"This is Fake News": Characterizing the Spontaneous Debunking from Twitter Users to COVID-19 False Information
"This is Fake News": Characterizing the Spontaneous Debunking from Twitter Users to COVID-19 False Information
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DOI：
10.48550/arxiv.2203.14242
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
K. Miyazaki;T. Uchiba;Kenji Tanaka;Jisun An;Haewoon Kwak;K. Sasahara
中科院分区：
文献类型：
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作者：
K. Miyazaki;T. Uchiba;Kenji Tanaka;Jisun An;Haewoon Kwak;K. Sasahara
虚假信息在社交媒体上传播，事实核查是一种潜在的对策。然而，事实核查人员严重短缺；迫切需要一种有效的方式来扩大事实核查的规模，特别是在新冠肺炎这样的流行病中。在这项研究中，我们关注的是社交媒体用户的自发揭穿，这一点在现有研究中一直被忽视，尽管它被证明对事实核查和打击虚假信息是有用的。具体地说，我们将这些推文定性为虚假信息或虚假推文，这些推文往往被揭穿，而Twitter用户往往会揭穿虚假推文。对于这个分析，我们创建了一个对虚假推文的回复的全面数据集，对其中的一个子集进行了注释，并建立了一个用于检测揭穿行为的分类模型。我们发现，大多数虚假推文都没有被揭穿，自发揭穿的速度比其他形式的回复慢，而且自发揭穿在政治话题中表现出党派色彩。这些结果为利用自发揭穿来扩大常规事实核查提供了可操作的见解，从而从一个新的角度补充了现有的研究。
False information spreads on social media, and fact-checking is a potential countermeasure. However, there is a severe shortage of fact-checkers; an efficient way to scale fact-checking is desperately needed, especially in pandemics like COVID-19. In this study, we focus on spontaneous debunking by social media users, which has been missed in existing research despite its indicated usefulness for fact-checking and countering false information. Specifically, we characterize the tweets with false information, or fake tweets, that tend to be debunked and Twitter users who often debunk fake tweets. For this analysis, we create a comprehensive dataset of responses to fake tweets, annotate a subset of them, and build a classification model for detecting debunking behaviors. We find that most fake tweets are left undebunked, spontaneous debunking is slower than other forms of responses, and spontaneous debunking exhibits partisanship in political topics. These results provide actionable insights into utilizing spontaneous debunking to scale conventional fact-checking, thereby supplementing existing research from a new perspective.