计算机文字分析方法在亲密伴侣暴力研究中的系统文献综述
Lilly Neubauer1, Isabel Straw1, Enrico Mariconti1
1University College London, Gower Street, London, WC1E 6BT UK.
Journal of family violence
|June 26, 2023
概括
计算式文本挖掘为在亲密伴侣暴力 (IPV) 研究中分析大型数据集提供了创新方法. 这一系统性审查强调了当前的应用和方法,作为未来研究的指南.
科学领域:
- 计算社会科学 计算社会科学
- 数字人文学科 数字人文学科
- 公共卫生信息学 公共卫生信息学
背景情况:
- 亲密伴侣暴力 (IPV) 研究面临着大型,非结构化数据集的挑战.
- 文本挖掘提供了一个计算方法来分析大量与IPV相关的文本数据.
- 现有的研究还没有全面审查IPV中文本挖掘的应用.
研究的目的:
- 提供当前学术研究的概述,将计算文本挖掘方法应用于亲密伴侣暴力 (IPV).
- 作为对在IPV研究中利用文本挖掘技术感兴趣的研究人员的起点.
- 在IPV研究中识别文本挖掘应用的趋势和差距.
主要方法:
- 进行了学术文献的系统审查.
- 一个以PRISMA为指导的协议为审查过程提供了信息.
- 在8个数据库中进行了全面的搜索,确定了IPV研究中计算文本挖掘的22项相关研究.
主要成果:
- 审查涵盖了多种方法,包括监督 (基于规则的分类,传统的机器学习,深度学习) 和无监督 (主题建模) 的方法.
- 数据集主要来自社交媒体 (15项研究),其他来源包括警察记录,医疗服务提供者数据和诉讼文本.
- 常见的评估指标是准确性和F1分数,通常使用持有测试集或k-fold交叉验证. 伦理方面的考虑很少被讨论.
结论:
- 计算式文本挖掘在亲密伴侣暴力 (IPV) 研究中为数据收集和分析提供了有希望的途径.
- 未来在IPV中采用计算方法的研究必须优先考虑和解决伦理问题.
- 这些发现强调了文本挖掘的潜力,以推进对IPV的理解和干预策略.
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