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Haeseung Seo

Publications and source records attributed to Haeseung Seo.

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Partisan Fact-Checkers' Warnings Can Effectively Correct Individuals' Misbeliefs About Political Misinformation

Political misinformation, particularly harmful when it aligns with individuals' preexisting beliefs and political ideologies, has become widespread on social media platforms. In response, platforms like Facebook and X introduced warning messages leveraging fact-checking results from third-party fact-checkers to alert users against false content. However, concerns persist about the effectiveness of these fact-checks, especially when fact-checkers are perceived as politically biased. To address these concerns, this study presents findings from an online human-subject experiment (N=216) investigating how the political stances of fact-checkers influence their effectiveness in correcting misbeliefs about political misinformation. Our findings demonstrate that partisan fact-checkers can decrease the perceived accuracy of political misinformation and correct misbeliefs without triggering backfire effects. This correction is even more pronounced when the misinformation aligns with individuals' political ideologies. Notably, while previous research suggests that fact-checking warnings are less effective for conservatives than liberals, our results suggest that explicitly labeled partisan fact-checkers, positioned as political counterparts to conservatives, are particularly effective in reducing conservatives' misbeliefs toward pro-liberal misinformation.

cs.HC

Fakes of Varying Shades: How Warning Affects Human Perception and Engagement Regarding LLM Hallucinations

The widespread adoption and transformative effects of large language models (LLMs) have sparked concerns regarding their capacity to produce inaccurate and fictitious content, referred to as `hallucinations'. Given the potential risks associated with hallucinations, humans should be able to identify them. This research aims to understand the human perception of LLM hallucinations by systematically varying the degree of hallucination (genuine, minor hallucination, major hallucination) and examining its interaction with warning (i.e., a warning of potential inaccuracies: absent vs. present). Participants (N=419) from Prolific rated the perceived accuracy and engaged with content (e.g., like, dislike, share) in a Q/A format. Participants ranked content as truthful in the order of genuine, minor hallucination, and major hallucination, and user engagement behaviors mirrored this pattern. More importantly, we observed that warning improved the detection of hallucination without significantly affecting the perceived truthfulness of genuine content. We conclude by offering insights for future tools to aid human detection of hallucinations. All survey materials, demographic questions, and post-session questions are available at: https://github.com/MahjabinNahar/fakes-of-varying-shades-survey-materials

cs.HC