Your most telling friends: Propagating latent ideological features on Twitter using neighborhood coherence

Abstract

A growing literature on ideology estimation through scaling methods in social networks restricts the scaling procedure to nodes that provide interpretability of the resulting feature space. On Twitter, for example, it is common to consider the subnetwork of parliamentarians and their followers. While effective in inferring meaningful ideological features, this restriction limits interesting applications such as country-wide measurement of polarization and its evolution. We propose two methods to propagate ideological features beyond these sub-networks: based on homophily (linked users have similar ideology), and based on structural similarity (nodes with similar neighborhoods have similar ideologies). In our methods, we leverage the concept of ideological coherence of a neighborhood as a parameter for propagation. Using Twitter data, we produce an ideological scaling for 370K users, and analyze the two propagation methods on a population of 6.5M users. We find that, when coherence is considered, the ideology of a user is better estimated from those with similar neighborhoods, than from their immediate neighbors.