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

Abstract

Edited by R. Michael AlvarezPoliticians 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.

Social media and political communication in the 2014 elections to the European Parliament

“…We adopt a modeling strategy similar to the one presented in 3 with the difference that in this case we can use the the policy position of the candidates calculated from the Twitter follower graph (Barberá, 2015;Barberá et al, 2015). An interesting and contrary pattern emerges to that shown above for the probability of having a Twitter account.…”

“…In order to directly discover hashtags that represent a position on EU integration, we used an "elastic net" regularized regression model (Zou and Hastie, 2005) to measure the association between hashtag frequency and the EU integration policy of MEPs, using estimates of policy positions calculated from the Twitter follower graph and validated against EU positions from the expert judgements (Barberá, 2015;Barberá et al, 2015). The elastic net regression combines the penalties of LASSO and ridge regression, We extracted the frequency with which hashtags occurred in the tweets of each candidate, for hashtags that were used by at least ten candidates and occurred at least twenty times in total, and weighted the frequencies by the product of term frequency and inverse document frequency (tf-idf ), considering the tweets of each candidate as a single document.…”

Quantifying Social Media’s Political Space: Estimating Ideology from Publicly Revealed Preferences on Facebook

“…2 Political scientists have begun to study how people engage and express their political viewpoints online using this rich data source (e.g., Barberá 2014Butler and Broockman 2011;Butler, Karpowitz, and Pope 2012;Grimmer, Messing, and Westwood 2012;Messing and Westwood 2012;Mutz and Young 2011;Wojcieszak and Mutz 2009).…”

The empiricist’s challenge: Asking meaningful questions in political science in the age of big data

“…In the other group, we find studies using digital trace data as predictors of political events and phenomena or proxies for other more traditional measurement approaches in the social sciences, such as surveys (Barberá, 2015;DiGrazia, McKelvey, Bollen, & Rojas, 2013;Steinert-Threlkeld, Mocanu, Vespignani, & Fowler, 2015;Tumasjan, Sprenger, Sandner, & Welpe, 2010). Here, we find a strong prominence of studies concentrating on statistical predictions of various political outcomes based on signals found in digital trace data (see Hofman, Sharma, & Watts, 2017;Schoen et al, 2013).…”