Drivers of the Virality of COVID‐19 Misinformation Sharing on Social Media

Yuehua Zhao, Jingwei Da, Jiaqi Yan, Hao Wang, Sanhong Deng, Ye Chen
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Abstract

ABSTRACT During global health crises, identifying the key factors of the misinformation dissemination process on social media can provide decision support for public health management. Drawing on the elaboration likelihood model (ELM), this study characterizes the effects of content types and social capital of social media users on the virality of misinformation related to the COVID‐19 pandemic. We used scale, depth, and width to quantify the extent and structure of the virality of misinformation spreading on social media. The findings reveal that both the social capital of users and the content types have major influences on the dissemination of misinformation. Surprisingly, we discovered that the number of followers a user possesses has a varied influence on the dissemination scale, width, and depth, demonstrating the importance of considering dissemination structure.
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COVID - 19在社交媒体上传播错误信息的驱动因素
在全球卫生危机期间,识别社交媒体上错误信息传播过程的关键因素可以为公共卫生管理提供决策支持。利用细化似然模型(ELM),本研究表征了社交媒体用户的内容类型和社会资本对与COVID - 19大流行相关的错误信息病毒性的影响。我们使用规模、深度和宽度来量化错误信息在社交媒体上传播的病毒式传播的程度和结构。研究发现,用户的社会资本和内容类型对错误信息的传播都有重要影响。令人惊讶的是,我们发现用户拥有的关注者数量对传播规模、宽度和深度有不同的影响,这表明考虑传播结构的重要性。
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来源期刊
Proceedings of the Association for Information Science and Technology
Proceedings of the Association for Information Science and Technology Social Sciences-Library and Information Sciences
CiteScore
1.30
自引率
0.00%
发文量
164
期刊介绍: Information not localized
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