• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.513-514
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• 2018

우리 정부는 신기술인 지능정보기술의 효과 실현을 극대화하고, 선진 기술 확보를 가속화하기 위해 기존 추격형을 넘어선 선도형 R&D 지원체계가 필수적인 상황을 인지하고 있다. 특히, 미국 등 선진국에서는 신기술 분야에 대한 효과 극대화와 R&D 촉진을 위해 도전 경쟁형 R&D 지원체계를 활용한 R&D 추진을 활발히 지원하고 있다. 세계적으로 첨단 미개척 인공지능 분야의 연구 진작을 위해 우리정부는 지난 '17년부터 도전형 경쟁형 개방형 R&D 지원 체계인 '인공지능 R&D 챌린지' 도입하여 지원하고 있다. 이에 본 논문에서는 인공지능 R&D 챌린지의 가짜뉴스 찾기를 통해 국내 R&D의 지원방향에 대해 검토해 보았다.

• The Journal of Society for e-Business Studies
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• v.23 no.4
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• pp.19-39
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• 2018

Recently, fake news has attracted worldwide attentions regardless of the fields. The Hyundai Research Institute estimated that the amount of fake news damage reached about 30.9 trillion won per year. The government is making efforts to develop artificial intelligence source technology to detect fake news such as holding "artificial intelligence R&D challenge" competition on the title of "searching for fake news." Fact checking services are also being provided in various private sector fields. Nevertheless, in academic fields, there are also many attempts have been conducted in detecting the fake news. Typically, there are different attempts in detecting fake news such as expert-based, collective intelligence-based, artificial intelligence-based, and semantic-based. However, the more accurate the fake news manipulation is, the more difficult it is to identify the authenticity of the news by analyzing the news itself. Furthermore, the accuracy of most fake news detection models tends to be overestimated. Therefore, in this study, we first propose a method to secure the fairness of false news detection model accuracy. Secondly, we propose a method to identify the authenticity of the news using the social data broadly generated by the reaction to the news as well as the contents of the news.

• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.15-21
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• 2019

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.