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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Predicting party switching through machine learning and open data.

Nicolò Meneghetti1,2, Fabio Pacini3,4, Francesca Biondi Dal Monte3

  • 1The Biorobotics Institute, Scuola Superiore Sant'Anna, 56025 Pisa, Italy.

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|July 7, 2023
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Summary
This summary is machine-generated.

Predicting political party switching is now possible using machine learning and open parliamentary data. An algorithm achieved over 70% accuracy in forecasting party switches up to two months in advance.

Keywords:
Applied sciencesNetworkSocial sciences

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Area of Science:

  • Political Science
  • Computational Social Science
  • Data Science

Background:

  • Parliamentary dynamics can be unpredictable, impacting policy design.
  • Predicting voting patterns requires advanced analytical tools and data.
  • Open data and machine learning offer potential solutions for legislative analysis.

Purpose of the Study:

  • To develop and validate a machine learning algorithm for predicting party switching in the Italian Parliament.
  • To analyze legislative voting data to identify patterns preceding party switches.
  • To demonstrate the utility of open political data and ML in understanding political dynamics.

Main Methods:

  • Utilized voting data from the XVII (2013-2018) and XVIII (2018-2022) Italian legislatures.
  • Developed a predictive algorithm based on machine learning techniques.
  • Analyzed voting behavior, focusing on participation in secret ballots and party vote coherence.

Main Results:

  • The algorithm achieved over 70% accuracy in predicting party switches up to two months in advance.
  • Party switchers demonstrated higher participation in secret ballots.
  • A progressive decrease in coherence with the party's majority votes was observed in switchers prior to their defection.

Conclusions:

  • Machine learning combined with open political data can effectively predict party switching.
  • Understanding pre-switch voting behavior offers insights into political dynamics.
  • This approach can support evidence-based policy design through scenario simulation.