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<p>Background: Despite the rapid rise of short-video platforms as health information sources, the informational quality of robotic-surgery videos—an area requiring precise clinical explanation—remains insufficiently studied. Objective: To compare the quality and reliability of robotic-surgery videos on TikTok and Bilibili and determine how platform features and uploader identity shape content quality. Methods: We conducted a cross-sectional analysis of 200 videos (100 per platform) scored using the Global Quality Score (GQS) and modified DISCERN (mDISCERN). Videos were coded by uploader type and theme, and group differences and predictors were examined with nonparametric tests and multivariable regression. Results: TikTok videos drew more engagement, but Bilibili posts were longer and scored higher on GQS and mDISCERN. Professional uploaders produced the most reliable content. Engagement metrics correlated with each other yet did not predict quality in regression. Conclusions: On short-video platforms, visibility and scientific accuracy diverge—highlighting the need for professional involvement, quality-sensitive governance, and stronger public digital health literacy.</p>

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Do likes reflect quality? Engagement metrics and information reliability of robotic surgery short videos

  • 期刊论文

  • 发表日期：

    2026年1月19日

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  • Haoran Dai,
  • Jingyang Liu,
  • Yuan Chen,
  • Yao Yang,
  • Yanling Zhou,
  • Zhiwen Zheng,
  • Zheng Gong,
  • Xingfu Ma,
  • Xianzuo Zhang,
  • Junchen Zhu

摘要

Background: Despite the rapid rise of short-video platforms as health information sources, the informational quality of robotic-surgery videos—an area requiring precise clinical explanation—remains insufficiently studied. Objective: To compare the quality and reliability of robotic-surgery videos on TikTok and Bilibili and determine how platform features and uploader identity shape content quality. Methods: We conducted a cross-sectional analysis of 200 videos (100 per platform) scored using the Global Quality Score (GQS) and modified DISCERN (mDISCERN). Videos were coded by uploader type and theme, and group differences and predictors were examined with nonparametric tests and multivariable regression. Results: TikTok videos drew more engagement, but Bilibili posts were longer and scored higher on GQS and mDISCERN. Professional uploaders produced the most reliable content. Engagement metrics correlated with each other yet did not predict quality in regression. Conclusions: On short-video platforms, visibility and scientific accuracy diverge—highlighting the need for professional involvement, quality-sensitive governance, and stronger public digital health literacy.