Follower Behavior Analysis via Influential Transmitters on Social Issues in Twitter

Authors

  • Kwang-Yong Jeong CAIIT, Chonbuk National University
  • Kyung-Soon Lee CAIIT, Chonbuk National University

DOI:

https://doi.org/10.13053/cys-20-3-2452

Keywords:

Follower behavior, Influential transmitter, opinion classification, supporting/Non-supporting follower, Social issue.

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.

Author Biographies

Kwang-Yong Jeong, CAIIT, Chonbuk National University

Received his Master degree in computer science and engineering from Chonbuk National University in 2015. His scientific interest is in information retrieval and social data anlaysis.

Kyung-Soon Lee, CAIIT, Chonbuk National University

received her MS and PhD degree in Computer Science from KAIST(Korea Advanced Institute of Science and Technology) in 1997 and 2001, respectively. Her scientific interest is in information retrieval, natural language understanding and text data mining.

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Published

2016-09-30

Issue

Vol. 20 No. 3 (2016): Thematic Issue: Natural Language Processing and Computational Linguistics (Guest Editor: Alexander Gelbukh)

Section

Articles

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