Evaluating U.S. Electoral representation with a joint statistical model of congressional roll-calls, legislative text, and voter registration data
Publication
, Conference
Xing, Z; Hillygus, S; Carin, L
Published in: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
August 13, 2017
Extensive information on 3 million randomly sampled United States citizens is used to construct a statistical model of constituent preferences for each U.S. congressional district. This model is linked to the legislative voting record of the legislator from each district, yielding an integrated model for constituency data, legislative roll-call votes, and the text of the legislation. The model is used to examine the extent to which legislators' voting records are aligned with constituent preferences, and the implications of that alignment (or lack thereof) on subsequent election outcomes. The analysis is based on a Bayesian formalism, with fast inference via a stochastic variational Bayesian analysis.
Duke Scholars
Published In
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
DOI
Publication Date
August 13, 2017
Volume
Part F129685
Start / End Page
1205 / 1214
Citation
APA
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ICMJE
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Xing, Z., Hillygus, S., & Carin, L. (2017). Evaluating U.S. Electoral representation with a joint statistical model of congressional roll-calls, legislative text, and voter registration data. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Vol. Part F129685, pp. 1205–1214). https://doi.org/10.1145/3097983.3098151
Xing, Z., S. Hillygus, and L. Carin. “Evaluating U.S. Electoral representation with a joint statistical model of congressional roll-calls, legislative text, and voter registration data.” In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Part F129685:1205–14, 2017. https://doi.org/10.1145/3097983.3098151.
Xing Z, Hillygus S, Carin L. Evaluating U.S. Electoral representation with a joint statistical model of congressional roll-calls, legislative text, and voter registration data. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017. p. 1205–14.
Xing, Z., et al. “Evaluating U.S. Electoral representation with a joint statistical model of congressional roll-calls, legislative text, and voter registration data.” Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. Part F129685, 2017, pp. 1205–14. Scopus, doi:10.1145/3097983.3098151.
Xing Z, Hillygus S, Carin L. Evaluating U.S. Electoral representation with a joint statistical model of congressional roll-calls, legislative text, and voter registration data. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017. p. 1205–1214.
Published In
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
DOI
Publication Date
August 13, 2017
Volume
Part F129685
Start / End Page
1205 / 1214