Examining the Impact of Label Detail and Content Stakes on User Perceptions of AI-Generated Images on Social Media

Fuente: arXiv

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Bibliographic Details
Main Authors:	Chen, Jingruo, Wang, TungYen, Williams, Marie, Jordan, Natalia, Shao, Mingyi, Zhang, Linda, Fussell, Susan R.
Format:	Preprint
Published:	2025
Subjects:	Human-Computer Interaction
Online Access:	Acceder al recurso
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author	Chen, Jingruo Wang, TungYen Williams, Marie Jordan, Natalia Shao, Mingyi Zhang, Linda Fussell, Susan R.
author_facet	Chen, Jingruo Wang, TungYen Williams, Marie Jordan, Natalia Shao, Mingyi Zhang, Linda Fussell, Susan R.
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2510_19024
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Examining the Impact of Label Detail and Content Stakes on User Perceptions of AI-Generated Images on Social Media Chen, Jingruo Wang, TungYen Williams, Marie Jordan, Natalia Shao, Mingyi Zhang, Linda Fussell, Susan R. Human-Computer Interaction 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.
title	Examining the Impact of Label Detail and Content Stakes on User Perceptions of AI-Generated Images on Social Media
topic	Human-Computer Interaction
url	https://arxiv.org/abs/2510.19024