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Does the Source of a Warning Matter? Examining the Effectiveness of Veracity Warning Labels Across Warners

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arxiv 2407.21592 v1 pith:SAVIUG3F submitted 2024-07-31 cs.CY cs.HCcs.SI

Does the Source of a Warning Matter? Examining the Effectiveness of Veracity Warning Labels Across Warners

classification cs.CY cs.HCcs.SI
keywords warningtrusteffectiveinformationmediaotherwerelabels
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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Cited by 1 Pith paper

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  1. On the Effectiveness of Fact Checking Information from Politically Congruent and Incongruent Large Language Models

    cs.CY 2026-07 conditional novelty 6.0

    LLM fact-checkers shift trust in political headlines across partisan lines, with perceived chatbot politics mattering only for politically distant true headlines.