CoAID+: 소셜 컨텍스트 기반 가짜뉴스 탐지를 위한COVID-19 뉴스 파급 데이터

초록

In the current COVID-19 pandemic, fake news and misinformation related to COVID-19 have been causing serious confusion in oursociety. To accurately detect such fake news, social context-based methods have been widely studied in the literature. They detect fakenews based on the social context that indicates how a news article is propagated over social media (e.g., Twitter). Most existing COVID-19related datasets gathered for fake news detection, however, contain only the news content information, but not its social contextinformation. In this case, the social context-based detection methods cannot be applied, which could be a big obstacle in the fake newsdetection research. To address this issue, in this work, we collect from Twitter the social context information based on CoAID, whichis a COVID-19 news content dataset built for fake news detection, thereby building CoAID+ that includes both the news content informationand 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 invarious perspectives, we present some interesting features capable of differentiating real and fake news.

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