By Armita Adily, George Karystianis and Tony Butler

Police attend numerous family and domestic violence (FDV) related events each year and record details of these events as both structured data and unstructured free-text narratives. These descriptive narratives include information about the types of abuse (eg physical, emotional, financial) and the injuries sustained by victims. However, this information is not used in research. In this paper we demonstrate the application of an automated text mining method to identify abuse types and victim injuries in a large corpus of NSW Police Force FDV event narratives (492,393) recorded between January 2005 and December 2016. Specific types of abuse and victim injuries were identified in 71.3 percent and 35.9 percent of FDV event narratives respectively. The most commonly identified abuse types mentioned in the narratives were non-physical (55.4%). Our study supports the application of text mining for use in FDV research and monitoring.

Trends & issues in crime and criminal justice no. 630. Canberra: Australian Institute of Criminology. 2021. 12p.

By Armita Adily, George Karystianis and Tony Butler

In this paper, we describe the feasibility of using a text-mining method to generate new insights relating to family and domestic violence (FDV) from free-text police event narratives. Despite the rich descriptive content of the event narratives regarding the context and individuals involved in FDV events, the police narratives are untapped as a source of data to generate research evidence. We used text mining to automatically identify mentions of mental disorders for both persons of interest (POIs) and victims of FDV in 492,393 police event narratives created between January 2005 and December 2016. Mentions of mental disorders for both POIs and victims were identified in nearly 15.8 percent (77,995) of all FDV events. Of all events with mentions of mental disorder, 76.9 percent (60,032) and 16.4 percent (12,852) were related to either POIs or victims, respectively. The next step will be to use actual diagnoses from NSW Health records to determine concordance between the two data sources. We will also use text mining to extract information about the context of FDV events among key at-risk groups.

Trends & issues in crime and criminal justice no. 629. Canberra: Australian Institute of Criminology. 2021. 16p.

By Hayley Boxall and Siobhan Lawler

A key assumption in the domestic violence literature is that abuse escalates in severity and frequency over time. However, very little is known about how violence and abuse unfolds within intimate relationships and there is no consensus on how escalation should be defined or how prevalent it is. A narrative review of the literature identified two primary definitions of escalation: a pattern of increasingly frequent and/or severe violent incidents, or the occurrence of specific violent acts (ie outcomes). Escalation appears to be limited to serious or prolific offenders rather than characterising all abusive relationships. However, disparities in prevalence estimates between those provided by victim–survivors and recorded incident data highlight the difficulty of measuring this aspect of abusive relationships.

Trends & issues in crime and criminal justice no. 626. Canberra: Australian Institute of Criminology 2021.  17p.

By Christopher Dowling, Hayley Boxall and Anthony Morgan

This study examines the officially recorded criminal careers of 2,076 domestic violence offenders and 9,925 non-domestic violence offenders in New South Wales in the 10 years following their first police proceeding. Group-based trajectory modelling was used to examine both domestic violence and non-domestic violence offending. Special attention is given to the degree of versatility in offending, and to the interplay of domestic violence and non-domestic violence offending trajectories. Domestic violence offending often formed part of a broader pattern of offending. While trajectories of low‑frequency domestic violence and non-domestic violence offending were most common, domestic violence typically increases as non-domestic violence offences begin to decline. Importantly, there was variability in the offending profiles of domestic violence offenders. This was amplified when non-domestic violence offending was analysed, indicative of a complex array of underlying risk factors.

Trends & issues in crime and criminal justice no. 624. Canberra: Australian Institute of Criminology. 2021. 17p.