从社交媒体上的自由文本描述中识别亲密伴侣暴力
Phan Trinh Ha1, Rhea D'Silva2, Ethan Chen2
1Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA.
Journal of computational social science
|April 30, 2025
概括
使用机器学习,DetectIPV在自由文本中准确地识别了亲密伴侣暴力 (IPV). 该工具在检测身体,情感和性虐待方面表现出很高的表现,有助于公共卫生研究和社交媒体监控.
科学领域:
- 公共卫生 公共卫生
- 计算机科学 计算机科学
- 计算社会科学 计算社会科学
背景情况:
- 亲密伴侣暴力 (IPV) 是一个广泛的公共卫生问题,由于缺乏认可,经常被低估,特别是非物理形式.
- 社交媒体平台为了解关系问题和识别IPV提供了一个独特的,尽管是非结构化的数据来源.
- 在自由文本中对IPV进行大规模分析受到标记数据集的缺失的阻碍.
研究的目的:
- 开发一种用于检测亲密伴侣暴力 (IPV) 的计算工具.
- 利用在已建立的IPV问卷上训练的机器学习模型来分析社交媒体和其他文本数据.
- 从自由文本中检测到的IPV的流行率和类型的特征.
主要方法:
- 利用通用句子编码器 (USE) 和机器学习算法 (随机森林,SVM,后勤回归,天真贝叶斯) 来构建DetectIPV工具.
- 训练有素的模型使用已建立的IPV问卷中的自我报告数据从自由文本预测IPV.
- 评估了一般IPV检测模型和特定类型的物理,情感和性虐待模型的性能.
主要成果:
- 一般的DetectIPV模型实现了89%的ROC曲线下的区域 (AUROC) 在自由文本中检测IPV.
- 特定类型的模型对身体虐待 (98%AUROC) 具有很高的准确性,对性虐待具有显著的准确性.
- 情绪滥用检测虽然最具挑战性,但却取得了80%以上的AUROC,这表明DetectIPV的广泛适用性.
结论:
- DetectIPV是一个可靠的工具,用于检测各种形式的亲密伴侣暴力.
- 该工具在标记社交媒体内容和分析IPV研究的大型文本大体方面具有潜在的应用.
- 这种计算方法提高了研究IPV流行,原因和后果的能力.
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