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Predicting party switching through machine learning and open data

Parliament dynamics might seem erratic at times. Predicting future voting patterns could support policy design based on the simulation of voting scenarios. The availability of open data on legislative activities and machine learning tools might enable such prediction. In our paper, we provide eviden...

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Detalles Bibliográficos
Autores principales: Meneghetti, Nicolò, Pacini, Fabio, Biondi Dal Monte, Francesca, Cracchiolo, Marina, Rossi, Emanuele, Mazzoni, Alberto, Micera, Silvestro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10319836/
https://www.ncbi.nlm.nih.gov/pubmed/37416469
http://dx.doi.org/10.1016/j.isci.2023.107098
Descripción
Sumario:Parliament dynamics might seem erratic at times. Predicting future voting patterns could support policy design based on the simulation of voting scenarios. The availability of open data on legislative activities and machine learning tools might enable such prediction. In our paper, we provide evidence for this statement by developing an algorithm able to predict party switching in the Italian Parliament with over 70% accuracy up to two months in advance. The analysis was based on voting data from the XVII (2013–2018) and XVIII (2018–2022) Italian legislature. We found party switchers exhibited higher participation in secret ballots and showed a progressive decrease in coherence with their party’s majority votes up to two months before the actual switch. These results show how machine learning combined with political open data can support predicting and understanding political dynamics.