通过自然语言处理改进警察记录的家庭暴力和虐待犯罪数据
Darren Cook1, Ruth Weir1, Leslie Humphreys2
1Violence and Society Centre, City University of London, London, United Kingdom.
Frontiers in sociology
|December 10, 2025
概括
机器学习模型显著提高了英国警察数据中对家庭暴力和虐待 (DVA) 关系类型的分类准确度. 这提高了公共卫生和保护工作的数据可靠性.
科学领域:
- 计算社会科学 计算社会科学
- 公共卫生信息学 公共卫生信息学
- 执法技术 执法技术 执法技术
背景情况:
- 家庭暴力和虐待 (DVA) 在英国是一个重大的公共卫生和保护挑战.
- 现有的警察记录数据质量问题阻碍了各机构有效的风险评估和干预设计.
- 准确地分类受害者与犯罪者的关系对于理解DVA动态至关重要.
研究的目的:
- 为了评估机器学习模型 (随机森林,DistilBERT) 在分类DVA事件关系类型中的有效性.
- 将机器学习性能与基于规则的DVA数据分类器进行比较.
- 评估选择性分类对提高对具有挑战性的DVA关系类别的精度的影响.
主要方法:
- 利用了大约19,000个英国警察记录的DVA事件.
- 训练并评估了用于关系分类的随机森林和蒸BERT模型 (前伴侣,现任伴侣,家庭).
- 实施了选择性分类策略,以管理低置信度预测并减少假阳性.
主要成果:
- 这两种机器学习模型在精度,回忆和F1分数方面都超过了基线分类器.
- 观察到平均绝对收益为精度11%和回忆率16%.
- 前合作伙伴的案例显示了最高的准确性,而当前合作伙伴的案例是最不准确的;选择性分类提高了困难类别的准确性.
结论:
- 计算工具,特别是机器学习,可以显著提高警察DVA数据的完整性和可靠性.
- 这些工具的应用需要在预测准确性,可解释性和保护性考虑之间保持谨慎的平衡.
- 通过机器学习提高DVA数据质量,支持更好的资源配置和干预策略.
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