Replication Data for: How Politicians Learn from Citizens' Feedback: The Case of Gender on Twitter
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'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 su' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Gender","Issue attention","Political responsiveness","Representation","Social media"]} <|eot_id|>
 Here is the Description of a dataset. Description:'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 su' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
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 su
Gender; Issue attention; Political responsiveness; Representation; Social media