Predicting Propensity to Vote with Machine Learning
Maret School（马雷特学校）；Bullis School（布利斯学校）；Private Identity LLC（Private Identity 有限责任公司）；Villanova University（维拉诺瓦大学）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
