Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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A Comparative Study of Text analysis and Network embedding Methods for Effective Fake News Detection

효과적인 가짜 뉴스 탐지를 위한 텍스트 분석과 네트워크 임베딩 방법의 비교 연구

  • Park, Sung Soo (SKK Business School, Sungkyunkwan University) ;
  • Lee, Kun Chang (Global Business Administration/Dept of Health Sciences & & Technology, SAIHST(Samsung Advanced Institute for Health Science & Technology), Sungkyunkwan University)
  • 박성수 (성균관대학교 경영대학) ;
  • 이건창 (성균관대학교 글로벌경영학과/삼성융합의과학원 융합의과학과)
  • Received : 2019.02.21
  • Accepted : 2019.05.20
  • Published : 2019.05.28
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Abstract

Fake news is a form of misinformation that has the advantage of rapid spreading of information on media platforms that users interact with, such as social media. There has been a lot of social problems due to the recent increase in fake news. In this paper, we propose a method to detect such false news. Previous research on fake news detection mainly focused on text analysis. This research focuses on a network where social media news spreads, generates qualities with DeepWalk, a network embedding method, and classifies fake news using logistic regression analysis. We conducted an experiment on fake news detection using 211 news on the Internet and 1.2 million news diffusion network data. The results show that the accuracy of false network detection using network embedding is 10.6% higher than that of text analysis. In addition, fake news detection, which combines text analysis and network embedding, does not show an increase in accuracy over network embedding. The results of this study can be effectively applied to the detection of fake news that organizations spread online.

가짜 뉴스는 소셜 미디어와 같이 사용자가 상호작용하는 미디어 플랫폼에서 정보가 빠른 속도로 확산되는 이점을 가지는 오류 정보(misinformation)의 한 형태이다. 최근 가짜 뉴스의 증가로 인해 사회적으로 많은 문제가 발생하고 있다. 본 논문에서는 이러한 가짜 뉴스를 탐지하는 방법을 제안한다. 이전의 가짜 뉴스 탐지는 텍스트 분석을 사용한 연구가 주로 수행되었다. 본 연구는 소셜 미디어의 뉴스가 확산되는 네트워크에 초점을 두고, 네트워크 임베딩 방법인 DeepWalk 로 자질을 생성하고 로지스틱 회귀분석을 사용하여 가짜 뉴스를 분류한다. 인터넷에 공개된 뉴스 211개와 120만개의 뉴스 확산 네트워크 데이터를 사용한 가짜 뉴스 탐지에 대한 실험을 수행하였다. 연구 결과 텍스트 분석에 비하여 네트워크 임베딩을 사용한 가짜 뉴스 탐지의 정확도가 최소 1.7%에서 최대 10.6% 더 높게 나타났다. 또한, 텍스트 분석과 네트워크 임베딩을 결합한 가짜 뉴스 탐지는 네트워크 임베딩에 비해 정확도의 상승이 나타나지 않았다. 본 연구의 결과는 기업이나 조직은 온라인 상에서 확산되는 가짜 뉴스 탐지에 효과적으로 활용될 수 있다.

Keywords

DJTJBT_2019_v17n5_137_f0001.png 이미지

Fig. 1. DeepWalk Overview

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Fig. 2. Fake news detection procedures that combine text and network embedding methods

Table 1. Statistics for fake news detection data set

DJTJBT_2019_v17n5_137_t0001.png 이미지

Table 2. Fake news detection experiment results using text analysis and graph embedding

DJTJBT_2019_v17n5_137_t0002.png 이미지

Table 3. Fake News Detection Analysis Results Combining Text Analysis and Graph Embedding

DJTJBT_2019_v17n5_137_t0003.png 이미지

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