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Framework of Voting Prediction of Parliament Members

Zahi Mizrahi, Shai Berkovitz, Nimrod Talmon, Michael Fire

TL;DR

This paper presents the Voting Prediction Framework (VPF), a data-driven, open-source platform to forecast parliamentary voting at both the individual-legislator and bill levels across multiple legislatures. VPF integrates data collection from diverse open-government sources, a parsing/feature-integration pipeline, and machine learning models to predict votes, with SHAP-based explanations to reveal feature importance. On data from Canada, Israel, Tunisia, the United Kingdom, and the United States, VPF achieves up to $85\%$ precision for individual votes and $82$–$85\%$ accuracy for bill outcomes, demonstrating strong cross-country predictive power and practical utility for transparency and policy analysis. The work also highlights country-specific drivers of voting behavior and warns of ethical risks and data limitations, underscoring the need for robust guidelines as predictive capabilities mature. Overall, VPF provides a scalable framework for comparative parliamentary analysis and decision-support in legislative prioritization.

Abstract

Keeping track of how lawmakers vote is essential for government transparency. While many parliamentary voting records are available online, they are often difficult to interpret, making it challenging to understand legislative behavior across parliaments and predict voting outcomes. Accurate prediction of votes has several potential benefits, from simplifying parliamentary work by filtering out bills with a low chance of passing to refining proposed legislation to increase its likelihood of approval. In this study, we leverage advanced machine learning and data analysis techniques to develop a comprehensive framework for predicting parliamentary voting outcomes across multiple legislatures. We introduce the Voting Prediction Framework (VPF) - a data-driven framework designed to forecast parliamentary voting outcomes at the individual legislator level and for entire bills. VPF consists of three key components: (1) Data Collection - gathering parliamentary voting records from multiple countries using APIs, web crawlers, and structured databases; (2) Parsing and Feature Integration - processing and enriching the data with meaningful features, such as legislator seniority, and content-based characteristics of a given bill; and (3) Prediction Models - using machine learning to forecast how each parliament member will vote and whether a bill is likely to pass. The framework will be open source, enabling anyone to use or modify the framework. To evaluate VPF, we analyzed over 5 million voting records from five countries - Canada, Israel, Tunisia, the United Kingdom and the USA. Our results show that VPF achieves up to 85% precision in predicting individual votes and up to 84% accuracy in predicting overall bill outcomes. These findings highlight VPF's potential as a valuable tool for political analysis, policy research, and enhancing public access to legislative decision-making.

Framework of Voting Prediction of Parliament Members

TL;DR

This paper presents the Voting Prediction Framework (VPF), a data-driven, open-source platform to forecast parliamentary voting at both the individual-legislator and bill levels across multiple legislatures. VPF integrates data collection from diverse open-government sources, a parsing/feature-integration pipeline, and machine learning models to predict votes, with SHAP-based explanations to reveal feature importance. On data from Canada, Israel, Tunisia, the United Kingdom, and the United States, VPF achieves up to precision for individual votes and – accuracy for bill outcomes, demonstrating strong cross-country predictive power and practical utility for transparency and policy analysis. The work also highlights country-specific drivers of voting behavior and warns of ethical risks and data limitations, underscoring the need for robust guidelines as predictive capabilities mature. Overall, VPF provides a scalable framework for comparative parliamentary analysis and decision-support in legislative prioritization.

Abstract

Keeping track of how lawmakers vote is essential for government transparency. While many parliamentary voting records are available online, they are often difficult to interpret, making it challenging to understand legislative behavior across parliaments and predict voting outcomes. Accurate prediction of votes has several potential benefits, from simplifying parliamentary work by filtering out bills with a low chance of passing to refining proposed legislation to increase its likelihood of approval. In this study, we leverage advanced machine learning and data analysis techniques to develop a comprehensive framework for predicting parliamentary voting outcomes across multiple legislatures. We introduce the Voting Prediction Framework (VPF) - a data-driven framework designed to forecast parliamentary voting outcomes at the individual legislator level and for entire bills. VPF consists of three key components: (1) Data Collection - gathering parliamentary voting records from multiple countries using APIs, web crawlers, and structured databases; (2) Parsing and Feature Integration - processing and enriching the data with meaningful features, such as legislator seniority, and content-based characteristics of a given bill; and (3) Prediction Models - using machine learning to forecast how each parliament member will vote and whether a bill is likely to pass. The framework will be open source, enabling anyone to use or modify the framework. To evaluate VPF, we analyzed over 5 million voting records from five countries - Canada, Israel, Tunisia, the United Kingdom and the USA. Our results show that VPF achieves up to 85% precision in predicting individual votes and up to 84% accuracy in predicting overall bill outcomes. These findings highlight VPF's potential as a valuable tool for political analysis, policy research, and enhancing public access to legislative decision-making.
Paper Structure (19 sections, 5 figures, 10 tables)

This paper contains 19 sections, 5 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Open Parliament Data
  4. Open Parliament Frameworks
  5. The Legislative Process
  6. Data Mining on Legislative Voting
  7. Methods
  8. Data Collection and Preprocessing
  9. Data Analysis
  10. Model Evaluation
  11. Experiments
  12. Canada
  13. Israel
  14. Tunisia
  15. United Kingdom
  16. ...and 4 more sections

Figures (5)

  • Figure 1: Framework Overview
  • Figure 2: Illustration of the legislative process
  • Figure 3: Experiments Result Summary of Vote Prediction
  • Figure 4: ROC Curves
  • Figure 5: SHAP values in Beeswarm visualization of Canada, Israel, Tunisia, United Kingdom, and United States.