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http://dx.doi.org/10.15207/JKCS.2019.10.7.015

Research Analysis in Automatic Fake News Detection  

Jwa, Hee-Jung (Dept. of Computer Science and Engineering, Korea University)
Oh, Dong-Suk (Human-inspired AI & Computing Research Center, Korea University)
Lim, Heui-Seok (Dept. of Computer Science and Engineering, Korea University)
Publication Information
Journal of the Korea Convergence Society / v.10, no.7, 2019 , pp. 15-21 More about this Journal
Abstract
Research in detecting fake information gained a lot of interest after the US presidential election in 2016. Information from unknown sources are produced in the shape of news, and its rapid spread is fueled by the interest of public drawn to stimulating and interesting issues. In addition, the wide use of mass communication platforms such as social network services makes this phenomenon worse. Poynter Institute created the International Fact Checking Network (IFCN) to provide guidelines for judging the facts of skilled professionals and releasing "Code of Ethics" for fact check agencies. However, this type of approach is costly because of the large number of experts required to test authenticity of each article. Therefore, research in automated fake news detection technology that can efficiently identify it is gaining more attention. In this paper, we investigate fake news detection systems and researches that are rapidly developing, mainly thanks to recent advances in deep learning technology. In addition, we also organize shared tasks and training corpus that are released in various forms, so that researchers can easily participate in this field, which deserves a lot of research effort.
Keywords
Fake news; Fake Information; Fake News Challenge; Maching Learning; Deep Learning;
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