in healthcare.

clinicalTrial

none

As per the author’s request the PDF is not available.
Copyright

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it’s website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.

Citation

Please cite as:

Nuo C, Xiu-Ling W, Yang M, Hui-Jun L, Yan-Ning M, Yong-Hui Y, Da-Xin G, Shuang Z, Guang-Wei Z

Machine learning prediction models for the popularization and dissemination of medical science popularization videos

Journal of Medical Internet Research

DOI: 10.2196/14886

URL: https://preprints.jmir.org/preprint/11848

PMID: 31789598

PMCID: 6352016

Machine learning prediction models for the popularization and dissemination of medical science popularization videos

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