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On a set of frequently retweeted sources, our technique achieves 94 percent accuracy and high rank correlation as compared with manually created labels. We then apply our inference technique to 119 million election-related tweets collected in seven months during the 2012 U.S.

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. In this paper, we study the problem of quantifying and inferring the political leaning of Twitter users.

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.

We find the Twitter alignments in the political Right were more nuanced than those on the political Left and discuss implications of this behavior in relation to the rise of the Tea Party during the 2010 elections. Going beyond discourses of fragmentation and polarization, certain clusters engaged in strategic expression such as “retweeting” (i.e., sharing someone else’s tweet with one’s followers) and “hashjacking” (i.e., co-opting the hashtags preferred by political adversaries). We find that Twitter users in that election cycle did not align in a simple Right-Left division rather, five unique clusters emerged within Twitter networks, three of them representing different conservative groupings. House and Senate and governorships in 2010.

We examine the political alignments and networking of Twitter users, analyzing 9 million tweets produced by more than 23,000 randomly selected followers of candidates for the U.S. Twitter provides a direct method for political actors to connect with citizens, and for those citizens to organize into online clusters through their use of hashtags (i.e., a word or phrase marked with # to identify an idea or topic and facilitate a search for it).

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