Background: Social media is a rich, virtually untapped source of data on the dynamics of intimate partner violence, one that is both global in scale and intimate in detail. Objective: The aim of this study is to use machine learning and other computational methods to analyze social media data for the reasons victims give for staying in or leaving abusive relationships. Methods: Human annotation, part-of-speech tagging, and machine learning predictive models, including support vector machines, were used on a Twitter data set of 8767 #WhyIStayed and #WhyILeft tweets each. Results: Our methods explored whether we can analyze micronarratives that include details about victims, abusers, and other stakeholders, the actions that constitute abuse, and how the stakeholders respond. Conclusions: Our findings are consistent across various machine learning methods, which correspond to observations in the clinical literature, and affirm the relevance of natural language processing and machine learning for exploring issues of societal importance in social media.

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中文翻译：

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分析微博中亲密伴侣暴力的定量方法：观察性研究

背景：社交媒体是关于亲密伴侣暴力行为动态的丰富，几乎未开发的数据源，这种数据既具有规模，又具有全球性。目的：本研究的目的是使用机器学习和其他计算方法来分析社交媒体数据，以了解受害者给出的留在或离开虐待关系的原因。方法：在8767 #WhyIStayed和#WhyILeft推特的Twitter数据集上使用了人工注释，词性标注和机器学习预测模型（包括支持向量机）。结果：我们的方法探讨了我们是否可以分析微观叙事，其中包括受害者，施虐者和其他利益相关者的详细信息，构成虐待的行为以及利益相关者的反应。结论：

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