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

Pablo Barberá in Political Analysis

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Politicians 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 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. is 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 US and five European countries. e estimated positions of legislators and political parties
replicate conventional measures of ideology. e 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  US presidential election campaign
is clustered along ideological lines.

Citation: Barberá, P. 2015. “Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data”Political Analysis, 23 (1), 76-91.
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