Examining the Impact of Label Detail and Content Stakes on User Perceptions of AI-Generated Images on Social Media
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Abstract (arXiv)
AI-generated images are increasingly prevalent on social media, raising concerns about trust and authenticity. This study investigates how different levels of label detail (basic, moderate, maximum) and content stakes (high vs. low) influence user engagement with and perceptions of AI-generated images through a within-subjects experimental study with 105 participants. Our findings reveal that increasing label detail enhances user perceptions of label transparency but does not affect user engagement. However, content stakes significantly impact user engagement and perceptions, with users demonstrating higher engagement and trust in low-stakes images. These results suggest that social media platforms can adopt detailed labels to improve transparency without compromising user engagement, offering insights for effective labeling strategies for AI-generated content.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Oct 2025.
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.
- 2026Full Disclosure, Less Trust? How the Level of Detail about AI Use in News Writing Affects Readers' Trustcited by 1
- 2026Towards Gaze-Informed AI Disclosure Interfaces: Eye-Tracking Attentional and Cognitive Load While Reading AI-Assisted Newscited by 1
- 2025User Negotiations of Authenticity, Ownership, and Governance on AI-Generated Video Platforms: Evidence from Soracited by 0
- 2026Towards Gaze-Informed AI Disclosure Interfaces: Eye-Tracking Attentional and Cognitive Load While Reading AI-Assisted News14 May 2026
- 2026Full Disclosure, Less Trust? How the Level of Detail about AI Use in News Writing Affects Readers' Trust14 Jan 2026
- 2025User Negotiations of Authenticity, Ownership, and Governance on AI-Generated Video Platforms: Evidence from Sora5 Dec 2025