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http://dx.doi.org/10.3745/KTSDE.2022.11.4.149

CoAID+ : COVID-19 News Cascade Dataset for Social Context Based Fake News Detection  

Han, Soeun (한양대학교 컴퓨터소프트웨어학과)
Kang, Yoonsuk (한양대학교 컴퓨테이셔널 사회과학연구센터)
Ko, Yunyong (한양대학교 인공지능 혁신인재교육 연구단)
Ahn, Jeewon (한양대학교 컴퓨터소프트웨어학과)
Kim, Yushim (Arizona State University 행정학과)
Oh, Seongsoo (한양대학교 행정학과)
Park, Heejin (한양대학교 정보통신학부)
Kim, Sang-Wook (한양대학교 정보통신학부)
Publication Information
KIPS Transactions on Software and Data Engineering / v.11, no.4, 2022 , pp. 149-156 More about this Journal
Abstract
In the current COVID-19 pandemic, fake news and misinformation related to COVID-19 have been causing serious confusion in our society. To accurately detect such fake news, social context-based methods have been widely studied in the literature. They detect fake news based on the social context that indicates how a news article is propagated over social media (e.g., Twitter). Most existing COVID-19 related datasets gathered for fake news detection, however, contain only the news content information, but not its social context information. In this case, the social context-based detection methods cannot be applied, which could be a big obstacle in the fake news detection research. To address this issue, in this work, we collect from Twitter the social context information based on CoAID, which is a COVID-19 news content dataset built for fake news detection, thereby building CoAID+ that includes both the news content information and its social context information. The CoAID+ dataset can be utilized in a variety of methods for social context-based fake news detection, thus would help revitalize the fake news detection research area. Finally, through a comprehensive analysis of the CoAID+ dataset in various perspectives, we present some interesting features capable of differentiating real and fake news.
Keywords
Fake News Detection; Propagation; Coronavirus; Social Context Based Detection;
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