Domestic Violence (DV) is a pervasive public health problem characterized by patterns of coercive and abusive behavior within intimate relationships. With the rise of social media as a key outlet for DV victims to disclose their experiences, online self-disclosure has emerged as a critical yet underexplored avenue for support-seeking. In addition, existing research lacks a comprehensive and nuanced understanding of DV self-disclosure, support provisions, and their connections. To address these gaps, this study proposes a novel computational framework for modeling DV support-seeking behavior alongside community support mechanisms. The framework consists of four key components: self-disclosure detection, post clustering, topic summarization, and support extraction and mapping. We implement and evaluate the framework with data collected from relevant social media communities. Our findings not only advance existing knowledge on DV self-disclosure and online support provisions but also enable victim-centered digital interventions.

翻译：家庭暴力是一种普遍存在的公共健康问题，以亲密关系中强制性和虐待性行为模式为特征。随着社交媒体成为家暴受害者披露经历的关键渠道，在线自我披露已成为寻求支持的重要但尚未充分探索的途径。此外，现有研究缺乏对家暴自我披露、支持供给及其关联的全面而细致的理解。为填补这些空白，本研究提出了一种新颖的计算框架，用于建模家暴求助行为及社区支持机制。该框架包含四个关键组成部分：自我披露检测、帖子聚类、主题概括以及支持抽取与映射。我们利用从相关社交媒体社区收集的数据实施并评估了该框架。研究结果不仅深化了现有关于家暴自我披露与在线支持供给的知识，还推动了以受害者为中心的数字干预措施的发展。