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Human-made vs. AI-generated: how provenance labels drive strategic curation via perceived effort
Han Sol Lim1, Bong Gyou Lee1, Yoon Hi Sung2
1Graduate School of Information, Yonsei University, Seoul, Republic of Korea.
Frontiers in Psychology
|June 19, 2026
Summary
Content provenance labels on short-form video platforms impact user perceptions. AI-generated labels reduce perceived creator effort, undermining user intervention in algorithmic curation, unlike human-made labels.
Area of Science:
- Human-Computer Interaction
- Social Psychology
- Digital Media Studies
Background:
- Provenance labels on short-form video platforms are often viewed as risk disclosures.
- This study reframes provenance labels as value signals, examining their psychological impact on users.
- Investigates how attribution cues influence beliefs about algorithmic curation and user behavior.
Purpose of the Study:
- To examine how content provenance labeling affects perceived creator effort, algorithmic curation efficacy, and strategic curation intention.
- To test the dual-pathway mechanism linking perceived effort to strategic curation.
- To understand the psychological mechanisms behind user responses to AI-generated versus human-made content labels.
Main Methods:
- A between-subjects experiment with 618 short-form video users.
- A 3 (label type: human-made, AI-generated, unlabeled) × 2 (content type: eudaimonic, hedonic) factorial design.
- Measured perceived effort, algorithmic curation efficacy, and strategic curation intention.
Main Results:
- AI-generated labels significantly reduced perceived creator effort compared to unlabeled content.
- Human-made labels did not differ from unlabeled conditions, suggesting an implicit human-made default.
- Perceived effort influenced strategic curation intentions, but AI labels weakened this effect, reducing user intervention.
- Effort devaluation from AI labeling was context-independent.
Conclusions:
- AI content labeling can inadvertently devalue human creativity and effort, leading to reduced user engagement with algorithmic curation.
- Future labeling policies should prioritize human-centered value certification over passive risk disclosure.
- User agency in algorithmic systems may depend more on efficacy beliefs than technical knowledge.
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