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

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.