ECT-Related Video Quality Scores Across TikTok, BiliBili, and YouTube
by Yuxia Fan·Updated 1mo ago
14.3 KB1files
Available on 1 platform
Sign in to view source links and access this dataset
Description
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.
Primary data file is a DOCX document (14.3 KB); the underlying tabular data likely requires extraction.