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Scaling Politically Meaningful Dimensions Using Texts and Votes
Author(s) -
Lauderdale Benjamin E.,
Clark Tom S.
Publication year - 2014
Publication title -
american journal of political science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.347
H-Index - 170
eISSN - 1540-5907
pISSN - 0092-5853
DOI - 10.1111/ajps.12085
Subject(s) - latent dirichlet allocation , voting , categorization , computer science , metadata , multidimensional scaling , variety (cybernetics) , topic model , artificial intelligence , identification (biology) , dirichlet distribution , data science , politics , machine learning , political science , mathematics , world wide web , mathematical analysis , botany , law , biology , boundary value problem
Item response theory models for roll‐call voting data provide political scientists with parsimonious descriptions of political actors' relative preferences. However, models using only voting data tend to obscure variation in preferences across different issues due to identification and labeling problems that arise in multidimensional scaling models. We propose a new approach to using sources of metadata about votes to estimate the degree to which those votes are about common issues. We demonstrate our approach with votes and opinion texts from the U.S. Supreme Court, using latent Dirichlet allocation to discover the extent to which different issues were at stake in different cases and estimating justice preferences within each of those issues. This approach can be applied using a variety of unsupervised and supervised topic models for text, community detection models for networks, or any other tool capable of generating discrete or mixture categorization of subject matter from relevant vote‐specific metadata.