DocumentCode :
589248
Title :
Predicting Congressional Votes Based on Campaign Finance Data
Author :
Smith, Samuel ; Jae Yeon Baek ; Zhaoyi Kang ; Song, Dong ; El Ghaoui, Laurent ; Frank, Michael
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Univ. of California, Berkeley, Berkeley, CA, USA
Volume :
1
fYear :
2012
fDate :
12-15 Dec. 2012
Firstpage :
640
Lastpage :
645
Abstract :
The USA is witnessing a heavy debate about the influence of political campaign contributions and votes cast on the floor of the United States Congress. We contribute quantitative arguments to this predominantly qualitative discussion by analyzing a dataset of political campaign contributions. We validate that the campaign donations of politicians are mainly influenced by his or her political power and affiliation to a political party. Approaching the question of whether donations influence votes, we employ supervised learning techniques to classify how a politician will vote based solely upon from whom he or she received donations. The statistical significance of the results are assessed within the context of the debate currently surrounding campaign finance reform. Our experimental findings exhibit a large predictive power of the donations, demonstrating high informativeness of the donations with respect to voting outcomes. However, observing the slightly superior accuracy of the party line as a predictor, a causal relationship between donations and votes cannot be identified.
Keywords :
financial management; government data processing; learning (artificial intelligence); politics; statistical analysis; USA; United States Congress; campaign finance data; campaign finance reform; congressional vote prediction; political affiliation; political campaign contributions; political party; political power; politician campaign donations; quantitative arguments; statistical significance; supervised learning techniques; voting outcomes; Accuracy; Companies; Correlation; Standards organizations; Support vector machines; Training; L1-regularization; behavior prediction; classification; politics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Applications (ICMLA), 2012 11th International Conference on
Conference_Location :
Boca Raton, FL
Print_ISBN :
978-1-4673-4651-1
Type :
conf
DOI :
10.1109/ICMLA.2012.119
Filename :
6406640
Link To Document :
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