Predicting Political Orientation of News Articles Based on User Behavior Analysis in Social Network

Abstract

SUMMARY News articles usually represent a biased viewpoint on contentious issues, potentially causing social problems. To mitigate this media bias, we propose a novel framework for predicting orientation of a news article by analyzing social user behaviors in Twitter. Highly active users tend to have consistent behavior patterns in social network by retweeting behavior among users with the same viewpoints for contentious issues. The bias ratio of highly active users is measured to predict orientation of users. Then political orientation of a news article is predicted based on the bias ratio of users, mutual retweeting and opinion analysis of tweet documents. The analysis of user behavior shows that users with the value of 1 in bias ratio are 88.82%. It indicates that most of users have distinctive orientation. Our prediction method based on orientation of users achieved 88.6% performance in accuracy. Experimental results show significant improvements over the SVM classification. These results show that proposed detection