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.

翻译：AI生成图像在社交媒体上日益普遍，引发了关于信任与真实性的担忧。本研究通过一项包含105名参与者的被试内实验，探究了不同标签细节水平（基础、中等、详尽）和内容风险（高 vs. 低）如何影响用户对AI生成图像的参与度与感知。研究发现，增加标签细节能提升用户对标签透明度的感知，但不影响用户参与度。然而，内容风险显著影响用户参与度与感知：用户对低风险图像表现出更高的参与度和信任度。这些结果表明，社交媒体平台可采用详细标签以提升透明度而不损害用户参与度，为AI生成内容的有效标注策略提供了见解。