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WARNING This Contains Misinformation: The Effect of Cognitive Factors, Beliefs, and Personality on Misinformation Warning Tag Attitudes

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arxiv 2407.02710 v1 pith:HWUHSB6Q submitted 2024-07-02 cs.HC cs.SI

classification cs.HCcs.SI
keywords warningmisinformationtagsattitudesbehaviorscognitivecontentmitigation
verification ladder T0 review T1 audit T2 compute T3 formal

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Social media platforms enhance the propagation of online misinformation by providing large user bases with a quick means to share content. One way to disrupt the rapid dissemination of misinformation at scale is through warning tags, which label content as potentially false or misleading. Past warning tag mitigation studies yield mixed results for diverse audiences, however. We hypothesize that personalizing warning tags to the individual characteristics of their diverse users may enhance mitigation effectiveness. To reach the goal of personalization, we need to understand how people differ and how those differences predict a person's attitudes and self-described behaviors toward tags and tagged content. In this study, we leverage Amazon Mechanical Turk (n = 132) and undergraduate students (n = 112) to provide this foundational understanding. Specifically, we find attitudes towards warning tags and self-described behaviors are positively influenced by factors such as Personality Openness and Agreeableness, Need for Cognitive Closure (NFCC), Cognitive Reflection Test (CRT) score, and Trust in Medical Scientists. Conversely, Trust in Religious Leaders, Conscientiousness, and political conservatism were negatively correlated with these attitudes and behaviors. We synthesize our results into design insights and a future research agenda for more effective and personalized misinformation warning tags and misinformation mitigation strategies more generally.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Improving Human-Autonomous Vehicle Interaction in Complex Systems

    cs.HC 2025-04 conditional novelty 6.0 of 10

    Human-AV communication should adapt to context, rider traits, and goals, as shown by two experiments and a machine-learning trust prediction study.

  2. Evaluation Metrics for Misinformation Warning Interventions: Challenges and Prospects

    cs.HC 2025-05 conditional novelty 4.0 of 10

    A systematic review classifying misinformation warning evaluation metrics into behavioral, trust, usability, and cognitive/psychological categories, and highlighting standardization challenges.

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