Detecting Authoritarianism in Digital Age: Language Modeling and Political Discourse in the 21st Century

The paper presents a new approach to studying authoritarian discourse using big data strategies. It introduces a deep learning model that utilizes a pre-trained transformers model fine-tuned for detecting authoritarian discourse in political speeches. Set up as a regression problem with weak supervision logic, the model is trained for the task of classification of segments of text for being/not being associated with authoritarian discourse. Rather than trying to define what an authoritarian discourse is, the model builds on the assumption that authoritarian leaders inherently define it. In other words, authoritarian leaders talk like authoritarians. When combined with the discourse defined by democratic leaders, the model learns the instances that are more often associated with authoritarians on the one hand and democrats on the other. The paper discusses several applications of the model and advocates for its usefulness in a broad range of research problems. It also outlines methodological advances in studying authoritarianism in political elites that go beyond qualitative description and circumstantial evidence.