Deriving the Political Affinity of Twitter Users from Their Followers

Abstract

In this work, we show that Twitter users can reveal valuable political information about particular Nodes of Interest (NOIs) they opt to follow. More precisely, we utilize an interesting graph projection method and a series of algorithmic approaches, such as modularity clustering, a minimum linear arrangement (MinLA) approximation algorithm and the DeGroot opinion update model in order to reveal the political affinity of selected NOIs. Our methods, which are purely structure-based, are applied to a snapshot of the Twitter network based on the user accounts of NOIs, consisting of the members of the current Greek Parliament along with their respective followers. The findings confirm that the information obtained can portray with significant precision the political affinity of the NOIs. We, furthermore, argue that these methods are of general interest for imprinting the political leaning of other NOIs, for example news media, and potentially classifying them in respect to their political bias.