Characterizing AI-Generated Misinformation on Social Media

AI-generated misinformation (e.g., deepfakes) poses a growing threat to information integrity on social media. However, prior research has largely focused on its potential societal consequences rather than its real-world prevalence. In this study, we conduct a large-scale empirical analysis of AI-generated misinformation on the social media platform X. Specifically, we analyze a dataset comprising 82,076 misleading posts, both AI-generated and non-AI-generated, that have been identified and flagged through X's Community Notes platform. Our analysis yields four main findings: (i) AI-generated misinformation is more often centered on entertaining content and tends to exhibit a more positive sentiment than conventional forms of misinformation, (ii) it is perceived as less believable and less harmful than conventional misinformation, (iii) it more often originates from smaller user accounts, while authors posting such content are also associated with higher levels of partisanship and misinformation exposure, and (iv) AI-generated misinformation is significantly more likely to go viral. Altogether, our findings highlight the unique characteristics of AI-generated misinformation on social media and offer important implications for platforms and future research.