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Title: Predicting Propensity to Vote with Machine Learning

Citation Type: Miscellaneous

Publication Year: 2016

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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 (GOTV) 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.

Url: https://arxiv.org/pdf/2102.01535.pdf

User Submitted?: No

Authors: Pollard, Rebecca D; Pollard, Sara M; Streit, Scott E

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Data Collections: IPUMS CPS

Topics: Methodology and Data Collection, Other

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