Stigma and Support in Online Sexual Violence Narratives on Reddit

This paper introduces the SCOPE (Stigma and COmmunity Peer Expressions) dataset, which links stigma signals in online sexual violence survivor narratives on Reddit to the types of support offered in corresponding comment threads. The dataset includes 3,675 posts and 5,131 comments from three subreddits: r/meToo, r/SexualHarassment, and r/sexualassault. Posts are annotated using a multi-dimensional stigma taxonomy including Experienced, Internalized, Anticipated, and Structural Stigma, while comments are annotated using a support taxonomy encompassing Information Support, Emotional Support, Esteem Support, Tangible Assistance, and Group Interaction. The authors used a multi-stage annotation pipeline with iterative rounds, achieving substantial inter-annotator agreement, and then scaled annotations using a K-shot in-context learning approach with Gemini 2.0 Flash, which achieved the best performance (F1 = 0.957 for relevance, 0.820 for Stigma vs. No Stigma, and 0.773 for fine-grained stigma classification).

The analyses reveal that Internalized Stigma is the most prevalent category (1,809 posts) and is associated with the highest volume of support responses. Across all stigma categories, Informational and Esteem Support are the most common forms of response, while Tangible Support remains relatively rare. The contextual analysis using LLooM concept discovery shows that "Stigma-related narratives are primarily characterized by themes of internalized distress and interpersonal harm, including internalized self-blame and minimization following sexual violence, internalized blame and self-silencing, fear of disclosure, and intimacy avoidance due to trauma, whereas No Stigma posts are more oriented toward situational interpretation and clarification, including recollections of past sexual assault, impact of trauma on functioning and well-being, seeking clarification on sexual violence, and navigating ambiguous sexual encounters."

Linguistic analysis using LIWC reveals that "posts tagged as Internalized, Experienced, and Anticipated Stigma exhibit higher Authenticity (84.20) and lower Analytic scores (10.00) than Structural Stigma (Authentic = 76.49, Analytic = 16.49), reflecting a more emotionally confessional style compared to the more analytical framing of structural barriers. No Stigma posts fall between these extremes. At the comment level, Esteem Support exhibits the highest Clout across categories... suggesting that affirmation-based responses are particularly assertive. Group Interaction shows the highest Authenticity, reflecting the personal and experiential nature of peer engagement, while Information Support has the highest Analytic scores, consistent with its instructional and solution-oriented role."

Emotion analysis using the NRC Emotion Lexicon shows that "narratives labeled as Internalized, Experienced, Anticipated Stigma exhibit relatively higher proportions of negative emotions such as sadness, fear, and anger, reflecting the distress and personal vulnerability associated with lived experiences of stigma, while Structural posts show a more moderated emotional profile... indicating discussions that are less personal and more systemic in nature, while No Stigma posts display higher levels of trust, anticipation, and joy."

The central finding is "the relative stability of community support norms. Across Stigma and No Stigma posts, commenters rely on a broadly consistent repertoire of Information, Emotional Support, and Esteem Support, as well as Tangible Assistance and Group Interaction, with differences arising mainly in emphasis rather than in kind. Specifically, Internalized, Experienced, and Anticipated Stigma receive slightly more Esteem Support (26.1%), Structural Stigma shows higher Tangible Support (7%), and No Stigma posts have the most Information Support (38.7%). The authors conclude that while stigma deeply shapes how survivors narrate their experiences, community responses remain structurally stable even as those narratives differ, and they suggest that the goal should not be to generate highly customized responses for every stigma subtype, but to leverage datasets like SCOPE to detect and amplify timely, safe, and norm-congruent forms of peer assistance."

Improvements to AI Systems:

1. Stigma-Aware Support Response Generation: Train a peer-support AI (e.g., for crisis chat or online community moderation) to detect the specific stigma subtype (Internalized, Experienced, Anticipated, Structural) in a survivor’s narrative, then generate responses that prioritize Esteem Support (affirmation, validation) for Internalized/Experienced/Anticipated stigma, and Tangible Assistance (concrete resources, legal/medical info) for Structural stigma—rather than using a one-size-fits-all empathetic template.

2. Norm-Congruent Response Ranking: Build a re-ranking module for AI-generated comments in support forums that scores candidate replies against the stable community support norms found (Information, Emotional, Esteem, Tangible, Group Interaction). The AI would favor responses that mix Informational and Esteem Support (the most common) and avoid over-customizing to stigma subtype, ensuring safety and norm alignment.

3. Linguistic Style Adaptation for Stigma Narratives: Implement a style-transfer layer in AI writing tools that adjusts tone based on detected stigma type—e.g., using higher Authenticity (confessional, first-person, emotionally direct) for Internalized/Experienced/Anticipated stigma posts, and a more Analytical, systemic tone for Structural stigma posts—matching the linguistic patterns (LIWC) observed in human narratives.

4. Emotion-Contrastive Empathy Calibration: Enhance AI emotional intelligence by training a classifier that distinguishes between high-distress stigma narratives (sadness, fear, anger) and lower-distress, systemic or ambiguous posts (trust, anticipation). The AI would then modulate its emotional mirroring—offering deeper validation for high-negative-emotion posts and more clarifying, solution-oriented language for lower-emotion posts.

5. Support Type Prediction for Threads: Develop a predictive model that, given a survivor’s post, forecasts the distribution of support types the community is likely to provide (e.g., high Esteem for Internalized, high Information for No Stigma). This enables proactive AI moderation to fill gaps—e.g., injecting Tangible Support when predicted Tangible is low, or amplifying Group Interaction for isolated users.

6. Safe Disclosure Facilitation: Build an AI assistant that uses the stigma taxonomy to guide survivors in articulating their experiences—prompting for self-blame minimization (Internalized), fear of disclosure (Anticipated), or systemic barriers (Structural)—while simultaneously suggesting peer-support phrases that are norm-congruent, reducing the risk of harmful or invalidating responses.

What the Improved AI System Can Do:

• Automatically classify a survivor’s post into stigma subtypes and generate responses that match the most effective, community-validated support style (e.g., validation-heavy for Internalized, resource-heavy for Structural).

• Rank and filter AI-generated peer support in real-time to ensure it aligns with the stable norms of human communities, preventing over-customization that could feel robotic or off-topic.

• Adapt its writing style (confessional vs. analytical) and emotional intensity to mirror the survivor’s linguistic profile, improving rapport and perceived empathy.

• Predict and proactively fill support gaps in online threads (e.g., adding Tangible Assistance when it’s rare) to improve overall community response quality.

• Provide a safe, stigma-aware drafting tool for survivors that helps them express their experience while receiving norm-congruent, supportive feedback—reducing the risk of retraumatization or unhelpful replies.

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.