Examining resident interactions with local government on a digital participation platform

• In Event: Civic Engagement

Abstract

Digital participation platforms, such as 311 systems, are a direct avenue for residents to communicate non-emergency, public service requests to their local government. Many scholars have examined the spatial and demographic heterogeneity in resident requests and government response at the neighborhood level (Cook et al., 2024; Kontokosta and Hong, 2020; Minkoff, 2016; Wichowsky et al., 2022; Clark et al., 2019; White and Trump, 2018; Clark and Brudney, 2021) and reached different conclusions about disparities in use, bias in government response, and correlation with other forms of civic engagement. 

We contribute to this rich literature by examining digital platform use at the individual level. Using service request data (N = 1,891,360) from the digital 311 platform of a large city in the US from 2016-2024, we can link users to residential records, voting history, and political affiliation. We will characterize the residents who utilize 311 and examine their political affiliation and identification with local politics. Precise geographic and spatial data can also help us disentangle requests submitted where the resident lives, visits, or votes. Finally, we will examine how residents communicate on these digital platforms, using machine learning and artificial intelligence methods to classify the type and toxicity of language entered in open text fields on the digital platform.

Municipalities establish digital platform co-production systems (Clark et al., 2013) to promote civic engagement (Cho et al., 2021). To realize this synergy between residents and government, we need a robust understanding of the individual interactions. We hope that our research will empower municipalities to critically examine their 311 systems and outreach to promote greater civic trust, engagement, and equity.

Author

• Audrey Feldman, Harvard University
  Presenting Author