Follower Behavior Analysis via Influential Transmitters on Social Issues in Twitter

Abstract

A follower can be divided into supporter, non-supporter, or neutral according to a follower’s intention to a target user. Even though a follower is identified as a supporter, an opinion may not be positive to the target user. In this paper, we propose a method to classify a follower as supporter, non-supporter or neutral. To expand information of a follower, influential transmitters who support a target user are detected by using a modified HITS algorithm. To detect a follower’s specific opinion, social issues are extracted based on tweets of influential transmitters. The thread tweets are clustered based on Latent Dirichlet Allocation for social issues. Then, sentiment analysis is conducted for the clusters of a follower. To see the effectiveness of our method, a Korean tweet collection is constructed. As a result, we found that lots of supporting followers show opposite opinions depending on particular issues.