TY - GEN
T1 - Machine Learning Detection of Campaign Financing from FIRE Industries to Members of the 116th U.S. Congress
AU - Karagiannis, Dimitri
AU - Tarquinio, Michael
AU - Jalali, Ali
AU - Thomas, Michael
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This paper uses a random forest machine learning classifier to detect relationships between funding of congressional candidates in the United States House of Representatives 116th congress from sources associated with the Finance, Insurance, and Real Estate (FIRE) industries, and votes cast by those members on bills and resolutions that have been lobbied by companies within FIRE industries. Patterns between representative voting and funding were investigated in two modes; detection of previous funding level using votes on relevant bills, and detection of future funding level using those votes. This paper shows detectable patterns in both modes.
AB - This paper uses a random forest machine learning classifier to detect relationships between funding of congressional candidates in the United States House of Representatives 116th congress from sources associated with the Finance, Insurance, and Real Estate (FIRE) industries, and votes cast by those members on bills and resolutions that have been lobbied by companies within FIRE industries. Patterns between representative voting and funding were investigated in two modes; detection of previous funding level using votes on relevant bills, and detection of future funding level using those votes. This paper shows detectable patterns in both modes.
UR - http://www.scopus.com/inward/record.url?scp=85189938355&partnerID=8YFLogxK
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U2 - 10.1109/ACDSA59508.2024.10467691
DO - 10.1109/ACDSA59508.2024.10467691
M3 - Conference contribution
AN - SCOPUS:85189938355
T3 - International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2024
BT - International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2024
Y2 - 1 February 2024 through 2 February 2024
ER -