Quantifying political leaning from tweets, retweets, and retweeters

Felix Ming Fai Wong, Chee Wei Tan, Soumya Sen, Mung Chiang

Research output: Contribution to journalArticle

37 Scopus citations

Abstract

The widespread use of online social networks (OSNs) to disseminate information and exchange opinions, by the general public, news media, and political actors alike, has enabled new avenues of research in computational political science. In this paper, we study the problem of quantifying and inferring the political leaning of Twitter users. We formulate political leaning inference as a convex optimization problem that incorporates two ideas: (a) users are consistent in their actions of tweeting and retweeting about political issues, and (b) similar users tend to be retweeted by similar audience. We then apply our inference technique to 119 million election-related tweets collected in seven months during the 2012 U.S. presidential election campaign. On a set of frequently retweeted sources, our technique achieves 94 percent accuracy and high rank correlation as compared with manually created labels. By studying the political leaning of 1,000 frequently retweeted sources, 232,000 ordinary users who retweeted them, and the hashtags used by these sources, our quantitative study sheds light on the political demographics of the Twitter population, and the temporal dynamics of political polarization as events unfold.

Original languageEnglish (US)
Article number7454756
Pages (from-to)2158-2172
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Volume28
Issue number8
DOIs
StatePublished - Aug 1 2016

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

Keywords

  • Twitter
  • convex programming
  • data analytics
  • inference
  • political science
  • signal processing

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