{"ID":2852428,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.19024","arxiv_id":"2510.19024","title":"Examining the Impact of Label Detail and Content Stakes on User Perceptions of AI-Generated Images on Social Media","abstract":"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.","short_abstract":"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-...","url_abs":"https://arxiv.org/abs/2510.19024","url_pdf":"https://arxiv.org/pdf/2510.19024v1","authors":"[\"Jingruo Chen\",\"TungYen Wang\",\"Marie Williams\",\"Natalia Jordan\",\"Mingyi Shao\",\"Linda Zhang\",\"Susan R. Fussell\"]","published":"2025-10-21T19:06:46Z","proceeding":"cs.HC","tasks":"[\"cs.HC\"]","methods":"[]","has_code":false}