• Corpus ID: 231749563

Predicting Propensity to Vote with Machine Learning

  title={Predicting Propensity to Vote with Machine Learning},
  author={Rebecca D. pollard and Sara M. Pollard and Scott Streit},
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… 

Tables from this paper



Machine learning: Trends, perspectives, and prospects

The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.

Strategies for Predicting Whether a Citizen Will Vote and Estimation of Electoral Outcomes

A FREQUENT topic of discussion among those interested in public opinion research has been the estimation of electoral outcomes from preelection surveys. These estimates are important both for

Predicting and Interpolating State‐Level Polls Using Twitter Textual Data

This article combines 1,200 state-level polls during the 2012 presidential campaign with over 100 million state-located political tweets; models the polls as a function of the Twitter text using a new linear regularization feature-selection method; and shows that when properly modeled, the Twitter-based measures track and to some degree predict opinion polls.

Certain Problems in Election Survey Methodology

This article describes some solutions to common problems in pre-election surveys drawing upon Gallup Poll experience. It touches upon problems in sampling, estimation, response validity, the

Will Democracy Survive Big Data and Artificial Intelligence?

We are in the middle of a technological upheaval that will transform the way society is organized. We must make the right decisions now. Enlightenment is man’s emergence from his self-imposed

Predicting earnings from census data

  • Palo Alto, 2017. Accessed: Jan. 16, 2021. [Online]. Available: http://cs229.stanford.edu/proj2017/finalreports/5232542.pdf.
  • 2017

The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

This article shows how MCC produces a more informative and truthful score in evaluating binary classifications than accuracy and F1 score, by first explaining the mathematical properties, and then the asset of MCC in six synthetic use cases and in a real genomics scenario.

Integrated Public Use Microdata Series

  • S. Ruggles
  • Economics
    Encyclopedia of Gerontology and Population Aging
  • 2021

Integrated Public Use Microdata Series, Current Population Survey: Version 8.0 [dataset

  • Minneapolis, MN, 2020. Accessed: Feb. 11, 2021. [Online]. Available: https://doi.org/10.18128/D030.V8.0.
  • 2020

Igielnik, “Can Likely U.S. Voter Models Be Improved? | Pew Research Center.

  • Accessed: Jan
  • 2021