通过社交媒体自动检测亲密伴侣暴力受害者,积极提供支持
Yuting Guo1, Sangmi Kim2, Elise Warren3
1Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, United States.
概括
研究人员开发了一种机器学习系统,用于检测Reddit上的亲密伴侣暴力 (IPV) 自我报告. 这种社交媒体监控可以帮助识别受害者,并通过在线平台提供支持.
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
- 自然语言处理自然语言处理.
背景情况:
- 亲密伴侣暴力 (IPV) 受害者越来越多地利用社交媒体平台来分享经验和寻求支持.
- 这些数据的自动处理可以使基于社交媒体的监控和干预设计成为可能.
研究的目的:
- 开发一个监督的分类系统,用于在Reddit上自动描述与IPV相关的帖子.
- 评估使用机器学习来检测IPV在社交媒体上的自我报告的可行性.
主要方法:
- 在Reddit上收集了四个IPV相关的subreddits的数据.
- 手动注释了289个帖子,以区分IPV的自我报告.
- 训练并比较监督机器学习分类器,包括RoBERTa.
主要成果:
- 基于 RoBERTa 变压器的分类器实现了最高的性能.
- 在IPV自我报告类中获得了78%的整体准确度和0.67的F1得分.
- 错误分析表明,长篇文章和非第一人称报告存在挑战.
结论:
- 开发的分类方法在Reddit上检测IPV受害者方面显示出有希望的结果.
- 这种方法可以为IPV患者提供及时的支持和干预.
- 进一步的研究可以改进模型以应对分类挑战并提高检测准确性.
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