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Politician-Citizen Interactions and Dynamic Representation: Evidence from Twitter Nikolas Schöll Aina Gallego Gaël Le Mens January 2021 Barcelona GSE Working Paper Series Working Paper nº 1238 Politician-Citizen Interactions and Dynamic Representation: Evidence from Twitter∗ Nikolas Sch¨oll,y Aina Gallegozand Ga¨elLe Mensx January 29 2021 Abstract We study how politicians learn about public opinion through their regular interac- tions with citizens and how they respond to perceived changes. We model this process within a reinforcement learning framework: politicians talk about different policy is- sues, 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 fe- male 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. Keywords: Political responsiveness, representation, social media, gender. Word count: 9461 ∗ Acknowledgements: We thank Ruben Enikolopov, Maria Petrova, David Rossell, Tania Verge, Fabrizio Gilardi, Pablo Barber´a,Elias Dinas, Elisa Mougin, and Cyrill Otteni, Mikhail Spektor, seminar participants at Universitat Pompeu Fabra, Wissenschaftszentrum Berlin, European University Institute, King's College London, Online Political Economy Seminar Series, Instituto de Empresa, and conference attendants at the European Political Science Association for helpful comments and suggestions.
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