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텍스트 마이닝과 기계 학습을 이용한 국내 가짜뉴스 예측
Fake News Detection for Korean News Using Text Mining and Machine Learning Techniques

첫 페이지 보기
  • 발행기관
    한국정보기술응용학회 바로가기
  • 간행물
    JITAM 바로가기
  • 통권
    Vol.25 No.1 (2018.03)바로가기
  • 페이지
    pp.19-32
  • 저자
    윤태욱, 안현철
  • 언어
    한국어(KOR)
  • URL
    https://www.earticle.net/Article/A340683

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원문정보

초록

영어
Fake news is defined as the news articles that are intentionally and verifiably false, and could mislead readers. Spread of fake news may provoke anxiety, chaos, fear, or irrational decisions of the public. Thus, detecting fake news and preventing its spread has become very important issue in our society. However, due to the huge amount of fake news produced every day, it is almost impossible to identify it by a human. Under this context, researchers have tried to develop automated fake news detection method using Artificial Intelligence techniques over the past years. But, unfortunately, there have been no prior studies proposed an automated fake news detection method for Korean news. In this study, we aim to detect Korean fake news using text mining and machine learning techniques. Our proposed method consists of two steps. In the first step, the news contents to be analyzed is convert to quantified values using various text mining techniques (Topic Modeling, TF-IDF, and so on). After that, in step 2, classifiers are trained using the values produced in step 1. As the classifiers, machine learning techniques such as multiple discriminant analysis, case based reasoning, artificial neural networks, and support vector machine can be applied. To validate the effectiveness of the proposed method, we collected 200 Korean news from Seoul National University’s FactCheck (http://factcheck.snu.ac.kr). which provides with detailed analysis reports from about 20 media outlets and links to source documents for each case. Using this dataset, we will identify which text features are important as well as which classifiers are effective in detecting Korean fake news.

목차

Abstract
1. 서론
2. 연구 배경
2.1 가짜뉴스
2.2 자동화된 가짜뉴스 탐지 방법론
3. 제안 방법론
4. 실증 분석
4.1 실험 데이터
4.2 실험 설계
4.3 실험 결과
5. 결론
References

키워드

Fake News Detection Korean News Machine Learning Text Mining

저자

  • 윤태욱 [ Tae-Uk Yun | Master’s Candidate, Graduate School of Business IT, Kookmin University ]
  • 안현철 [ Hyunchul Ahn | Associate Professor, Graduate School of Business IT, Kookmin University ] Corresponding author

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    한국정보기술응용학회 [The Korea Society of Information Technology Applications]
  • 설립연도
    1999
  • 분야
    사회과학>경영학
  • 소개
    본 학회는 정보기술 관련 분야의 연구 및 교류를 촉진하여 국가 및 기업정보화 발전에 공헌함을 그 목적으로 한다.

간행물

  • 간행물명
    JITAM [Journal of Information Technology Applications and Management]
  • 간기
    격월간
  • pISSN
    1598-6284
  • eISSN
    2508-1209
  • 수록기간
    1999~2026
  • 십진분류
    KDC 005 DDC 005

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