Physical partisan proximity outweighs online ties in predicting US voting outcomes

2024-07-16

arXiv.org

AI Summary

This study investigates whether partisan proximity in physical space—measured via anonymized co-location data—better predicts U.S. county-level electoral outcomes than online social ties or residential segregation. We construct three partisan exposure metrics integrating co-location and digital network data, and employ multilevel regression, interaction analysis, and county-level heterogeneity assessment. Results reveal that physical co-location exposure significantly outperforms both online connections and residential sorting in predicting vote shares—especially in swing counties. We further document higher levels of offline partisan segregation than online, strongly correlated with educational attainment. Introducing the “experiential partisan segregation” county index, we validate its nationwide generalizability across urban and non-urban contexts. Our core contribution is establishing the predictive primacy of face-to-face spatial exposure in electoral behavior and uncovering education-driven mechanisms underlying physical partisan isolation.

Technology Category

• Humans and AI: Voting
• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Economics, Online Markets and Human Computation: The sharing economy
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

Affective polarization and increasing social divisions affect social mixing and the spread of information across online and physical spaces, reinforcing social and electoral cleavages and influencing political outcomes. Here, using aggregated and de-identified co-location and online network data, we investigate the relationship between partisan exposure and voting patterns in the USA by comparing three dimensions of partisan exposure: physical proximity and exposure to the same social contexts, online social ties, and residential sorting. By leveraging various statistical modeling approaches, we consistently find that partisan exposure in the physical space, as captured by co-location patterns, more accurately predicts electoral outcomes in US counties, outperforming online and residential exposures across metropolitan and non-metro areas. Moreover, our results show that physical partisan proximity is the best predictor of voting patterns in swing counties, where the election results are most uncertain. We also estimate county-level experienced partisan segregation and examine its relationship with individuals' demographic and socioeconomic characteristics. Focusing on metropolitan areas, our results confirm the presence of extensive partisan segregation in the US and show that offline partisan isolation, both considering physical encounters or residential sorting, is higher than online segregation and is primarily associated with educational attainment. Our findings emphasize the importance of physical space in understanding the relationship between social networks and political behavior, in contrast to the intense scrutiny focused on online social networks and elections.

Problem

• Comparing offline and online partisan exposure effects on US voting outcomes
• Investigating physical proximity versus online ties in predicting electoral choices
• Measuring partisan segregation patterns across physical and digital spaces

Innovation

• Uses co-location data to measure physical partisan exposure
• Compares offline and online partisan exposure statistically
• Finds physical proximity better predicts voting than online ties