ECT-Related Video Quality Scores Across TikTok, BiliBili, and YouTube

A cross-sectional analysis of 232 videos from TikTok, BiliBili, and YouTube, collected on December 8, 2025. The dataset, authored by Yuxia Fan and shared on figshare, contains quality, reliability, and dissemination metrics for electroconvulsive therapy-related content. It includes scores from the Global Quality Scale, modified DISCERN, and the Medical Video Evaluation Tool.

Use Cases

• Compare video quality and reliability across social media platforms based on the described scoring tools.
• Analyze correlations between uploader identity, presentation format, and content quality as mentioned in the results.
• Investigate the relationship between engagement metrics and informational quality across different platforms.
• Identify factors associated with higher informational quality for electroconvulsive therapy content.

Strengths

• Includes 232 videos (71 TikTok, 75 BiliBili, 86 YouTube) from a specific collection date (December 8, 2025).
• Assessed using three established evaluation tools: GQS, mDISCERN, and MQ-VET.
• Released under a permissive CC-BY-4.0 license, facilitating reuse.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for the underlying data table is unknown, which may limit suitability assessment.
• Data reflects a single snapshot in time (December 2025); freshness should be verified for longitudinal studies.

Provenance

Source

figshare, authored by Yuxia Fan.

Collection Method

Top 100 videos retrieved using specific keywords on each platform were screened and assessed by independent raters.

Time Range

Data collected on a single day: December 8, 2025.

Freshness

Last updated 2026-04 20 05:22:45.

Geography

Platforms studied are global (YouTube) and China-focused (TikTok, BiliBili); video content language is Chinese and English.