Schöll, NikolasGallego Dobón, AinaLe Mens, Gaël2023-03-242023-03-242024Schöll N, Gallego A, Le Mens G. How politicians learn from citizens' feedback: the case of gender on Twitter. Am J Pol Sci. 2024 Apr;68(2):557-74. DOI: 10.1111/ajps.127720092-5853http://hdl.handle.net/10230/56344Includes supplementary materials: online appendix; replication file.This article studies how politicians react to feedback from citizens on social media. We use a reinforcement-learning framework to model how politicians respond to citizens’ positive feedback by increasing attention to better received issues and allow feedback to vary depending on politicians’ gender. To test the model, we collect 1.5 million tweets published by Spanish MPs over 3 years, identify gender-issue tweets using a deep-learning algorithm (BERT) and measure feedback using retweets and likes. We find that citizens provide more positive feedback to female politicians for writing about gender, and that this contributes to their specialization in gender issues. The analysis of mechanisms suggests that female politicians receive more positive feedback because they are treated differently by citizens. To conclude, we discuss implications for representation, misperceptions, and polarization.application/pdfeng© 2023 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.Xarxes socials en líniaCiutadaniaPolíticsinfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1111/ajps.12772info:eu-repo/semantics/openAccess