scholarly journals Automated Analysis of Domestic Violence Police Reports to Explore Abuse Types and Victim Injuries: Text Mining Study

10.2196/13067 ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. e13067 ◽  
Author(s):  
George Karystianis ◽  
Armita Adily ◽  
Peter W Schofield ◽  
David Greenberg ◽  
Louisa Jorm ◽  
...  
Keyword(s):  
Domestic Violence ◽  
Text Mining ◽  
Automated Analysis ◽  
Police Reports

scholarly journals Automated Analysis of Domestic Violence Police Reports to Explore Abuse Types and Victim Injuries: Text Mining Study (Preprint)

2018 ◽  
Author(s):  
George Karystianis ◽  
Armita Adily ◽  
Peter W Schofield ◽  
David Greenberg ◽  
Louisa Jorm ◽  
...  
Keyword(s):  
Domestic Violence ◽  
Text Mining ◽  
Automated Analysis ◽  
Public Health Research ◽  
Free Text ◽  
Large Set ◽  
South Wales ◽  
Police Reports ◽  
Relationship Of ◽  
The Relationship

BACKGROUND The police attend numerous domestic violence events each year, recording details of these events as both structured (coded) data and unstructured free-text narratives. Abuse types (including physical, psychological, emotional, and financial) conducted by persons of interest (POIs) along with any injuries sustained by victims are typically recorded in long descriptive narratives. OBJECTIVE We aimed to determine if an automated text mining method could identify abuse types and any injuries sustained by domestic violence victims in narratives contained in a large police dataset from the New South Wales Police Force. METHODS We used a training set of 200 recorded domestic violence events to design a knowledge-driven approach based on syntactical patterns in the text and then applied this approach to a large set of police reports. RESULTS Testing our approach on an evaluation set of 100 domestic violence events provided precision values of 90.2% and 85.0% for abuse type and victim injuries, respectively. In a set of 492,393 domestic violence reports, we found 71.32% (351,178) of events with mentions of the abuse type(s) and more than one-third (177,117 events; 35.97%) contained victim injuries. “Emotional/verbal abuse” (33.46%; 117,488) was the most common abuse type, followed by “punching” (86,322 events; 24.58%) and “property damage” (22.27%; 78,203 events). “Bruising” was the most common form of injury sustained (51,455 events; 29.03%), with “cut/abrasion” (28.93%; 51,284 events) and “red marks/signs” (23.71%; 42,038 events) ranking second and third, respectively. CONCLUSIONS The results suggest that text mining can automatically extract information from police-recorded domestic violence events that can support further public health research into domestic violence, such as examining the relationship of abuse types with victim injuries and of gender and abuse types with risk escalation for victims of domestic violence. Potential also exists for this extracted information to be linked to information on the mental health status.

scholarly journals Prevalence of Mental Illnesses in Domestic Violence Police Records: Text Mining Study (Preprint)

2020 ◽  
Author(s):  
George Karystianis ◽  
Annabeth Simpson ◽  
Armita Adily ◽  
Peter Schofield ◽  
David Greenberg ◽  
...  
Keyword(s):  
Domestic Violence ◽  
Mental Illness ◽  
Text Mining ◽  
New South ◽  
Mental Illnesses ◽  
Free Text ◽  
South Wales ◽  
Police Records ◽  
Health Justice ◽  
Improve Health

BACKGROUND The New South Wales Police Force (NSWPF) records details of significant numbers of domestic violence (DV) events they attend each year as both structured quantitative data and unstructured free text. Accessing information contained in the free text such as the victim’s and persons of interest (POI's) mental health status could be useful in the better management of DV events attended by the police and thus improve health, justice, and social outcomes. OBJECTIVE The aim of this study is to present the prevalence of extracted mental illness mentions for POIs and victims in police-recorded DV events. METHODS We applied a knowledge-driven text mining method to recognize mental illness mentions for victims and POIs from police-recorded DV events. RESULTS In 416,441 police-recorded DV events with single POIs and single victims, we identified 64,587 events (15.51%) with at least one mental illness mention versus 4295 (1.03%) recorded in the structured fixed fields. Two-thirds (67,582/85,880, 78.69%) of mental illnesses were associated with POIs versus 21.30% (18,298/85,880) with victims; depression was the most common condition in both victims (2822/12,589, 22.42%) and POIs (7496/39,269, 19.01%). Mental illnesses were most common among POIs aged 0-14 years (623/1612, 38.65%) and in victims aged over 65 years (1227/22,873, 5.36%). CONCLUSIONS A wealth of mental illness information exists within police-recorded DV events that can be extracted using text mining. The results showed mood-related illnesses were the most common in both victims and POIs. Further investigation is required to determine the reliability of the mental illness mentions against sources of diagnostic information.

scholarly journals Entrapment and institutional collusion: Domestic violence police reports and the ‘couple rule’ in social security law

2018 ◽  
Vol 44 (1) ◽  
pp. 17-22
Author(s):  
Lyndal Sleep
Keyword(s):  
Domestic Violence ◽  
Social Security ◽  
Social Welfare ◽  
Couple Relationship ◽  
Violent Relationships ◽  
Social Security Payment ◽  
The Social ◽  
Police Reports ◽  
Social Security Act

In Australia’s heavily targeted social welfare apparatus, couples are assessed jointly for their eligibility for social security payment. Specific guidelines for deciding if a social security recipient is a member of a couple are provided by the ‘couple rule’ in section 4(3) of the Social Security Act 1991 (Cth). A plethora of information is used by the Department to decide if a social security recipient is a member of a couple for social security purposes. Of particular concern is the use of domestic violence police reports as evidence of a couple relationship. This article argues that the current use of police domestic violence reports in ‘couple rule’ decisions is problematic. This is because it effectively entraps women in violent relationships, provides a financial barrier to leaving and is used by perpetrators to further control their victims.

scholarly journals Automatic Extraction of Mental Health Disorders From Domestic Violence Police Narratives: Text Mining Study (Preprint)

2018 ◽  
Author(s):  
George Karystianis ◽  
Armita Adily ◽  
Peter Schofield ◽  
Lee Knight ◽  
Clara Galdon ◽  
...  
Keyword(s):  
Mental Health ◽  
Domestic Violence ◽  
Text Mining ◽  
Large Scale ◽  
New South ◽  
New South Wales ◽  
Mental Health Disorders ◽  
Free Text ◽  
South Wales ◽  
Health Disorders

BACKGROUND Vast numbers of domestic violence (DV) incidents are attended by the New South Wales Police Force each year in New South Wales and recorded as both structured quantitative data and unstructured free text in the WebCOPS (Web-based interface for the Computerised Operational Policing System) database regarding the details of the incident, the victim, and person of interest (POI). Although the structured data are used for reporting purposes, the free text remains untapped for DV reporting and surveillance purposes. OBJECTIVE In this paper, we explore whether text mining can automatically identify mental health disorders from this unstructured text. METHODS We used a training set of 200 DV recorded events to design a knowledge-driven approach based on lexical patterns in text suggesting mental health disorders for POIs and victims. RESULTS The precision returned from an evaluation set of 100 DV events was 97.5% and 87.1% for mental health disorders related to POIs and victims, respectively. After applying our approach to a large-scale corpus of almost a half million DV events, we identified 77,995 events (15.83%) that mentioned mental health disorders, with 76.96% (60,032/77,995) of those linked to POIs versus 16.47% (12,852/77,995) for the victims and 6.55% (5111/77,995) for both. Depression was the most common mental health disorder mentioned in both victims (22.30%, 3258) and POIs (18.73%, 8918), followed by alcohol abuse for POIs (12.24%, 5829) and various anxiety disorders (eg, panic disorder, generalized anxiety disorder) for victims (11.43%, 1671). CONCLUSIONS The results suggest that text mining can automatically extract targeted information from police-recorded DV events to support further public health research into the nexus between mental health disorders and DV.

scholarly journals Correction: Automatic Extraction of Mental Health Disorders From Domestic Violence Police Narratives: Text Mining Study

10.2196/13007 ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. e13007 ◽  
Author(s):  
George Karystianis ◽  
Armita Adily ◽  
Peter Schofield ◽  
Lee Knight ◽  
Clara Galdon ◽  
...  
Keyword(s):  
Mental Health ◽  
Domestic Violence ◽  
Text Mining ◽  
Mental Health Disorders ◽  
Automatic Extraction ◽  
Health Disorders

Text mining police narratives for mentions of mental disorders in family and domestic violence events

2021 ◽  
Author(s):  
Armita Adily ◽  
George Karystianis
Keyword(s):  
Domestic Violence ◽  
Text Mining ◽  
Mental Disorders ◽  
Research Evidence ◽  
Risk Groups ◽  
Free Text ◽  
Mining Method ◽  
Health Records ◽  
The Rich ◽  
Extract Information

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.

Nonfatal Strangulation During Domestic Violence Events in New South Wales: Prevalence and Characteristics Using Text Mining Study of Police Narratives

2021 ◽  
pp. 107780122110259
Author(s):  
Mandy Wilson ◽  
Erin Spike ◽  
George Karystianis ◽  
Tony Butler
Keyword(s):  
Domestic Violence ◽  
Text Mining ◽  
New South ◽  
Intimate Partner ◽  
Public Health Issue ◽  
South Wales ◽  
Police Data ◽  
Intimate Partner Relationships ◽  
Rich Information ◽  
Partner Relationships

Nonfatal strangulation (NFS) is a common form of domestic violence (DV) that frequently leaves no visible signs of injury and can be a portent for future fatality. A validated text mining approach was used to analyze a police dataset of 182,949 DV events for the presence of NFS. Results confirmed NFS within intimate partner relationships is a gendered form of violence. The presence of injury and/or other (non-NFS) forms of physical abuse, emotional/verbal/social abuse, and the perpetrator threatening to kill the victim, were associated with significantly higher odds of NFS perpetration. Police data contain rich information that can be accessed using automated methodologies such as text mining to add to our understanding of this pressing public health issue.

Text mining with emergent self organizing maps and multi-dimensional scaling: A comparative study on domestic violence

2011 ◽  
Vol 11 (4) ◽  
pp. 3870-3876 ◽  
Author(s):  
Jonas Poelmans ◽  
Marc M. Van Hulle ◽  
Stijn Viaene ◽  
Paul Elzinga ◽  
Guido Dedene
Keyword(s):  
Domestic Violence ◽  
Text Mining ◽  
Comparative Study ◽  
Self Organizing Maps ◽  
Dimensional Scaling ◽  
Multi Dimensional Scaling ◽  
Self Organizing
Sign in / Sign up

Export Citation Format

Share Document