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Research Analysis in Automatic Fake News Detection
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자동화기반의 가짜 뉴스 탐지를 위한 연구 분석

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Type
Academic journal
Author
Journal
korea convergence society 한국융합학회논문지 한국융합학회논문지 제10권 제7호 KCI Accredited Journals
Published
2019.1
Pages
15 - 21 (7page)

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Research Analysis in Automatic Fake News Detection
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Abstract· Keywords

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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.
#Fake news #Fake Information #Fake News Challenge #Maching Learning #Deep Learning

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