Seeing Should Probably not be Believing: The Role of Deceptive Support in COVID-19 Misinformation on Twitter
Seeing Should Probably not be Believing: The Role of Deceptive Support in COVID-19 Misinformation on Twitter
复制标题
眼见为实：欺骗性支持在 Twitter 上的 COVID-19 错误信息中所扮演的角色
DOI：
10.1145/3546914
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Ray, Indrakshi
中科院分区：
文献类型：
--
作者：
Zuo, Chaoyuan;Banerjee, Ritwik;Shirazi, Hossein;Chaleshtori, Fateme Hashemi;Ray, Indrakshi
随着 SARS-CoV-2 的传播，有关该流行病的大量信息通过 Twitter 等社交媒体平台传播。社交媒体帖子经常利用读者对著名新闻机构的信任，并引用新闻文章作为获得可信度的一种方式。然而，所引用的文章并不总是支持社交媒体帖子中的主张。我们提出了一个跨类型的 hocpipeline 来识别 Twitter 帖子（即“推文”）中的信息是否确实得到引用的新闻文章的支持。我们的方法基于经验，基于超过 4686 万条推文的语料库，分为两个任务：（i）开发模型来检测包含主张和值得进行事实检查的推文，以及（ii）验证推文中提出的主张是否得到其引用的新闻专线文章的支持。与之前通过传播模式的事后分析来检测未经证实的信息的研究不同，我们寻求在错误信息开始传播之前确定可靠的支持（或缺乏可靠的支持）。我们发现近一半的推文 (43.4%) 不真实，因此不值得检查——考虑到 Twitter 等平台上社交媒体帖子的数量巨大，这是一个重要的过滤器。此外，我们发现，在包含看似事实的主张但引用新闻文章作为支持证据的推文中，至少有 1% 的推文实际上并未得到所引用新闻的支持，因此具有误导性。
With the spread of the SARS-CoV-2, enormous amounts of information about the pandemic are disseminated through social media platforms such as Twitter. Social media posts often leverage the trust readers have in prestigious news agencies and cite news articles as a way of gaining credibility. Nevertheless, it is not always the case that the cited article supports the claim made in the social media post. We present a cross-genread hocpipeline to identify whether the information in a Twitter post (i.e., a “Tweet”) is indeed supported by the cited news article. Our approach is empirically based on a corpus of over 46.86 million Tweets and is divided into two tasks: (i) development of models to detect Tweets containing claim and worth to be fact-checked and (ii) verifying whether the claims made in a Tweet are supported by the newswire article it cites. Unlike previous studies that detect unsubstantiated information by post hoc analysis of the patterns of propagation, we seek to identify reliable support (or the lack of it)beforethe misinformation begins to spread. We discover that nearly half of the Tweets (43.4%) are not factual and hence not worth checking—a significant filter, given the sheer volume of social media posts on a platform such as Twitter. Moreover, we find that among the Tweets that contain a seemingly factual claim while citing a news article as supporting evidence, at least 1% are not actually supported by the cited news and are hence misleading.