Evaluating U.S. Electoral representation with a joint statistical model of congressional roll-calls, legislative text, and voter registration data

Zhengming Xing; Sunshine Hillygus; Lawrence Carin

doi:10.1145/3097983.3098151

Evaluating U.S. Electoral representation with a joint statistical model of congressional roll-calls, legislative text, and voter registration data

Zhengming Xing, Sunshine Hillygus, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing [email protected]
Pages	1205-1214
Number of pages	10
ISBN (Print)	9781450348874
DOIs	https://doi.org/10.1145/3097983.3098151
State	Published - Aug 13 2017
Externally published	Yes

Access to Document

10.1145/3097983.3098151

Cite this

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 (pp. 1205-1214). Association for Computing [email protected]. https://doi.org/10.1145/3097983.3098151

@inproceedings{4460787e757042b79614e07f1c334a2e,

title = "Evaluating U.S. Electoral representation with a joint statistical model of congressional roll-calls, legislative text, and voter registration data",

abstract = "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.",

author = "Zhengming Xing and Sunshine Hillygus and Lawrence Carin",

note = "Generated from Scopus record by KAUST IRTS on 2021-02-09",

year = "2017",

month = aug,

day = "13",

doi = "10.1145/3097983.3098151",

language = "English (US)",

isbn = "9781450348874",

pages = "1205--1214",

booktitle = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

publisher = "Association for Computing [email protected]",

}

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. Association for Computing [email protected], pp. 1205-1214. https://doi.org/10.1145/3097983.3098151

Evaluating U.S. Electoral representation with a joint statistical model of congressional roll-calls, legislative text, and voter registration data. / Xing, Zhengming; Hillygus, Sunshine; Carin, Lawrence.
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing [email protected], 2017. p. 1205-1214.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Evaluating U.S. Electoral representation with a joint statistical model of congressional roll-calls, legislative text, and voter registration data

AU - Xing, Zhengming

AU - Hillygus, Sunshine

AU - Carin, Lawrence

N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09

PY - 2017/8/13

Y1 - 2017/8/13

N2 - 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.

AB - 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.

UR - https://dl.acm.org/doi/10.1145/3097983.3098151

UR - http://www.scopus.com/inward/record.url?scp=85029021406&partnerID=8YFLogxK

U2 - 10.1145/3097983.3098151

DO - 10.1145/3097983.3098151

M3 - Conference contribution

SN - 9781450348874

SP - 1205

EP - 1214

BT - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

PB - Association for Computing [email protected]

ER -

Evaluating U.S. Electoral representation with a joint statistical model of congressional roll-calls, legislative text, and voter registration data

Abstract

Access to Document

Other files and links

Fingerprint

Cite this