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

TL;DR

This study explores how label detail and content stakes influence user perceptions and engagement with AI-generated images on social media, revealing that detailed labels improve transparency perceptions while content stakes affect trust and engagement.

Contribution

It provides new insights into how label detail and content stakes impact user perceptions and engagement with AI-generated images, informing better labeling strategies.

Findings

Increased label detail improves perceived transparency.

Content stakes significantly influence user engagement and trust.

Detailed labels do not reduce user engagement.

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.