发表机构
Virginia Tech; Drexel University（弗吉尼亚理工大学; 德雷塞尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究构建SCOPE数据集，分析Reddit上性暴力幸存者叙事的污名类型与对应评论的支持类型，发现内化型污名最普遍，污名影响叙事与同伴回应，对在线系统相关工作有启示。
AI 中文摘要
在线社区逐渐成为性暴力幸存者分享经历并寻求支持的空间。尽管已有研究分别探讨了污名与社会支持，但幸存者叙事中表达的污名与回应时提供的支持之间的关联尚不明确。我们推出SCOPE数据集，将在线幸存者叙事中的污名信号与对应评论线程中的支持类型关联起来。我们使用多维污名分类法标注帖子，包括经历型污名、内化型污名、预期型污名和结构性污名；并使用支持分类法标注评论，涵盖信息支持、情感支持、尊重支持、实际帮助和群体互动。通过语境、语言和情感分析，我们比较了含污名与非污名内容，发现含污名叙事更强调内化痛苦，而非污名叙事则更侧重解读情境与经历。内化型污名是最普遍的类别，且社区回应在各类污名中基本保持稳定，信息支持与尊重支持出现最频繁。这些发现揭示了污名如何塑造幸存者叙事与同伴回应，对计算建模、内容审核及更安全的在线系统具有启示意义。
英文摘要
Online communities increasingly provide spaces where survivors of sexual violence can share their experiences and seek support. Although prior research has examined stigma and social support separately, less is known about how stigma expressed in survivor narratives relates to the support offered in response. We introduce the SCOPE dataset, linking stigma signals in online survivor narratives to support types in corresponding comment threads. We annotate posts using a multi-dimensional stigma taxonomy, including Experienced, Internalized, Anticipated, and Structural Stigma, and comments using a support taxonomy encompassing Information Support, Emotional Support, Esteem Support, Tangible Assistance, and Group Interaction. Using contextual, linguistic, and emotion analyses, we compare Stigma and No Stigma content and find that Stigma narratives place greater emphasis on internalized distress, whereas No Stigma narratives focus more on interpreting situations and experiences. Internalized Stigma is the most prevalent category, and community responses remain broadly stable across stigma types, with Information and Esteem Support appearing most often. These findings show how stigma shapes survivor narratives and peer responses and have implications for computational modeling, content moderation, and safer online systems.
Comments37th ACM Conference on Hypertext (HT '26)
