Text mining domestic violence police narratives to identify behaviours linked to coercive control

Abstract

Abstract Background and setting Domestic and family violence (DFV) is a significant societal problem that predominantly affects women and children. One behaviour that has been linked to DFV perpetration is coercive control. While various definitions have been proposed, it involves “acts of assault, threats, humiliation and intimidation or other abuse that is used to harm, punish, or frighten a victim” ranging from emotional to social and financial abuse. One potentially rich source of information on coercive control are police reports. In this paper we determine whether it is possible to automatically identify behaviours linked to coercive control from DFV police reports and present the prevalence of such behaviours by age and sex. Methods We modified an existing rule-based text mining method to identify 48 coercive control related behaviours from 406,196 DFV reports involving a single person of interest (POI) (i.e., an individual suspected or charged with a DFV offence) against a single victim from NSW Police Force records between 2009 and 2020. Results 223,778 (54.6%) DFV events had at least one identifiable coercive control behaviour with the most common behaviour being verbal abuse (38.9%) followed by property damage (30.0%). Financial (3.2%) and social abuse (0.4%) were the least common behaviours linked to coercive control. No major differences were found in the proportion of DFV events between male and female POIs or victims. The oldest POI group (≥ 65 years) had the largest proportion for behaviours related to verbal abuse (38.0%) while the youngest POI group reported the highest proportion of DFV involving property damage (45.5%). The youngest victim group (< 18 years old) had the highest proportion of DFV events involving verbal abuse (37.3%) while victims between 18 and 24 years old reported the most harassment through phone calls and text messages (3.1% and 2.4% respectively); double that of those in the oldest (≥ 65 years) victim group (1.4% and 0.7% respectively). Conclusions Police data capture a wide variety of behaviours linked to coercive control, offering insights across the age spectrum and sex. Text mining can be used to retrieve such information. However, social and financial abuse were not commonly recorded emphasising the need to improve police training to encourage inquiring about such behaviours when attending DFV events.