Depression Video Quality And Engagement Metrics From TikTok

A dataset contains extracted data from a study evaluating depression-related videos on TikTok. It includes video characteristics, publisher types, engagement metrics, and quality scores from mDISCERN, JAMA, and GQS evaluations. Two independent reviewers assessed the videos, with the data supporting analysis of video quality, reliability, and educational value.

Use Cases

• Analyze correlations between engagement metrics (likes, comments, shares, collections) and quality evaluation scores (mDISCERN, JAMA, GQS).
• Compare video characteristics and publisher types to identify patterns in content quality and audience engagement.
• Train a model to predict quality scores (mDISCERN, JAMA, GQS) based on publisher types and video characteristics.
• Investigate the reliability and educational value of depression-related content by statistically analyzing reviewer scoring results.

Strengths

• Includes multiple quality assessment frameworks: mDISCERN, JAMA benchmark criteria, and Global Quality Scale (GQS).
• Data was evaluated by two independent reviewers, supporting reliability for statistical analysis.
• Contains both content metrics (video characteristics, publisher types) and audience engagement metrics (likes, comments, shares, collections).

Limitations

• Sample size and temporal coverage are unknown, limiting generalizability.
• Focus is exclusively on TikTok (Douyin) for depression content, lacking comparative data from other platforms mentioned in the title (Bilibili).
• Potential for subjective bias in human-scored quality evaluations, despite dual review.

Provenance

Source

figshare, author yanbin lin.

Collection Method

Extracted data from TikTok (Douyin) videos, evaluated by two independent reviewers using specified quality scales.

Time Range

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Freshness

Last updated March 2026.

Geography

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