WARNING This Contains Misinformation: The Effect of Cognitive Factors, Beliefs, and Personality on Misinformation Warning Tag Attitudes
Robert Kaufman, Aaron Broukhim, Michael Haupt
摘要
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. However, past warning tag mitigation studies yield mixed results for diverse audiences. 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 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. With all participants combined, we find attitudes towards warning tags and self-described behaviors are significantly influenced by factors such as Need for Cognitive Closure (NFCC), Political orientation, and Trust in Medical Scientists when controlled for covariates such as age and recruiting platform. Analyses of each sample further show that tag attitudes were influenced by Trust in Religious Leaders, and Big Five Inventory (BFI) traits for Openness and Conscientiousness. We synthesize these results into design insights and a future research agenda for more effective and personalized warning tags and misinformation mitigation strategies more generally.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper1
问问它们各自怎么用它它引用的顶会 Paper5
- Disproportionate Removals and Differing Content Moderation Experiences for Conservative, Transgender, and Black Social Media Users: Marginalization and Moderation Gray AreasOliver L. Haimson, Daniel Delmonaco, Peipei Nie, Andrea WegnerCSCW 2021 · 被引用 287 次
- Human-AI Collaboration via Conditional Delegation: A Case Study of Content ModerationVivian Lai, Samuel Carton, Rajat Bhatnagar, Q. Vera Liao 等CHI 2022 · 被引用 135 次
- Understanding Effects of Algorithmic vs. Community Label on Perceived Accuracy of Hyper-partisan MisinformationChenyan Jia, Alexander Boltz, Angie Zhang, Anqing Chen 等CSCW 2022 · 被引用 47 次
- Who's in the Crowd Matters: Cognitive Factors and Beliefs Predict Misinformation Assessment AccuracyRobert A. Kaufman, Michael Robert Haupt, Steven P. DowCSCW 2022 · 被引用 27 次
- Can The Crowd Identify Misinformation Objectively?: The Effects of Judgment Scale and Assessor's BackgroundKevin Roitero, Michael Soprano, Shaoyang Fan, Damiano Spina 等SIGIR 2020 · 被引用 2 次
相关 Paper
- Countering Fake News: A Comparison of Possible Solutions Regarding User Acceptance and EffectivenessJan Kirchner, Christian ReuterCSCW 2020 · 被引用 89 次
- Remove, Reduce, Inform: What Actions do People Want Social Media Platforms to Take on Potentially Misleading Content?Shubham Atreja, Libby Hemphill, Paul ResnickCSCW 2023 · 被引用 22 次
- Seeing is Not Believing: A Nuanced View of Misinformation Warning Efficacy on Video-Sharing Social Media PlatformsChen Guo, Nan Zheng, Chengqi (John) GuoCSCW 2023 · 被引用 27 次
- Who Does Not Benefit from Fact-checking Websites?: A Psychological Characteristic Predicts the Selective Avoidance of Clicking Uncongenial FactsYuko Tanaka, Miwa Inuzuka, Hiromi Arai, Yoichi Takahashi 等CHI 2023 · 被引用 4 次
- Mitigating Misinformation Sharing on Social Media through Personalised NudgingTom Biselli, Katrin Hartwig, Christian ReuterCSCW 2025 · 被引用 6 次
