Labeling Synthetic Content: User Perceptions of Warning Label Designs for AI-generated Content on Social Media
Dilrukshi Gamage, Dilki Sewwandi, Min Zhang, Arosha Bandara
TL;DR
This paper addresses the challenge of informing users about AI-generated content on social media through warning labels. It develops a four-dimensional design space (Label Sentiment, Icon/Color, Position, Detail) and derives ten prototype labels, then empirically tests them in a large, randomized experiment (n=911) to assess effects on belief in AI-generation, trust in the label, and engagement. Key findings show that labels raise belief that content is AI-generated and that trust in labels varies by design, with some designs (notably Content Credentials-based) maximizing trust, while overall engagement with labeled content remains largely unchanged. The study highlights that label design can influence user perceptions and platform credibility, but labels alone are insufficient to dramatically alter engagement, underscoring the need for complementary strategies (education, policy, and provenance standards) to mitigate AI-generated misinformation in social media.
Abstract
In this research, we explored the efficacy of various warning label designs for AI-generated content on social media platforms e.g., deepfakes. We devised and assessed ten distinct label design samples that varied across the dimensions of sentiment, color/iconography, positioning, and level of detail. Our experimental study involved 911 participants randomly assigned to these ten label designs and a control group evaluating social media content. We explored their perceptions relating to 1. Belief in the content being AI-generated, 2. Trust in the labels and 3. Social Media engagement perceptions of the content. The results demonstrate that the presence of labels had a significant effect on the users belief that the content is AI generated, deepfake, or edited by AI. However their trust in the label significantly varied based on the label design. Notably, having labels did not significantly change their engagement behaviors, such as like, comment, and sharing. However, there were significant differences in engagement based on content type: political and entertainment. This investigation contributes to the field of human computer interaction by defining a design space for label implementation and providing empirical support for the strategic use of labels to mitigate the risks associated with synthetically generated media.