Politician-citizen interactions and dynamic representation: Evidence from Twitter

Mostra el registre complet Registre parcial de l'ítem

• dc.contributor.author Gallego, Aina
• dc.contributor.author Schöll, Nikolas
• dc.contributor.author Le Mens, Gaël
• dc.contributor.other Universitat Pompeu Fabra. Departament d'Economia i Empresa
• dc.date.accessioned 2024-11-14T10:09:56Z
• dc.date.available 2024-11-14T10:09:56Z
• dc.date.issued 2021-01-29
• dc.date.modified 2024-11-14T10:07:41Z
• dc.description.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.
• dc.format.mimetype application/pdf*
• dc.identifier https://econ-papers.upf.edu/ca/paper.php?id=1769
• dc.identifier.citation American Journal of Political Science, 2023, DOI: https://doi.org/10.1111/ajps.12772
• dc.identifier.uri http://hdl.handle.net/10230/68646
• dc.language.iso eng
• dc.relation.ispartofseries Economics and Business Working Papers Series; 1769
• dc.rights L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
• dc.subject.keyword political responsiveness
• dc.subject.keyword representation
• dc.subject.keyword social media
• dc.subject.keyword gender
• dc.title Politician-citizen interactions and dynamic representation: Evidence from Twitter
• dc.title.alternative How Politicians Learn from Citizens’ Feedback: The Case of Gender on Twitter
• dc.type info:eu-repo/semantics/workingPaper