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http://dx.doi.org/10.14400/JDC.2019.17.5.137

A Comparative Study of Text analysis and Network embedding Methods for Effective Fake News Detection  

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)
Publication Information
Journal of Digital Convergence / v.17, no.5, 2019 , pp. 137-143 More about this Journal
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.
Keywords
Fake news detection; Text analysis; Network embedding; DeepWalk; News diffusion network;
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