Replication Data for: How Politicians Learn from Citizens' Feedback: The Case of Gender on Twitter

This article studies how the feedback that politicians receive from citizens on social media affect the issues they choose to discuss, and hypothesizes that politicians are exposed to different feedback depending on their social group membership, leading to divergence in issue attention. We use a reinforcement learning framework to model how politicians choose policy issues and respond to citizens' feedback by increasing attention to better received issues. We collect 1.5 million tweets published by Spanish MPs over three 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.