1 Citation (Scopus)

Abstract

In this paper, we mine the US congress voting records to extract the latent information about the trust among congress members. In particular, we model the Senate as a social network and the voting process as a set of outcomes of the underlying opinion dynamics which we assume follow a corrupted DeGroot model. The transition matrix in the opinion dynamics model is the trust matrix among Senators that we estimate. Our methodology is to first cluster the voting bills into different groups, and then obtain the Senators' opinions about the theme of each cluster, by performing a weighted Bernoulli sampling on the binary voting results. A key characteristic of the US congress is that most of the Senators stick with their own ideology. In view of this, we assign the role of stubborn nodes to some Senators, since their existence can facilitate estimating the trust matrix. In fact, we find the trust matrix by solving a linear regression problem, and then analyze the underlying latent information. Interestingly, our numerical results are quite consistent with the common intuition. More importantly, the trust information extracted can help understand the underlying relationship in the Senate and offer insights for devising political strategies.

Original languageEnglish (US)
Title of host publication2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1202-1206
Number of pages5
ISBN (Electronic)9781509045457
DOIs
StatePublished - Apr 19 2017
Event2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Washington, United States
Duration: Dec 7 2016Dec 9 2016

Other

Other2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
CountryUnited States
CityWashington
Period12/7/1612/9/16

Fingerprint

Data mining
Linear regression
Dynamic models
Sampling

Keywords

  • Data mining
  • Senator
  • Social network
  • Trust analysis
  • US congress

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

Cite this

Wu, S. X., Wai, H. T., & Scaglione, A. (2017). Data mining the underlying trust in the US Congress. In 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings (pp. 1202-1206). [7906032] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2016.7906032

Data mining the underlying trust in the US Congress. / Wu, Sissi Xiaoxiao; Wai, Hoi To; Scaglione, Anna.

2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1202-1206 7906032.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wu, SX, Wai, HT & Scaglione, A 2017, Data mining the underlying trust in the US Congress. in 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings., 7906032, Institute of Electrical and Electronics Engineers Inc., pp. 1202-1206, 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016, Washington, United States, 12/7/16. https://doi.org/10.1109/GlobalSIP.2016.7906032
Wu SX, Wai HT, Scaglione A. Data mining the underlying trust in the US Congress. In 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1202-1206. 7906032 https://doi.org/10.1109/GlobalSIP.2016.7906032
Wu, Sissi Xiaoxiao ; Wai, Hoi To ; Scaglione, Anna. / Data mining the underlying trust in the US Congress. 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1202-1206
@inproceedings{618b8cb6d9a24441839f83010f95d1c2,
title = "Data mining the underlying trust in the US Congress",
abstract = "In this paper, we mine the US congress voting records to extract the latent information about the trust among congress members. In particular, we model the Senate as a social network and the voting process as a set of outcomes of the underlying opinion dynamics which we assume follow a corrupted DeGroot model. The transition matrix in the opinion dynamics model is the trust matrix among Senators that we estimate. Our methodology is to first cluster the voting bills into different groups, and then obtain the Senators' opinions about the theme of each cluster, by performing a weighted Bernoulli sampling on the binary voting results. A key characteristic of the US congress is that most of the Senators stick with their own ideology. In view of this, we assign the role of stubborn nodes to some Senators, since their existence can facilitate estimating the trust matrix. In fact, we find the trust matrix by solving a linear regression problem, and then analyze the underlying latent information. Interestingly, our numerical results are quite consistent with the common intuition. More importantly, the trust information extracted can help understand the underlying relationship in the Senate and offer insights for devising political strategies.",
keywords = "Data mining, Senator, Social network, Trust analysis, US congress",
author = "Wu, {Sissi Xiaoxiao} and Wai, {Hoi To} and Anna Scaglione",
year = "2017",
month = "4",
day = "19",
doi = "10.1109/GlobalSIP.2016.7906032",
language = "English (US)",
pages = "1202--1206",
booktitle = "2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

TY - GEN

T1 - Data mining the underlying trust in the US Congress

AU - Wu, Sissi Xiaoxiao

AU - Wai, Hoi To

AU - Scaglione, Anna

PY - 2017/4/19

Y1 - 2017/4/19

N2 - In this paper, we mine the US congress voting records to extract the latent information about the trust among congress members. In particular, we model the Senate as a social network and the voting process as a set of outcomes of the underlying opinion dynamics which we assume follow a corrupted DeGroot model. The transition matrix in the opinion dynamics model is the trust matrix among Senators that we estimate. Our methodology is to first cluster the voting bills into different groups, and then obtain the Senators' opinions about the theme of each cluster, by performing a weighted Bernoulli sampling on the binary voting results. A key characteristic of the US congress is that most of the Senators stick with their own ideology. In view of this, we assign the role of stubborn nodes to some Senators, since their existence can facilitate estimating the trust matrix. In fact, we find the trust matrix by solving a linear regression problem, and then analyze the underlying latent information. Interestingly, our numerical results are quite consistent with the common intuition. More importantly, the trust information extracted can help understand the underlying relationship in the Senate and offer insights for devising political strategies.

AB - In this paper, we mine the US congress voting records to extract the latent information about the trust among congress members. In particular, we model the Senate as a social network and the voting process as a set of outcomes of the underlying opinion dynamics which we assume follow a corrupted DeGroot model. The transition matrix in the opinion dynamics model is the trust matrix among Senators that we estimate. Our methodology is to first cluster the voting bills into different groups, and then obtain the Senators' opinions about the theme of each cluster, by performing a weighted Bernoulli sampling on the binary voting results. A key characteristic of the US congress is that most of the Senators stick with their own ideology. In view of this, we assign the role of stubborn nodes to some Senators, since their existence can facilitate estimating the trust matrix. In fact, we find the trust matrix by solving a linear regression problem, and then analyze the underlying latent information. Interestingly, our numerical results are quite consistent with the common intuition. More importantly, the trust information extracted can help understand the underlying relationship in the Senate and offer insights for devising political strategies.

KW - Data mining

KW - Senator

KW - Social network

KW - Trust analysis

KW - US congress

UR - http://www.scopus.com/inward/record.url?scp=85019256100&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85019256100&partnerID=8YFLogxK

U2 - 10.1109/GlobalSIP.2016.7906032

DO - 10.1109/GlobalSIP.2016.7906032

M3 - Conference contribution

AN - SCOPUS:85019256100

SP - 1202

EP - 1206

BT - 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -