Quality Assessment of Dry Eye Educational Videos on Xiaohongshu by Uploader Type

A cross-sectional study of 136 dry eye educational videos collected from the Xiaohongshu platform on April 16, 2026. The dataset includes video characteristics, content coverage, engagement metrics, and quality scores assessed using DISCERN, PEMAT-A/V, and the Global Quality Score. It was authored by Shiyang Niu and published under a CC-BY-4.0 license.

Use Cases

• Compare video quality scores based on uploader type (medical vs. non-medical users).
• Analyze the correlation between the number of content domains covered and quality assessment scores.
• Investigate the prevalence of incorrect or potentially misleading information in health education videos.

Strengths

• Includes 136 videos with detailed quality assessments using three standardized instruments (DISCERN, PEMAT-A/V, GQS).
• Compares 108 videos from non-medical individual users against 28 from individual medical users, revealing significant quality differences.
• Reports specific statistical findings, such as an odds ratio of 11.23 for incorrect information prevalence.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is small (67.4 KB), representing a limited snapshot from a single day in 2026.

Provenance

Source

figshare

Collection Method

Videos were collected from Xiaohongshu's 'Video' section using the keyword '干眼' (dry eye); the first 200 results from the default sorting algorithm were screened, with 136 meeting inclusion criteria.

Time Range

Data collection occurred on a single day: April 16, 2026.

Freshness

Last updated 2026-06-03 04:47:10; freshness should be verified.

Geography

The data source is the Chinese social media platform Xiaohongshu; the geographic focus of the content is not specified.