Predicting Propensity to Vote with Machine Learning
Physics and Society
2021-02-15 v3 Machine Learning
Abstract
We demonstrate that machine learning enables the capability to infer an individual's propensity to vote from their past actions and attributes. This is useful for microtargeting voter outreach, voter education and get-out-the-vote (GOVT) campaigns. Political scientists developed increasingly sophisticated techniques for estimating election outcomes since the late 1940s. Two prior studies similarly used machine learning to predict individual future voting behavior. We built a machine learning environment using TensorFlow, obtained voting data from 2004 to 2018, and then ran three experiments. We show positive results with a Matthews correlation coefficient of 0.39.
Cite
@article{arxiv.2102.01535,
title = {Predicting Propensity to Vote with Machine Learning},
author = {Rebecca D. Pollard and Sara M. Pollard and Scott Streit},
journal= {arXiv preprint arXiv:2102.01535},
year = {2021}
}
Comments
10 pages, 8 tables