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.

针对 COVID-19 在线信息误导问题，本文分析并对比了在 Twitter 上两个竞争性的 COVID-19 信息社区：误导用户和知情用户，并通过网络结构分析、语言模式等方面对他们进行了表征和分类，发现误导社区较为紧密、有组织，并可能存在大量的虚假信息宣传活动，其中大部分误导用户可能是反对疫苗接种者。

利用新型 Twitter 数据集表征 COVID-19 虚假信息社群

Characterizing COVID-19 Misinformation Communities Using a Novel Twitter  Dataset

In contrast to many decades of research on oral code-switching, the study of
written multilingual productions has only recently enjoyed a surge of interest.
Many open questions remain regarding the sociolinguistic underpinnings of
written code-switching, and progress has been limited by a lack of suitable
resources. We introduce a novel, large, and diverse dataset of written
code-switched productions, curated from topical threads of multiple bilingual
communities on the Reddit discussion platform, and explore questions that were
mainly addressed in the context of spoken language thus far. We investigate
whether findings in oral code-switching concerning content and style, as well
as speaker proficiency, are carried over into written code-switching in
discussion forums. The released dataset can further facilitate a range of
research and practical activities.

该研究介绍了一种新的、大规模和多样化的数据集，这个数据集是从 Reddit 的多个双语社区的主题帖中精选而来的书面混合语言产品，探讨了至今主要在口语语言背景下讨论的内容和风格、发言者的熟练程度等问题是否也能适用于书面语混合，该数据集可以进一步促进一系列研究和实际活动。