acePerceived AIGC saturation and AI content fatigue on short-video platforms: An appraisal-based structural equation modeling study
Artificial intelligence-generated content (AIGC) is increasingly embedded in short-video platforms, where algorithmic recommendation systems expose users to high volumes of rapidly produced synthetic media. Although prior research has examined generative AI from the perspectives of creativity, productivity, and technology adoption, less is known about how ordinary users evaluate repeated exposure to AI-generated short videos. This study develops and tests an appraisal-based model of AI content fatigue, distingui…
Among 513 surveyed Chinese short-video users, perceiving AI-generated content as saturating feeds was tied to fatigue, which was linked to lower satisfaction and lower intention to keep watching.
The data come from a cross-sectional survey of Chinese short-video users, so the findings are described as associations and cannot establish causation or change over time.
Evidence
- Peer-reviewedPLOS One2026-10-16
How should this claim be treated?
Truvace Impact Record TRV-2026-1732, v1: “Perceived AIGC saturation and AI content fatigue on short-video platforms: An appraisal-based structural equation modeling study.” Truvace, 2026-10-17. /record/TRV-2026-1732 (accessed at citation time). sha256 96113e3ff0240fd4…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1732 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.