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

IF 3.1 Q1 CRIMINOLOGY & PENOLOGY Crime Science Pub Date : 2024-02-04 DOI:10.1186/s40163-024-00200-2
George Karystianis, Nabila Chowdhury, Lorraine Sheridan, Sharon Reutens, Sunny Wade, Stephen Allnutt, Min-Taec Kim, Suzanne Poynton, Tony Butler
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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.

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对家庭暴力警察叙述进行文本挖掘,以确定与强制控制有关的行为
摘要 背景和环境 家庭暴力(DFV)是一个严重的社会问题,主要影响妇女和儿童。胁迫性控制是一种与家庭暴力相关的行为。虽然提出了各种不同的定义,但它涉及 "攻击、威胁、羞辱和恐吓行为或其他用于伤害、惩罚或恐吓受害者的虐待行为",包括情感虐待、社会虐待和经济虐待。警方报告可能是有关胁迫性控制的一个丰富信息来源。在本文中,我们将确定是否有可能从 DFV 警方报告中自动识别与胁迫性控制有关的行为,并按年龄和性别列出此类行为的发生率。 方法 我们修改了现有的基于规则的文本挖掘方法,从 406196 份 DFV 报告中识别出 48 种与胁迫控制有关的行为,这些报告涉及 2009 年至 2020 年期间新南威尔士州警方记录中的单个相关人员(即涉嫌或被指控 DFV 犯罪的个人)针对单个受害者的行为。 结果 223,778 起(54.6%)家庭暴力事件至少有一种可识别的胁迫性控制行为,其中最常见的行为是辱骂(38.9%),其次是财产损失(30.0%)。经济虐待(3.2%)和社会虐待(0.4%)是最不常见的胁迫性控制行为。在胁迫性暴力事件的比例方面,男性和女性主要被害人或受害人之间没有发现重大差异。年龄最大的 POI 组别(≥ 65 岁)中与辱骂相关的行为比例最高(38.0%),而年龄最小的 POI 组别报告的涉及财产损失的 DFV 比例最高(45.5%)。最年轻的受害者群体(< 18 岁)报告的涉及辱骂的家庭暴力事件比例最高(37.3%),而 18 至 24 岁的受害者报告的通过电话和短信进行骚扰的比例最高(分别为 3.1% 和 2.4%);是年龄最大(≥ 65 岁)的受害者群体(分别为 1.4% 和 0.7%)的两倍。 结论 警方数据记录了与胁迫性控制有关的各种行为,提供了跨越年龄和性别的洞察力。文本挖掘可用于检索此类信息。然而,社会虐待和经济虐待的记录并不常见,这就强调了有必要加强警方培训,鼓励他们在参加家庭暴力活动时询问此类行为。
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来源期刊
Crime Science
Crime Science Social Sciences-Cultural Studies
CiteScore
11.90
自引率
8.20%
发文量
12
审稿时长
13 weeks
期刊介绍: Crime Science is an international, interdisciplinary, peer-reviewed journal with an applied focus. The journal''s main focus is on research articles and systematic reviews that reflect the growing cooperation among a variety of fields, including environmental criminology, economics, engineering, geography, public health, psychology, statistics and urban planning, on improving the detection, prevention and understanding of crime and disorder. Crime Science will publish theoretical articles that are relevant to the field, for example, approaches that integrate theories from different disciplines. The goal of the journal is to broaden the scientific base for the understanding, analysis and control of crime and disorder. It is aimed at researchers, practitioners and policy-makers with an interest in crime reduction. It will also publish short contributions on timely topics including crime patterns, technological advances for detection and prevention, and analytical techniques, and on the crime reduction applications of research from a wide range of fields. Crime Science publishes research articles, systematic reviews, short contributions and theoretical articles. While Crime Science uses the APA reference style, the journal welcomes submissions using alternative reference styles on a case-by-case basis.
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