The quality and reliability of short videos about uraemia on BiliBili and TikTok: a cross-sectional study

This study aimed to assess the quality and reliability of short videos about uraemia on BiliBili and TikTok. On June 12, 2025, we searched the top 100 videos arranged by default using the keyword "uraemia" on both platforms. We collected and analysed their characteristics, content, and uploaders. To evaluate the quality, the Global Quality Score (GQS) and the modified DISCERN tool (mDISCERN) were used. This study included 153 videos in total, with 81 from TikTok and 72 from Bilibili. Overall, the audience engagement on TikTok was significantly greater than that on Bilibili. Although the median mDISCERN and GQS scores were identical across platforms (2 [2-2] and 2 [2-3], respectively), their score distributions differed significantly (p < 0.05). Interestingly, we found that videos from self-media achieved higher GQS scores than those from doctors or official media. A weak positive correlation was found between video quality and collections and shares on Bilibili alone (p < 0.05). In conclusion, the quality and reliability of health information from uraemia videos on TikTok and Bilibili were found to be suboptimal. Concerted efforts from the public, uploaders, and social media platforms are needed to foster the dissemination of high-quality health information.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-12-24
DOI
https://doi.org/10.1038/s41598-025-28155-7
Citations
1
Primary Topic
Social Media in Health Education
Type
article
Field-Weighted Citation Impact
2.17
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The quality and reliability of short videos about uraemia on BiliBili and TikTok: a cross-sectional study

Jiaqin Yuan, Hui Zeng, Jia Tang, Bo Zhang et al.
1 citations
Scientific Reports
Social Media in Health Education
2.17
article

The quality and reliability of short videos about uraemia on BiliBili and TikTok: a cross-sectional study

Jiaqin Yuan, Hui Zeng, Jia Tang, Bo Zhang, Deqiong Xie
article en
1 citations

Abstract

This study aimed to assess the quality and reliability of short videos about uraemia on BiliBili and TikTok. On June 12, 2025, we searched the top 100 videos arranged by default using the keyword "uraemia" on both platforms. We collected and analysed their characteristics, content, and uploaders. To evaluate the quality, the Global Quality Score (GQS) and the modified DISCERN tool (mDISCERN) were used. This study included 153 videos in total, with 81 from TikTok and 72 from Bilibili. Overall, the audience engagement on TikTok was significantly greater than that on Bilibili. Although the median mDISCERN and GQS scores were identical across platforms (2 [2-2] and 2 [2-3], respectively), their score distributions differed significantly (p < 0.05). Interestingly, we found that videos from self-media achieved higher GQS scores than those from doctors or official media. A weak positive correlation was found between video quality and collections and shares on Bilibili alone (p < 0.05). In conclusion, the quality and reliability of health information from uraemia videos on TikTok and Bilibili were found to be suboptimal. Concerted efforts from the public, uploaders, and social media platforms are needed to foster the dissemination of high-quality health information.

Scientific ReportsVol. 15(1)
Army Medical University (CN), Affiliated Hospital of Southwest Medical University (CN), Daping Hospital (CN), Second People’s Hospital of Yibin (CN)
Quality Education
Openalex Percentile: Top 9%
Social Media in Health Education
2.17
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.