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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.

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Paper Details

Authors:
Sissi Xiaoxiao Wu, Hoi-To Wai and Anna Scaglione
Submitted On:
6 December 2016 - 11:29pm
Short Link:
Type:
Presentation Slides
Event:
Presenter's Name:
XIAOXIAO WU
Paper Code:
1167
Document Year:
2016
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[1] Sissi Xiaoxiao Wu, Hoi-To Wai and Anna Scaglione, "Data Mining the Underlying Trust in the US Congress", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1393. Accessed: Sep. 20, 2020.
@article{1393-16,
url = {http://sigport.org/1393},
author = {Sissi Xiaoxiao Wu; Hoi-To Wai and Anna Scaglione },
publisher = {IEEE SigPort},
title = {Data Mining the Underlying Trust in the US Congress},
year = {2016} }
TY - EJOUR
T1 - Data Mining the Underlying Trust in the US Congress
AU - Sissi Xiaoxiao Wu; Hoi-To Wai and Anna Scaglione
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1393
ER -
Sissi Xiaoxiao Wu, Hoi-To Wai and Anna Scaglione. (2016). Data Mining the Underlying Trust in the US Congress. IEEE SigPort. http://sigport.org/1393
Sissi Xiaoxiao Wu, Hoi-To Wai and Anna Scaglione, 2016. Data Mining the Underlying Trust in the US Congress. Available at: http://sigport.org/1393.
Sissi Xiaoxiao Wu, Hoi-To Wai and Anna Scaglione. (2016). "Data Mining the Underlying Trust in the US Congress." Web.
1. Sissi Xiaoxiao Wu, Hoi-To Wai and Anna Scaglione. Data Mining the Underlying Trust in the US Congress [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1393