Framework of Voting Prediction of Parliament Members

Framework of Voting Prediction of Parliament Members | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Framework of Voting Prediction of Parliament Members Zahi Mizrahi, Michael Fire, Shai Berkovitz, Nimrod Talmon This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7277181/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Tracking lawmakers’ voting behavior is vital for government transparency. Although many parliaments publish voting records online, these datasets are often complex and difficult to analyze. This research presents the Voting Prediction Framework (VPF), a machine learning-based system designed to predict parliamentary voting outcomes at both the individual legislator and bill levels across multiple countries. VPF aims to support legislative transparency, streamline parliamentary work, and aid in policy refinement by identifying bills with a low likelihood of passing. The framework comprises three main components: (1) Data Collection – retrieving voting records from various countries using APIs, web crawlers, and structured databases; (2) Parsing and Feature Integration – enriching data with relevant features such as legislator seniority and bill content; and (3) Prediction Models – applying machine learning algorithms to forecast individual votes and bill-level outcomes. VPF is released as open source to encourage accessibility and adaptation. To validate VPF, we analyzed over 5 million votes from five countries: Canada, Israel, Tunisia, the United Kingdom, and the United States. Results indicate up to 85% precision in predicting individual votes and up to 84% accuracy for bill outcomes, demonstrating VPF’s effectiveness as a tool for political analysis and public insight into legislative processes. Votes Prediction Open Parliament Political Science Big Data Machine Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7277181","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512712006,"identity":"4ce25677-ac29-4d47-9062-3198be64337a","order_by":0,"name":"Zahi Mizrahi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYFACHoYDDBVgFhtUhLHxAGEtZ8Aa4FoaCGphYGxD0cLAgFeLfPvZg4duzrsjJz+/ge3h1zYGef4GZvy2MPbkJRzO3fbM2OAYA7uxbBuD4YwDBBzGzJBjANRyOHED0GHSkm0MjBsI+YWN/w1Qy5zDifPbIFrsCWrhkQDZ0nA4seEYA5vkxzaGRIJaJCSAtuQcA/klsd2Y4ZxE8ozDBLTI9+cYf86pAYZY8+FjD3+U2dj2t7c/fIBPCxSAjGVsYOZhkACFCFEA4hLGH8SpHgWjYBSMghEGADhKTLDQtG5QAAAAAElFTkSuQmCC","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":true,"prefix":"","firstName":"Zahi","middleName":"","lastName":"Mizrahi","suffix":""},{"id":512712007,"identity":"21b182d9-88bf-42fd-a4f6-bc46a2bf14d0","order_by":1,"name":"Michael Fire","email":"","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Fire","suffix":""},{"id":512712008,"identity":"1719611f-3cff-4720-b48c-53ecd5125439","order_by":2,"name":"Shai Berkovitz","email":"","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":false,"prefix":"","firstName":"Shai","middleName":"","lastName":"Berkovitz","suffix":""},{"id":512712009,"identity":"e2b376c7-21db-4cd4-8389-fa97dd802e70","order_by":3,"name":"Nimrod Talmon","email":"","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":false,"prefix":"","firstName":"Nimrod","middleName":"","lastName":"Talmon","suffix":""}],"badges":[],"createdAt":"2025-08-02 09:38:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7277181/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7277181/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95528294,"identity":"d73f165e-4f36-4cbc-b949-cc126c9b13fc","added_by":"auto","created_at":"2025-11-10 10:15:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2491909,"visible":true,"origin":"","legend":"","description":"","filename":"VotePredictionforsubmitfinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7277181/v1_covered_b8c32a4a-ef4a-4716-a696-5d4a8d10b8eb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Framework of Voting Prediction of Parliament Members","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Votes Prediction, Open Parliament, Political Science, Big Data, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-7277181/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7277181/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTracking lawmakers’ voting behavior is vital for government transparency. Although many parliaments publish voting records online, these datasets are often complex and difficult to analyze. This research presents the Voting Prediction Framework (VPF), a machine learning-based system designed to predict parliamentary voting outcomes at both the individual legislator and bill levels across multiple countries. VPF aims to support legislative transparency, streamline parliamentary work, and aid in policy refinement by identifying bills with a low likelihood of passing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe framework comprises three main components: (1) Data Collection – retrieving voting records from various countries using APIs, web crawlers, and structured databases; (2) Parsing and Feature Integration – enriching data with relevant features such as legislator seniority and bill content; and (3) Prediction Models – applying machine learning algorithms to forecast individual votes and bill-level outcomes. 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