Birds of the Same Feather Tweet Together. Bayesian Ideal Point Estimation Using Twitter Data

Abstract

Political actors and citizens increasingly engage in political conversations on social media outlets such as Twitter. In this paper I show that the structure of the social networks in which they are embedded has the potential to become a source of information about policy positions. Under the assumption that social networks are homophilic, 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 a large sample of Twitter users in the US, the UK, Spain, Germany, Italy, and the Netherlands. The resulting positions of the party accounts on Twitter are highly correlated with offline 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. Using the 2012 US presidential election campaign as a case study, I find that public exchanges on Twitter take place predominantly among users with similar viewpoints.