Textual Predictors of Bill Survival in Congressional Committees

A U. S. Congressional bill is a textual artifact that must pass through a series of hurdles to become a law. In this paper, we focus on one of the most precarious and least understood stages in a bill's life: its consideration, behind closed doors, by a Congressional committee. We construct predictive models of whether a bill will survive committee, starting with a strong, novel baseline that uses features of the bill's sponsor and the committee it is referred to. We augment the model with information from the contents of bills, comparing different hypotheses about how a committee decides a bill's fate. These models give significant reductions in prediction error and highlight the importance of bill substance in explanations of policy-making and agenda-setting.

[1]  Noah A. Smith,et al.  Predicting Risk from Financial Reports with Regression , 2009, NAACL.

[2]  Burt L. Monroe,et al.  Fightin' Words: Lexical Feature Selection and Evaluation for Identifying the Content of Political Conflict , 2008, Political Analysis.

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

[4]  John D. Wilkerson,et al.  Congress and the Politics of Problem Solving , 2013 .

[5]  Matt Thomas,et al.  Get out the vote: Determining support or opposition from Congressional floor-debate transcripts , 2006, EMNLP.

[6]  Justin Grimmer,et al.  Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts , 2013, Political Analysis.

[7]  Pradipto Das,et al.  Discovering voter preferences in blogs using mixtures of topic models , 2009, AND '09.

[8]  E. Adler,et al.  The Scope and Urgency of Legislation : Reconsidering Bill Success in the House of Representatives , 2005 .

[9]  K. T. Poole,et al.  Polarized America: The Dance of Ideology and Unequal Riches , 2006 .

[10]  Beata Beigman Klebanov,et al.  Lexical Cohesion Analysis of Political Speech , 2008 .

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

[12]  M. Laver,et al.  Extracting Policy Positions from Political Texts Using Words as Data , 2003, American Political Science Review.

[13]  David M. Blei,et al.  Probabilistic topic models , 2012, Commun. ACM.

[14]  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 .

[15]  K. T. Poole,et al.  A Spatial Model for Legislative Roll Call Analysis , 1985 .

[16]  Joshua D. Clinton,et al.  The Statistical Analysis of Roll Call Data , 2004, American Political Science Review.

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

[18]  Dragomir R. Radev,et al.  How to Analyze Political Attention with Minimal Assumptions and Costs , 2010 .

[19]  Fernando Pereira,et al.  Reading the Markets: Forecasting Public Opinion of Political Candidates by News Analysis , 2008, COLING.

[20]  Simon Jackman,et al.  Multidimensional Analysis of Roll Call Data via Bayesian Simulation: Identification, Estimation, Inference, and Model Checking , 2001, Political Analysis.

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

[22]  K. T. Poole,et al.  Patterns of congressional voting , 1991 .

[23]  Bo Pang,et al.  A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts , 2004, ACL.