Understanding the health misinformation dissemination on Twitter: The perspective of tweets-comments consistency

Yuchen Zhang, Xiaochuan Zheng, Chuanhui Wu, Yusheng Zhou and Hao Fan

Technology in Society, 2024, vol. 77, issue C

Abstract: This study investigated users' health misinformation dissemination behaviors from the perspective of tweets-comments consistency. Specifically, we proposed a model that explores the effect of information consistency between tweets and comments, i.e., content consistency and sentiment consistency, on users' health misinformation dissemination behavior, along with the moderating effect of degree of falsehood and perceived severity. By analyzing user-generated health misinformation on Twitter, the results suggest that both content consistency and sentiment consistency positively influence individuals' health misinformation dissemination behavior. Degree of falsehood enhances the effect of content consistency but weakens the impact of sentiment consistency. While perceived severity reduces the effect of sentiment consistency, it doesn't significantly affect the connection between content consistency and sharing. By uncovering the roles of tweets-comments consistency, degree of falsehood, and perceived severity, this study enhances our understanding of health misinformation dissemination during health crises and provides guidance for addressing misinformation.

Keywords: Health misinformation dissemination; Tweets-comments consistency; Degree of falsehood; Perceived severity (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:teinso:v:77:y:2024:i:c:s0160791x24000952

DOI: 10.1016/j.techsoc.2024.102547

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