Work

How Politicians Learn from Citizens’ Feedback: The Case of Gender on Twitter

Year: 2023

Type: article

Abstract: 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.

Source: American Journal of Political Science

DOI: 10.1111/ajps.12772

Authors: Nikolas Schöll, Aina Gallego, Gaël Le Mens

Institutions: Universitat Pompeu Fabra, Universitat de Barcelona, Barcelona School of Economics

Language: English

Topic: Gender Politics and Representation

Subfield: Gender Studies

Field: Social Sciences

Domain: Social Sciences

SDGs: Peace, justice, and strong institutions, Gender equality, Reduced inequalities

Open Access status: hybrid

Estimated APC paid: $3,860