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

TLDR

Politicians and citizens increasingly engage in political conversations on social media outlets such as Twitter. The article demonstrates that social network structure can reveal ideological positions and uses this insight to analyze how online behavior during the 2012 US presidential election clustered along ideological lines. A Bayesian Spatial Following model is developed that treats ideology as a latent variable inferred from whom users follow, and it is applied to estimate ideal points for a large sample of elite and mass public Twitter users in the United States and five European countries. The method estimates ideology for more actors than any alternative, replicates conventional measures for legislators and parties, and accurately classifies individuals with publicly stated preferences or party registration records.

Abstract

Politicians and citizens increasingly engage in political conversations on social media outlets such as Twitter. In this article, I show that the structure of the social networks in which they are embedded can be a source of information about their ideological positions. Under the assumption that social networks are homophilic, I develop a Bayesian Spatial Following model that considers ideology as a latent variable, whose value can be inferred by examining which politics actors each user is following. This method allows us to estimate ideology for more actors than any existing alternative, at any point in time and across many polities. I apply this method to estimate ideal points for a large sample of both elite and mass public Twitter users in the United States and five European countries. The estimated positions of legislators and political parties replicate conventional measures of ideology. The method is also able to successfully classify individuals who state their political preferences publicly and a sample of users matched with their party registration records. To illustrate the potential contribution of these estimates, I examine the extent to which online behavior during the 2012 US presidential election campaign is clustered along ideological lines.