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Ideology Detection for Twitter Users via Link Analysis

  • Yupeng GuEmail author
  • Ting Chen
  • Yizhou Sun
  • Bingyu Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10354)

Abstract

The problem of ideology detection is to study the latent (political) placement for people, which is traditionally studied on politicians according to their voting behaviors. Recently, more and more studies begin to address the ideology detection problem for ordinary users based on their online behaviors that can be captured by social media, e.g., Twitter. As far as we are concerned, the vast majority of the existing methods on ideology detection on social media have oversimplified the problem as a binary classification problem (i.e., liberal vs. conservative). Moreover, though social links can play a critical role in deciding one’s ideology, most of the existing work ignores the heterogeneous types of links in social media. In this paper we propose to detect numerical ideology positions for Twitter users, according to their follow, mention, and retweet links to a selected set of politicians. A unified probabilistic model is proposed that can (1) integrate heterogeneous types of links together in determining people’s ideology, and (2) automatically learn the quality of each type of links in deciding one’s ideology. Experiments have demonstrated the advantages of our model in terms of both ranking and political leaning classification accuracy.

Keywords

Ideal Point Link Type Twitter User Binary Classification Problem Party Affiliation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of California Los AngelesLos AngelesUSA
  2. 2.Northeastern UniversityBostonUSA

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