Understanding the Impact of AI Label Disclosure on Engagement with Deepfakes Videos

The rapid proliferation of AI-generated content on digital platforms—particularly deepfakes involving synthetic audio and videos—has intensified concerns about misinformation, content authenticity, and user trust. In response, major platforms have implemented AI labeling policies to enhance transparency and mitigate the societal risks posed by deepfakes. Drawing on the Elaboration Likelihood Model, this study empirically examines the effectiveness of such interventions by analyzing the relationship between video inauthenticity and user engagement, and how AI label disclosure moderates this effect. We analyzed a dataset of 787 TikTok videos, generated by 30 unique content creators between November 12, 2024, and April 14, 2025. We found that video inauthenticity negatively impacts user engagement, such as liking and sharing. The AI label disclosure has a positive moderating role in the negative relationship between video inauthenticity and user engagement. Our research informs policy and design strategies for platform governance in an era increasingly shaped by generative AI.