A Penny for Your Tweets: Campaign Contributions and Capitol Hill Microblogs
Keywords:Text Driven Prediction, Text Regression, Topic Model, Congress, Micorblogs, User Generated Text
Who influences a politician’s public statements? In this paper, we explore one plausible explanation: that financial incentives from campaign contributors affect what politicians say. Based on this idea, we design text-driven models for campaign contribution profile prediction. Using a large corpus of public microblog messages by members of the U.S. Congress, we find evidence for such an association, at the level of contributing industries. We find complementary strengths in a simple model (which has better predictive accuracy) and a more complex model (which gives a more intuitive, human-interpretable explanation).