Estimating the Political Orientation of Twitter Users Using Network Embedding Algorithms

Abstract

Estimating the political orientation of citizens has always been a crucial task in communication as well as political science studies. In this study, advanced network analysis tools are developed to tackle this task. Specifically, using network embedding algorithms, friendship networks are embedded into lower-dimensional Euclidean space while preserving specific topological features of social networks. The resulting embedded vectors are then used to estimate political orientation of Twitter users. It is also shown that these numerical representations can be used to estimate other user traits. The developed tools are applied to a benchmark dataset as well as a dataset developed by the authors. Our model decreased the mean absolute error of the state-of-the-art predictions on the benchmark income dataset by 15%. The developed tools have multiple use cases, for example, studying echo chambers and political communication on OSNs and in marketing campaigns to estimate user’s preferences.