Karystianis George, Adily Armita, Schofield Peter W, Greenberg David, Jorm Louisa, Nenadic Goran, Butler Tony
The Kirby Institute, Faculty of Medicine, The University of New South Wales, Sydney, Australia.
Neuropsychiatry Service, Hunter New England Health, Newcastle, Australia.
J Med Internet Res. 2019 Mar 12;21(3):e13067. doi: 10.2196/13067.
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.
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.
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.
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.
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.
警方每年都会处理大量家庭暴力事件，并将这些事件的详细信息记录为结构化（编码）数据和非结构化的自由文本叙述。相关人员实施的虐待类型（包括身体、心理、情感和经济方面）以及受害者遭受的任何伤害通常会记录在冗长的描述性叙述中。
我们旨在确定一种自动文本挖掘方法是否能够从新南威尔士州警察局的一个大型警方数据集中的叙述中识别出家庭暴力受害者遭受的虐待类型和任何伤害。
我们使用了一组200起已记录的家庭暴力事件作为训练集，基于文本中的句法模式设计了一种知识驱动的方法，然后将该方法应用于一大组警方报告。
在一组100起家庭暴力事件的评估集上测试我们的方法，虐待类型和受害者伤害的精确率分别为90.2%和85.0%。在一组492393份家庭暴力报告中，我们发现71.32%（351178起）的事件提到了虐待类型，超过三分之一（177117起事件；35.97%）包含受害者受伤情况。“情感/言语虐待”（33.46%；117488起）是最常见的虐待类型，其次是“殴打”（86322起事件；24.58%）和“财产损坏”（22.27%；78203起事件）。“瘀伤”是最常见的受伤形式（51455起事件；29.03%），“割伤/擦伤”（28.93%；51284起事件）和“红色印记/迹象”（23.71%；42038起事件）分别排名第二和第三。
结果表明，文本挖掘可以从警方记录的家庭暴力事件中自动提取信息，这些信息可支持对家庭暴力的进一步公共卫生研究，例如研究虐待类型与受害者伤害之间的关系，以及性别和虐待类型与家庭暴力受害者风险升级之间的关系。提取的这些信息还有可能与心理健康状况信息相联系。
