Soeun Han, Yoonsuk Kang, Yunyong Ko, Jeewon Ahn, Yushim Kim, Seongsoo Oh, Heejin Park, Sang-Wook Kim

KIPS Transactions on Software and Data Engineering, Vol. 11, No. 4, pp. 149-156, Apr. 2022
https://doi.org/10.3745/KTSDE.2022.11.4.149,   PDF Download:
Keywords: Fake news detection, propagation, Coronavirus, Social Context Based Detection
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.

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