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Birds of the Same Feather Tweet Together. Bayesian Ideal Point Estimation Using Twitter Data
Pablo Barbera
Conference Paper
May 10, 2013

 

Partiescandidates, and voters are becoming increasingly engaged in political
conversations through the micro-blogging platform Twitter. In this paper I show
that the structure of the social networks in which they are embedded has the poten-
tial to become a source of information about policy positions. Under the assumption
that social networks are homophilic (McPherson et al., 2001), this is, the propensity
of users to cluster along partisan lines, I develop a Bayesian Spatial Following model
that scales Twitter users along a common ideological dimension based on who they
follow. I apply this network-based method to estimate ideal points for Twitter users
in the US, the UK, Spain, Italy, and the Netherlands. The resulting positions of the
party accounts on Twitter are highly correlated with oine measures based on their
voting records and their manifestos. Similarly, this method is able to successfully
classify individuals who state their political orientation publicly, and a sample of
users from the state of Ohio whose Twitter accounts are matched with their voter
registration history. To illustrate the potential contribution of these estimates, I
examine the extent to which online behavior is polarized along ideological lines. Us-
ing the 2012 US presidential election campaign as a case study, I nd that public
exchanges on Twitter take place predominantly among users with similar viewpoints.

Parties, candidates, and voters are becoming increasingly engaged in politicalconversations through the micro-blogging platform Twitter. In this paper I showthat the structure of the social networks in which they are embedded has the poten-tial to become a source of information about policy positions. Under the assumptionthat social networks are homophilic (McPherson et al., 2001), this is, the propensityof users to cluster along partisan lines, I develop a Bayesian Spatial Following modelthat scales Twitter users along a common ideological dimension based on who theyfollow. I apply this network-based method to estimate ideal points for Twitter usersin the US, the UK, Spain, Italy, and the Netherlands. The resulting positions of theparty accounts on Twitter are highly correlated with oine measures based on theirvoting records and their manifestos. Similarly, this method is able to successfullyclassify individuals who state their political orientation publicly, and a sample ofusers from the state of Ohio whose Twitter accounts are matched with their voterregistration history. To illustrate the potential contribution of these estimates, Iexamine the extent to which online behavior is polarized along ideological lines. Us-ing the 2012 US presidential election campaign as a case study, I nd that publicexchanges on Twitter take place predominantly among users with similar viewpoints. Full paper and presentation here

 
 
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