Politician-Citizen Interactions and Dynamic Representation: Evidence from Twitter

Abstract

We study how politicians learn about public opinion through their regular interactions with citizens and how they respond to perceived changes. We model this process within a reinforcement learning framework: politicians talk about different policy issues, listen to feedback, and increase attention to better received issues. Because politicians are exposed to different feedback depending on their social identities, being responsive leads to divergence in issue attention over time. We apply these ideas to study the rise of gender issues. We collected 1.5 million tweets written by Spanish MPs, classified them using a deep learning algorithm, and measured feedback using retweets and likes. We find that politicians are responsive to feedback and that female politicians receive relatively more positive feedback for writing on gender issues. An analysis of mechanisms sheds light on why this happens. In the conclusion, we discuss how reinforcement learning can create unequal responsiveness, misperceptions, and polarization.