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

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.