Fitxers
Tipus de document
ArticleVersió
Versió acceptadaData de publicació
Llicència de publicació
Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/221351
How politicians learn from citizens' feedback: The case of gender on Twitter
Títol de la revista
Director/Tutor
ISSN de la revista
Títol del volum
Recurs relacionat
Resum
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.
Matèries
Matèries (anglès)
Citació
Citació
SCHÖLL, Nikolas, GALLEGO DOBÓN, Aina and MENS, Gaël le. How politicians learn from citizens' feedback: The case of gender on Twitter. American Journal of Political Science. 2022. Vol. 68, num. 2, pags. 557-574. ISSN 0092-5853. [consulted: 20 of August of 2026]. Available at: https://hdl.handle.net/2445/221351