Citizen Complaints as an Accountability Mechanism: Uncovering Patterns Using Topic Modeling

Abstract

Objectives Citizen complaints are considered by policing researchers as an indicator of police misconduct, and a proxy of police-community relations. Nevertheless, US and EU-based studies tend to focus on sustained complaints as reported by official agencies and officer-based correlates. Using the case of Carabineros, the Chilean militarized police force, this study examines (a) latent topics contained in a large set of complaints against the police on a digital platform, and (b) the change of those topics across time and (c) by complainants’ educational level. Methods We use novel computational natural language processing techniques to identify latent themes across the corpus of complaints ( N = 1,623), hosted on an online forum from 2013 to 2020. Results Our findings show eight latent themes across the corpus. Among others, these themes were related to police effectiveness, police misbehavior, and a master frame of institutional crisis that has significantly grown over the last year. Additionally, differences in the prevalence of topics by complainants’ educational level were also found. Conclusions Our findings contribute to the enterprise of opening the black box of complaints against the police and highlighting opportunities for social accountability in a developing country.