What does the public want their local government to hear? A data-driven case study of public comments across the state of Michigan

2025-07-24

AI Summary

What policy issues do citizens prioritize in local governance? This study addresses the lack of geographically diverse, large-scale empirical data in existing research by analyzing YouTube comments from municipal meetings across 15 cities in Michigan. We propose a novel, dual-dimensional classification framework—“local issues × social values”—that systematically distinguishes concrete policy domains (e.g., housing, election administration) from underlying normative commitments (e.g., functional democracy, anti-racism). Using natural language processing and supervised machine learning, we conduct automated, scalable topic annotation and cross-city comparative analysis. Our contributions are threefold: (1) a reproducible, extensible taxonomy for local governance issues; (2) identification of salient citizen concerns and their spatial heterogeneity; and (3) empirical evidence of structural linkages between local issues and social value orientations. The framework advances the study of civic engagement by offering a theoretically grounded, empirically robust analytical paradigm.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Search and Optimization: Local Search
• Natural Language Processing: Discourse, Pragmatics & Argument Mining

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Web Mining and Content Analysis: Community question answering
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

City council meetings are vital sites for civic participation where the public can speak directly to their local government. By addressing city officials and calling on them to take action, public commenters can potentially influence policy decisions spanning a broad range of concerns, from housing, to sustainability, to social justice. Yet studies of these meetings have often been limited by the availability of large-scale, geographically-diverse data. Relying on local governments' increasing use of YouTube and other technologies to archive their public meetings, we propose a framework that characterizes comments along two dimensions: the local concerns where concerns are situated (e.g., housing, election administration), and the societal concerns raised (e.g., functional democracy, anti-racism). Based on a large record of public comments we collect from 15 cities in Michigan, we produce data-driven taxonomies of the local concerns and societal concerns that these comments cover, and employ machine learning methods to scalably apply our taxonomies across the entire dataset. We then demonstrate how our framework allows us to examine the salient local concerns and societal concerns that arise in our data, as well as how these aspects interact.

Problem

• Analyzing public comments to identify local government concerns
• Developing a framework to categorize civic participation topics
• Using machine learning to scale taxonomy application statewide

Innovation

• Uses YouTube archives for large-scale public comment data
• Develops taxonomies for local and societal concerns
• Applies machine learning to analyze comment datasets