Internet News User Analysis Using Deep Learning and Similarity Comparison

Abstract

Nowadays, many Korean users read news from portal sites like Naver and Daum. Users can comment on news articles on such sites, and some try to influence public opinion through their comments. Therefore, news users need to be analyzed. This study proposes a deep learning method to classify each user’s political stance. Further, a method is developed to evaluate how many similar comments each user writes, and another method is developed to evaluate the similarity of a user’s comments with other users’ comments. We collect approximately 2.68 million comments from hundreds of thousands of political news articles in April 2017. First, for the top 100 news users, we classify each user’s political stance with 92.3% accuracy by using only 20% of data for deep learning training. Second, an evaluation of how many similar comments each user writes reveals that six users score more than 80 points. Third, an evaluation of the similarity of each user’s comments to other users’ comments reveals that 10 users score more than 80 points. Thus, based on this study, it is possible to detect malicious commenters, thereby enhancing comment systems used in news portal websites.