IEEE Transactions on Knowledge and Data Engineering
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. Then for evaluation and a numerical study, we apply our
inference technique to 119 million election-related tweets collected in seven months during the 2012 U.S. presidential election
campaign. Our technique achieves 94% accuracy and high rank correlation as compared with manually created labels. By studying the
political leaning of 1,000 frequently retweeted sources, 230,000 ordinary users who retweeted them, and the hashtags used by these
sources, our numerical study sheds light on the political demographics of the Twitter population, and the temporal dynamics of political
polarization as events unfold.
Wong, Felix MFW; Tan, Chee Wei; Sen, Soumya; Chiang, Mung.
Quantifying Political Leaning from Tweets, Retweets, and Retweeters.
IEEE Transactions on Knowledge and Data Engineering.
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