Open extraction of fine-grained political statements

Abstract

Text data has recently been used as evidence in estimating the political ideologies of individuals, including political elites and social media users. While inferences about people are often the intrinsic quantity of interest, we draw inspiration from open information extraction to identify a new task: inferring the political import of propositions like OBAMA IS A SOCIALIST. We present several models that exploit the structure that exists between people and the assertions they make to learn latent positions of people and propositions at the same time, and we evaluate them on a novel dataset of propositions judged on a political spectrum.