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

TL;DR

This study analyzes public comments from Michigan city council meetings using a data-driven framework to categorize local and societal concerns, revealing patterns and interactions in civic participation.

Contribution

It introduces a scalable machine learning framework to classify and analyze public comments across multiple cities, expanding understanding of civic engagement.

Findings

Identified key local concerns such as housing and sustainability.

Mapped societal concerns like democracy and anti-racism.

Demonstrated interactions between local and societal concerns.

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.