The Differential Impact of AI Versus Human Fact-Checkers On News Believability

This paper explores how different types of fact-checkers (i.e., AI or human) impact users’ perception of the believability of news that is flagged as false. Building on source credibility theory, we evaluate how the reputation of the news source and the user's political orientation (i.e., progressive or conservative) moderate the impact of AI versus human fact-checkers. We examined this interaction in two separate 3×2×2 online quasi-experiments conducted in the United States and the United Kingdom. In both studies, we found differences in the impact of fact-checker type and in the moderating effects of poster reputation and user political orientation. Our results show that AI fact-checkers are more effective than human fact-checkers in reducing users’ perceptions of news believability, particularly among progressives. We also found that if the news poster has a high reputation, this can further enhance this impact. By investigating the interplay among fact-checker type, the poster, and users’ political orientation, and by comparing results across two countries, we extend understanding of how different fact-checker types affect news believability regarding false news on social media platforms (and user engagement in robustness checks). Finally, we derive managerial implications for mitigating the spread of false news on social media platforms.