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How politicians learn from citizens' feedback: The case of gender on Twitter

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This article studies how politicians react to feedback from citizens on social media. We use a reinforcementlearning 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.

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American Journal of Political Science, 2022, vol. 68, num.2, p. 557-574

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Articles publicats en revistes (Ciència Política, Dret Constitucional i Filosofia del Dret)

# metadata.dc.title[].value
How politicians learn from citizens' feedback: The case of gender on Twitter