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This study analyzes the relationship between stigma in online sexual violence narratives and the support provided in response, utilizing the newly introduced SCOPE dataset. By annotating survivor posts with a multi-dimensional stigma taxonomy and categorizing comment responses, the research reveals that narratives containing stigma emphasize internalized distress, while non-stigmatized narratives focus on situational interpretation. The findings indicate that despite the presence of stigma, community responses remain consistent, predominantly offering Information and Esteem Support, which has significant implications for online community dynamics and safety measures.
Stigma in survivor narratives amplifies internalized distress, yet community support remains surprisingly stable across different stigma types.
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.
