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Title: Predicting party switching through machine learning and open data
Authors: Meneghetti, Nicolò
Pacini, Fabio 
Biondi Dal Monte, Francesca
Cracchiolo, Marina
Rossi, Emanuele
Mazzoni, Alberto
Micera, Silvestro
Journal: ISCIENCE 
Issue Date: 2023
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.
ISSN: 2589-0042
DOI: 10.1016/j.isci.2023.107098
Appears in Collections:A1. Articolo in rivista

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