Understanding and Predicting the Legislative Process in the Chamber of Deputies of Brazil
Authors: 
Danilo Amaral de Oliveira, João Porto de Albuquerque, and Alexandre C. B. DelbemAuthors Info & Claims
Danilo Amaral de Oliveira, João Porto de Albuquerque, and Alexandre C. B. DelbemAuthors Info & ClaimsJune 2018
Article No.: 23, Pages 1 - 8
Abstract
In this article, based on of open legislative data mining, we propose a methodology to create a model capable of indicating which characteristics have a positive or negative impact on the approval of a bill by the Chamber of Deputies. Added to the explanatory capacity, the model can also predict whether a bill will be approved or not. The model was submitted to experiments and analysis that measured and validated its explanatory and predictive capacity. In order to identify the most relevant characteristics we use an impact formula that calculates the relevance of the characteristics of the model in its final approval or archiving decision. In the end, the generated model contributed by clarifying characteristics relevant to the approval or not of the bill and achieved a good performance in its predictive capacity.
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- Understanding and Predicting the Legislative Process in the Chamber of Deputies of Brazil
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Published In
June 2018
578 pages
ISBN:9781450365598
DOI:10.1145/3229345
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- SIGAPP: ACM Special Interest Group on Applied Computing
- SIGMIS: ACM Special Interest Group on Management Information Systems
- SBC: Sociedade Brasileira de Computação
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Association for Computing Machinery
New York, NY, United States
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Published: 04 June 2018
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