Aligned content and social explanations improve misinformation detection accuracy, while misaligned explanations offer no accuracy benefit over no explanation.
Does the Source of a Warning Matter? Examining the Effectiveness of Veracity Warning Labels Across Warners
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
In this study, we conducted an online, between-subjects experiment (N = 2,049) to better understand the impact of warning label sources on information trust and sharing intentions. Across four warners (the social media platform, other social media users, Artificial Intelligence (AI), and fact checkers), we found that all significantly decreased trust in false information relative to control, but warnings from AI were modestly more effective. All warners significantly decreased the sharing intentions of false information, except warnings from other social media users. AI was again the most effective. These results were moderated by prior trust in media and the information itself. Most noteworthy, we found that warning labels from AI were significantly more effective than all other warning labels for participants who reported a low trust in news organizations, while warnings from AI were no more effective than any other warning label for participants who reported a high trust in news organizations.
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Designing Effective AI Explanations for Misinformation Detection: A Comparative Study of Content, Social, and Combined Explanations
Aligned content and social explanations improve misinformation detection accuracy, while misaligned explanations offer no accuracy benefit over no explanation.