A Penny for Your Tweets: Campaign Contributions and Capitol Hill Microblogs

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).

[1]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[2]  Sebastian Thrun,et al.  Text Classification from Labeled and Unlabeled Documents using EM , 2000, Machine Learning.

[3]  Dragomir R. Radev,et al.  An Automated Method of Topic-Coding Legislative Speech Over Time with Application to the 105th-108th U.S. Senate , 2006 .

[4]  Michael I. Jordan,et al.  Graphical Models, Exponential Families, and Variational Inference , 2008, Found. Trends Mach. Learn..

[5]  Chong Wang,et al.  Reading Tea Leaves: How Humans Interpret Topic Models , 2009, NIPS.

[6]  Jennifer Golbeck,et al.  Twitter use by the U.S. Congress , 2010, J. Assoc. Inf. Sci. Technol..

[7]  Isabell M. Welpe,et al.  Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment , 2010, ICWSM.

[8]  Brendan T. O'Connor,et al.  From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series , 2010, ICWSM.

[9]  Yutaka Matsuo,et al.  Earthquake shakes Twitter users: real-time event detection by social sensors , 2010, WWW '10.

[10]  Kristina Lerman,et al.  Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter Social Networks , 2010, ICWSM.

[11]  Justin Grimmer,et al.  A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate Press Releases , 2010, Political Analysis.

[12]  Sean Gerrish,et al.  Predicting Legislative Roll Calls from Text , 2011, ICML.

[13]  Eric P. Xing,et al.  Discovering Sociolinguistic Associations with Structured Sparsity , 2011, ACL.

[14]  Mark Dredze,et al.  You Are What You Tweet: Analyzing Twitter for Public Health , 2011, ICWSM.

[15]  Noah A. Smith,et al.  Textual Predictors of Bill Survival in Congressional Committees , 2012, NAACL.

[16]  T. Minka.  Estimating a Dirichlet distribution , 2012 .

[17]  Justin Grimmer,et al.  How Words and Money Cultivate a Personal Vote: The Effect of Legislator Credit Claiming on Constituent Credit Allocation , 2012, American Political Science Review.

[18]  Krishna P. Gummadi,et al.  The Emergence of Conventions in Online Social Networks , 2012, ICWSM.

[19]  Daniel Gayo-Avello,et al.  No, You Cannot Predict Elections with Twitter , 2012, IEEE Internet Comput..