论文标题
使用新颖的Twitter数据集来表征Covid-19的错误信息社区
Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset
论文作者
论文摘要
从阴谋理论到假治愈和假治疗，Covid-19已成为在线传播的热门床。确定在线揭穿和纠正虚假信息的方法比以往任何时候都重要。在本文中，我们提出了一种方法和分析，以在线表征这两个竞争性的Covid-19错误信息社区：（i）误解了积极发布错误信息的用户或用户，以及（ii）主动传播真实信息或呼吁错误信息的知情用户或用户。这项研究的目标是两个方面：（i）收集一组带注释的Covid-19-Twitter数据集，研究界可以将其用于进行有意义的分析； （ii）以网络结构，语言模式及其在其他社区的成员身份来表征两个目标社区。我们的分析表明，Covid-19错误信息的社区比知情社区更加密集，并且更有条理，可能会出现大量的错误信息是虚假信息运动的一部分。我们的分析还表明，绝大多数错误的用户可能是反vaxxers。最后，我们的社会语言分析表明，与误导用户相比，Covid-19知情用户倾向于使用更多的叙述。
From conspiracy theories to fake cures and fake treatments, COVID-19 has become a hot-bed for the spread of misinformation online. It is more important than ever to identify methods to debunk and correct false information online. In this paper, we present a methodology and analyses to characterize the two competing COVID-19 misinformation communities online: (i) misinformed users or users who are actively posting misinformation, and (ii) informed users or users who are actively spreading true information, or calling out misinformation. The goals of this study are two-fold: (i) collecting a diverse set of annotated COVID-19 Twitter dataset that can be used by the research community to conduct meaningful analysis; and (ii) characterizing the two target communities in terms of their network structure, linguistic patterns, and their membership in other communities. Our analyses show that COVID-19 misinformed communities are denser, and more organized than informed communities, with a possibility of a high volume of the misinformation being part of disinformation campaigns. Our analyses also suggest that a large majority of misinformed users may be anti-vaxxers. Finally, our sociolinguistic analyses suggest that COVID-19 informed users tend to use more narratives than misinformed users.
