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Machine Learning for Understanding the Relationship Between Political Participation and Political Culture

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Advances in Artificial Intelligence and Applied Cognitive Computing

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Abstract

How might machine learning be employed to help promote the ethos of democratic citizenship in popular society? This chapter uses machine learning tools to better understand the role political culture plays in determining levels of political participation among citizens spread across our democratic republic. Although many studies have utilized political culture to explore rates of political participation along class, economic, gender, and other such cleavage lines, many scholars focusing on this framework often neglect the importance of race. In what regional political culture are we more likely to find higher levels of political participation among White and Black Americans? We investigate this question. We also seek to determine whether Black Americans have their own political culture that transcends previous understandings of regional political subcultures? We show that Elazar’s classification of regional political subcultures applies mostly to White Americans, and that political culture among Blacks do not adhere to geographical lines or region. Machine learning tools are deployed to sift through massive data and uncover patterns and structures embedded within it. The machine learning tools here were used to test model specification and will be used to improve its performance to better predict political participation among Americans. The information may be used by policymakers and concerned citizens in their effort to increase civic participation and combat political apathy.

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Acknowledgment

This work is partially supported by NSF Grants #1912588 and #1817282.

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Authors and Affiliations

  1. Fisk University, Nashville, TN, USA

    A. Hannibal Leach & Sajid Hussain

Authors
  1. A. Hannibal Leach
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  2. Sajid Hussain
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Corresponding author

Correspondence to Sajid Hussain .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Georgia, Athens, GA, USA

    Hamid R. Arabnia

  2. Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, Canada

    Ken Ferens

  3. Business Administration, University of Oviedo, Oviedo, Asturias, Spain

    David de la Fuente

  4. Institute of Informatics Problems, The Russian Academy of Sciences, Moscow, Russia

    Elena B. Kozerenko

  5. Technology and Information systems, Universidad de Castilla La Mancha, Ciudad Real, Ciudad Real, Spain

    José Angel Olivas Varela

  6. Facultad de Informática - CIC PBA, Universidad Nacional de La Plata, La Plata, Argentina

    Fernando G. Tinetti

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Leach, A.H., Hussain, S. (2021). Machine Learning for Understanding the Relationship Between Political Participation and Political Culture. In: Arabnia, H.R., Ferens, K., de la Fuente, D., Kozerenko, E.B., Olivas Varela, J.A., Tinetti, F.G. (eds) Advances in Artificial Intelligence and Applied Cognitive Computing. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-70296-0_23

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  • DOI: https://doi.org/10.1007/978-3-030-70296-0_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-70295-3

  • Online ISBN: 978-3-030-70296-0

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