Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
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Research Analysis in Automatic Fake News Detection

자동화기반의 가짜 뉴스 탐지를 위한 연구 분석

  • 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)
  • 좌희정 (고려대학교 컴퓨터학과) ;
  • 오동석 (고려대학교 Human-inspired 복합지능 연구센터) ;
  • 임희석 (고려대학교 컴퓨터학과)
  • Received : 2019.05.20
  • Accepted : 2019.07.20
  • Published : 2019.07.28
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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.

가짜 정보를 탐지하기 위한 연구는 2016년 미국 대통령 선거 이후 본격적으로 시작되었다. 정확한 출처를 알 수 없는 정보들이 뉴스 형식으로 생산되고, 이는 자극적이고 흥미로운 소재에 많은 관심을 보이는 대중의 특성에 따라 빠른 속도로 확산되고 있다. 또한, 소셜 네트워크 서비스 등 정보를 전달하기 쉬운 플랫폼의 대중화는 이러한 현상을 더욱 악화시킨다. Poynter는 IFCN(International Fact Checking Network)를 만들어 숙련된 전문가들이 사실 여부를 판단할 수 있는 가이드라인을 제시하고, 팩트 체크 기관을 위한 강령을 제공하고 있다. 하지만 이러한 접근 방법은 하나의 기사에 대한 진위 여부를 검증하기 위해 다수의 전문가 인력이 투입되어야 하므로 시간 및 금전적 비용이 크다. 따라서 지속적으로 증가하는 가짜 뉴스에 효율적으로 대응할 수 있는 자동화된 가짜 뉴스 탐지 기술에 대한 연구가 주목받고 있다. 본 논문에서는 최근 딥러닝 기술의 접목으로 인해 빠르게 발전하고 있는 가짜 뉴스 탐지 시스템과 연구들을 정리 및 분석한다. 또한, 많은 연구가 필요한 본 분야에 연구자들이 쉽게 접근할 수 있도록 다양한 형태로 주어지는 학습 말뭉치 및 챌린지들도 정리한다.

Keywords

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Fig. 1. Reuters Institute Digital News Report 2018

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Fig. 2. Fake Information

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Fig. 3. Fake news Paper Type

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Fig. 4. Fake news Paper Dataset

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Fig. 5. Fakenews Paper Language

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Fig. 6. Fake news Paper Method

Table 1. Summary of works

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