Stigma and Support in Online Sexual Violence Narratives on Reddit

# Stigma and Support in Online Sexual Violence Narratives on Reddit
Source: [https://arxiv.org/html/2608.11433](https://arxiv.org/html/2608.11433)
## Stigma and Support in Online Sexual Violence Narratives on RedditConference:37th ACM Conference on Hypertext; September 14–18, 2026; London, United Kingdom37th ACM Conference on Hypertext \(HT ’26\), September 14–18, 2026, London, United KingdomDOI:[10\.1145/3800935\.3830853](https://doi.org/10.1145/3800935.3830853)ISBN:979\-8\-4007\-2564\-7/2026/09CCS:Human\-centered computingCCS:Social and professional topics GenderCCS:Applied computing

,Karan BindalAffiliation:Drexel University,Philadelphia,USAemail:[kb3887@drexel\.edu](mailto:[email protected]),Vaibhav GargNote:Both authors contributed equally\.Affiliation:Virginia Tech,Alexandria,USAemail:[vaibhavg@vt\.edu](mailto:[email protected])andRezvaneh RezapourAffiliation:Drexel University,Philadelphia,USAemail:[sr3563@drexel\.edu](mailto:[email protected])

2026; © cc

###### Abstract\.

Warning: This paper discusses sexual violence and may contain material that some readers, particularly survivors, may find distressing\.

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 theSCOPEdataset, linking stigma signals in online survivor narratives to support types in corresponding comment threads\. We annotate posts using a multi\-dimensional stigma taxonomy, includingExperienced, Internalized, Anticipated,andStructural Stigma, and comments using a support taxonomy encompassingInformation Support, Emotional Support, Esteem Support, Tangible Assistance,andGroup Interaction\. Using contextual, linguistic, and emotion analyses, we compareStigmaandNo Stigmacontent and find thatStigmanarratives place greater emphasis on internalized distress, whereasNo Stigmanarratives focus more on interpreting situations and experiences\.Internalized Stigmais the most prevalent category, and community responses remain broadly stable across stigma types, withInformationandEsteem Supportappearing 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\.

###### Keywords:

stigma, sexual violence, community support, large language models

††cc\-license:by## 1\.Introduction

Online platforms such as Reddit have become important socio\-technical spaces where individuals share lived experiences and seek advice or support from peers\([O’Neill 2018](https://arxiv.org/html/2608.11433#bib.bib39);[Andalibi et al\. 2016](https://arxiv.org/html/2608.11433#bib.bib6);[Alaggia and Wang 2020](https://arxiv.org/html/2608.11433#bib.bib4);[Bair 2022](https://arxiv.org/html/2608.11433#bib.bib8);[Bouzoubaa et al\. 2024b](https://arxiv.org/html/2608.11433#bib.bib11)\)\. While these interactions can foster support and solidarity, survivor narratives may also reveal the complex ways in which stigma is experienced, anticipated, and internalized, as well as how communities respond to these experiences\([Goffman 1963](https://arxiv.org/html/2608.11433#bib.bib26);[Kennedy and Prock 2018](https://arxiv.org/html/2608.11433#bib.bib29);[Stangl et al\. 2019](https://arxiv.org/html/2608.11433#bib.bib46);[Lanthier et al\. 2023](https://arxiv.org/html/2608.11433#bib.bib32);[Link and Phelan 2001](https://arxiv.org/html/2608.11433#bib.bib34)\)\. Understanding how stigma manifests in survivor narratives and how online communities respond is therefore critical to designing safer digital spaces and more supportive interventions\([Lanthier et al\. 2023](https://arxiv.org/html/2608.11433#bib.bib32);[Andalibi et al\. 2018](https://arxiv.org/html/2608.11433#bib.bib5)\)\.

Prior research has largely examined stigma and social support separately\. Studies of stigma have analyzed stigmatizing language in online narratives\([Bouzoubaa et al\. 2025](https://arxiv.org/html/2608.11433#bib.bib10);[Giorgi et al\. 2024](https://arxiv.org/html/2608.11433#bib.bib25)\)and investigated how narrative framing relates to stigma in mental health discussions\([Mittal and De Choudhury 2023](https://arxiv.org/html/2608.11433#bib.bib37);[Bouzoubaa et al\. 2026](https://arxiv.org/html/2608.11433#bib.bib12)\)\. In parallel, research on online social support has identified distinct forms of support and examined how they are expressed and exchanged within online communities\([De Choudhury and De 2014](https://arxiv.org/html/2608.11433#bib.bib17);[De Choudhury and Kiciman 2017](https://arxiv.org/html/2608.11433#bib.bib18);[Sharma and De Choudhury 2018](https://arxiv.org/html/2608.11433#bib.bib44);[Sharma et al\. 2020](https://arxiv.org/html/2608.11433#bib.bib43)\)\. However, limited work has jointly examined how stigma expressed in a post relates to the support provided in response, particularly across fine\-grained stigma categories within online communities for survivors of sexual violence\.

To address this gap, we introduceSCOPE\(Stigma andCOmmunityPeerExpressions\), a novel dataset of Reddit posts about sexual violence and their associated comment threads, annotated for stigma and community support\. Posts are labeled using a multilevel stigma taxonomy that distinguishesStigmafromNo Stigmaand further categorizes stigma asExperienced, Internalized, Anticipated, or Structural\([Bouzoubaa et al\. 2024a](https://arxiv.org/html/2608.11433#bib.bib9)\)\. Comments are annotated using the support taxonomy developed by[Lopes and Da Silva 2019](https://arxiv.org/html/2608.11433#bib.bib35), which capturesInformation, Emotional, Esteem, Tangible, andGroup\-Interactionsupport\. By linking survivor disclosures with community responses, our framework enables systematic study of how different forms of stigma relate to peer\-support strategies\.

Our analyses show that posts labeledStigmaemphasize validation, whereas posts labeled asNo Stigmalean toward informational and guidance\-oriented support; however, this distinction is not absolute, as support strategies remain largely consistent across both labels, differing mainly in emphasis\. At the post level, LIWC analysis reveals thatInternalized, Experienced, andAnticipated Stigmaexhibit higherAuthenticitybut lowerAnalyticscores thanStructural Stigma, reflecting more personal, subjective narratives, while posts labeled asNo Stigmafall between these groups\. At the comment level,Esteem Supportexhibits the highestClout,Group Interactionexhibits the highestAuthenticity, andInformation Supportshows the highestAnalyticscores, aligning with their respective communicative roles\. Emotion analysis also indicates greater emotional intensity in self\-blaming narratives than inStructural StigmaandNo Stigmacontent\. These findings demonstrate how stigma shapes survivor narratives and peer responses and highlight implications for computational modeling, content moderation, and the design of safer online environments\. We release the annotated dataset and code at[https://github\.com/ShirleneRose/Stigma\_SV](https://github.com/ShirleneRose/Stigma_SV)\.

CategoryDefinitionStigmaExperiencedDirect stigma from others, e\.g\., blame, disbelief, or dismissalInternalizedSelf\-directed stigma, such as shame, guilt, or self\-blameAnticipatedFear or expectation of judgment, blame, or negative reactions from othersStructuralStigma embedded in institutional or societal systems, such as legal or cultural barriersSupportInformation SupportAdvice, information, or guidance about resources or next stepsEmotional SupportExpressions of empathy or compassionEsteem SupportAffirmation of worth, validation, or encouragementTangible AssistanceOffers of concrete help or direct supportGroup InteractionReferences to shared experiences or community belonging

Table 1\.Definitions of stigma and support categories used in the annotation task\.
## 2\.Modeling Stigma and Support in Survivor Narratives

Stigma in Survivor Narratives\.Stigma is commonly understood as a socially discrediting attribute that shapes how individuals are perceived and treated\([Goffman 1963](https://arxiv.org/html/2608.11433#bib.bib26)\)\. Stigma is also framed as a multi\-level social process embedded in cultural norms, institutional practices, and power dynamics\([Link and Phelan 2001](https://arxiv.org/html/2608.11433#bib.bib34);[Aggleton et al\. 2003](https://arxiv.org/html/2608.11433#bib.bib2)\)\. Prior work identifies several key mechanisms of stigma, including enacted \(experienced discrimination\), anticipated \(expectations of judgment\), and internalized \(self\-directed stigma\)\([Earnshaw and Chaudoir 2009](https://arxiv.org/html/2608.11433#bib.bib21);[Corrigan et al\. 2005](https://arxiv.org/html/2608.11433#bib.bib15)\), as well as structural stigma, which reflects institutional and systemic barriers\([Stangl et al\. 2019](https://arxiv.org/html/2608.11433#bib.bib46);[Hatzenbuehler et al\. 2013](https://arxiv.org/html/2608.11433#bib.bib28)\)\. In the context of sexual violence, these forms of stigma often co\-occur in narratives; survivors may recount blame from others, express fear of disclosure, or articulate feelings of shame and distrust toward institutions\([Kennedy and Prock 2018](https://arxiv.org/html/2608.11433#bib.bib29);[Ullman 2023](https://arxiv.org/html/2608.11433#bib.bib49)\)\. Building on these frameworks, we adopt a taxonomy comprisingExperienced, Internalized, Anticipated, and Structural Stigma\([Bouzoubaa et al\. 2025](https://arxiv.org/html/2608.11433#bib.bib10)\), as these categories capture distinct dimensions of stigma observable in textual narratives\.

Support in Community Responses\.To characterize how communities respond to survivor narratives, we draw on an established framework of online social support\([Lopes and Da Silva 2019](https://arxiv.org/html/2608.11433#bib.bib35)\)\. This framework identifies functional types of support commonly observed in online health and crisis contexts\([De Choudhury and De 2014](https://arxiv.org/html/2608.11433#bib.bib17);[Sharma et al\. 2020](https://arxiv.org/html/2608.11433#bib.bib43)\)\. We considerInformation Support\(advice or guidance\),Emotional Support\(expressions of empathy and care\),Esteem Support\(affirmation of worth or validation\),Tangible Assistance\(offers of direct or practical help\), andGroup Interaction\(signals of shared experience or belonging\)\. These categories capture distinct functions that are particularly relevant to discussions of sexual violence, where survivors may seek validation, reassurance, or actionable guidance\. For example,Emotional Supportcan validate experiences, whileInformation Supportmay guide next steps, underscoring the importance of distinguishing between these categories in our analysis\. Table[1](https://arxiv.org/html/2608.11433#S1.T1)summarizes the stigma and support categories and their definitions\.

To illustrate these categories, we present two paraphrased examples from Reddit, highlighting color\-coded excerpts that reflect specific stigma and support types\. Example 1 shows multiple co\-occurring forms of stigma within a single narrative, while Example 2 demonstrates several forms of community support responses\.

Stigma Categories:Experienced StigmaInternalized StigmaAnticipated StigmaStructural StigmaExample 1: Stigma Excerpts from a Sexual Violence NarrativeI’ve been trying to get someone to see my story for six plus months with no success…He threatened to murder me and make an example of me for coming forward…I feared other forms of negative attention…I just feel so embarrassed and ashamed like I did something wrong…Support Categories:Information SupportEmotional SupportEsteem SupportTangible AssistanceGroup InteractionExample 2: Support Excerpts from Responses to a Sexual Violence NarrativeI am so sorry to hear about this\!…I’d recommend reaching out to RAINN\. It’s a free hotline\.…You are incredibly brave for sharing your story…DM me if you’d like to talk more about that…We hear you\. We believe you\. And you now have /all/ of us in your corner\.…

## 3\.Methodology

### 3\.1\.Data Collection

We built on a dataset of sexual violence narratives curated by[Garg et al\. 2025](https://arxiv.org/html/2608.11433#bib.bib23), which includes 5,328 posts from three subreddits:r/meToo,r/SexualHarassment, andr/sexualassault\. The dataset’s domain specificity and relevance to survivor disclosures make it well\-suited to our analysis\. To capture community responses, we collected direct replies to these posts using Python’s Reddit API Wrapper \(PRAW\)\. We performed standard preprocessing by removing empty posts and comments as well as those labeled as “\[deleted\]” or “\[removed\]\.” We also excluded posts without comments to ensure that each post had an interactional context for analyzing support\. The final dataset includes 3,675 posts and 5,131 comments\.

### 3\.2\.Data Annotation

#### 3\.2\.1\.Annotating Posts

We adopted a multi\-stage annotation pipeline to identify and characterize stigma in Reddit discussions of sexual violence\. Posts were first screened for relevance, then labeled asStigmaorNo Stigma, and finally annotated at a fine\-grained level to capture specific types of stigma\. Below, we describe each stage\.

Relevance Checking\.We screened posts for topical relevance and labeled them as ‘Relevant’ if they directly discussed sexual violence, and ‘Not Relevant’ otherwise\.

Stigma vs\. No Stigma\.After filtering for relevance, posts were assigned a binary label:Stigmaif they contained stigmatizing language, beliefs, or experiences, andNo Stigmaotherwise\.

Fine\-grained Stigma Labeling\.We applied fine\-grained annotation using the taxonomy in Table[1](https://arxiv.org/html/2608.11433#S1.T1), labeling posts asExperienced,Internalized,Anticipated, and/orStructural Stigma, allowing multiple labels per post\. Annotation was conducted iteratively, with disagreements resolved through discussion and guideline refinement until consensus was reached\. As shown in Table[2](https://arxiv.org/html/2608.11433#S3.T2), agreement improved across rounds, with initially subjective categories \(e\.g\.,Internalized Stigma,Anticipated Stigma\) reaching substantial agreement over time\.

#### 3\.2\.2\.Annotating Support in Comments

We annotated comments for support types using the taxonomy in Table[1](https://arxiv.org/html/2608.11433#S1.T1), allowing multiple labels per comment\. Annotation was conducted iteratively with disagreements resolved through discussion and guideline refinement\. As shown in Table[3](https://arxiv.org/html/2608.11433#S3.T3), agreement was generally high, with more explicit categories \(e\.g\.,Information Support\) showing stronger consistency and others \(e\.g\.,Esteem Support\) improving over time\.

Table 2\.Cohen’s kappa scores forStigmacategories \(κ\\kappa\)\.Table 3\.Cohen’s kappa scores for support categories \(κ\\kappa\)\.

### 3\.3\.LLM\-Based Classification and Scaling of Stigma and Support Annotations

To extend the annotated data, we applied aKK\-shot in\-context learning approach to classify stigma in posts and support in comments\. Each category was treated as a separate binary yes/no decision, allowing multi\-label assignments\.

Stigma Classification\.We first screened posts for relevance, labeling them as ‘Relevant’ if they directly discussed sexual violence\. Relevant posts were then assigned a coarse label ofStigmaorNo Stigma\. For fine\-grained stigma classification, each post was paired with the definition of a single target category \(Experienced,Internalized,Anticipated, orStructural\) along withK=5K=5semantically similar labeled examples\. We choseK=5K=5based on pilot experiments to balance contextual diversity with prompt length\. Examples were retrieved using cosine similarity over MPNet\-based SentenceTransformer embeddings\([Face 2025](https://arxiv.org/html/2608.11433#bib.bib22)\), ensuring semantic relevance and stylistic similarity\. The model followed a fixed procedure: it read the post, compared it to the category definition, and decided whether the stigma type was present \(yes/no\)\. We set the temperature to 0 to ensure consistency\([Atreja et al\. 2025](https://arxiv.org/html/2608.11433#bib.bib7)\)\. After validating this setup on the annotated subset, we applied it to the remaining data, achieving strong alignment with human labels and enabling scalable, consistent annotation\.

Support Classification\.We applied the same in\-context learning approach to support classification\. Each comment was evaluated against one category at a time \(Information,Emotional,Esteem,Tangible,Group Interaction\) usingK=5K=5semantically similar examples retrieved via MPNet\-based cosine similarity\. The prompt mirrored the stigma classification setup, combining the category definition, examples, and a binary yes/no decision\. We set the temperature to 0 for consistency\. After validating the approach on the annotated subset, we applied it to the remaining comments, enabling efficient annotation while maintaining alignment with the taxonomy\.

### 3\.4\.Contextual Analysis

Topic Analysis\.To identify recurring conceptual patterns in both sexual violence narratives and community responses, we applied the LLooM framework\([Lam et al\. 2024](https://arxiv.org/html/2608.11433#bib.bib31)\), a large language model–assisted method for extracting higher\-level concepts from text collections\. LLooM combines concept generation and example matching using LLMs to surface interpretable themes that capture common patterns across posts and comments\.

LIWC\.To analyze linguistic patterns in narratives and responses, we used Linguistic Inquiry and Word Count \(LIWC\)\([Boyd et al\. 2022](https://arxiv.org/html/2608.11433#bib.bib13)\), which captures linguistically meaningful features such as emotional expression, cognitive processes, and social language\.

Emotion Analysis\.To quantify emotional expression, we used the NRC Emotion Lexicon\([Mohammad and Turney 2013](https://arxiv.org/html/2608.11433#bib.bib38)\)to capture eight core emotions\. We compared emotion distributions acrossStigmaandNo Stigmaposts and their corresponding comments, highlighting differences in emotional tone and community responses\.

## 4\.Results

### 4\.1\.Classification Results

Stigma Categories\.Table[4](https://arxiv.org/html/2608.11433#S4.T4)presents the macro\-averaged precision, recall, and F1 scores for each of the models\. Level 1 classifies posts as ‘Relevant’ or ‘Not Relevant,’ Level 2 distinguishesStigmafromNo Stigma, and Level 3 assigns fine\-grained stigma categories\. At Level 1, Gemini \(2\.0 Flash\) achieves the best performance \(F1 = 0\.957\), with a more balanced precision–recall trade\-off than other models\. At Level 2, Gemini again achieves the highest F1 score \(F1 = 0\.820\), while GPT\-4o\-mini shows higher precision but lower recall, resulting in a lower overall F1\. Llama and GPT\-oss achieve lower F1 scores when distinguishingStigmafromNo Stigma\. At Level 3, which is a multi\-label classification task, Gemini maintains the strongest performance \(F1 = 0\.773\)\. Llama and GPT favor recall over precision, while GPT\-oss is more conservative, favoring precision over recall\.

Table 4\.Macro Precision \(P\), Recall \(R\), and F1 acrossStigmaclassification tasks\. Relevant \(Rel\.\), Stigma \(Stig\.\)Table 5\.Distribution of support types across Stigma categories in posts\. Stigma \(Stig\.\), Emotional \(Emo\.\), Informational \(Info\.\)Support Categories\.For support classification in comments, Gemini achieves the strongest overall performance, with an F1 score of 0\.798\. In contrast, GPT\-4o\-mini attains slightly higher precision \(0\.806\), reflecting a more conservative prediction strategy, but substantially lower recall \(0\.649\), indicating that it misses a considerable number of support instances\.

Performance Considerations and Task Complexity\.The observed performance \(F1≈\\approx0\.7–0\.8\) reflects the inherent difficulty of stigma and support classification\. These tasks involve nuanced, context\-dependent, and socially grounded categories that lack clear lexical boundaries\. Stigma detection is particularly challenging due to annotator subjectivity and overlapping concepts, which can co\-occur within the same narrative and introduce labeling ambiguity\([Giorgi et al\. 2024](https://arxiv.org/html/2608.11433#bib.bib25);[Bouzoubaa et al\. 2025](https://arxiv.org/html/2608.11433#bib.bib10)\)\. Similarly, support classification is complicated by the frequent co\-occurrence of multiple, semantically similar support categories within a single comment\. Prior work reports comparable performance ranges for both stigma classification\([Gottipati et al\. 2021](https://arxiv.org/html/2608.11433#bib.bib27);[Bouzoubaa et al\. 2025](https://arxiv.org/html/2608.11433#bib.bib10);[Bouzoubaa et al\. 2024a](https://arxiv.org/html/2608.11433#bib.bib9)\)and support detection\([Ahani et al\. 2026](https://arxiv.org/html/2608.11433#bib.bib3)\), suggesting that our results reflect the complexity of the tasks rather than solely the limitations of the models\.

Overall Data Labeling\.Based on the classification results, we selected Gemini 2\.0 Flash as the primary model for labeling both posts and comments in the full dataset\. Table[5](https://arxiv.org/html/2608.11433#S4.T5)presents the frequency of each support category across stigma categories in posts\.Internalized Stigmais the most prevalent category and is associated with the highest volume of support responses\. Across all stigma categories,InformationalandEsteem Supportare the most common forms of response, whileTangible Supportremains relatively rare\.

### 4\.2\.Contextual Analysis

Table 6\.LLooM\-derived post concepts and examples acrossStigmaandNo Stigma narratives\.
Table 7\.LLooM\-derived support concepts and examples acrossStigmaandNo Stigmanarratives\.

\(a\)Stigmavs\.No Stigmapost clusters\.\(b\) Stigma vs\. No Stigma support clusters\.
Figure 1\.Cluster\-based comparison of concepts across posts and comments\.Concept Discovery with LLooM\.Table[7](https://arxiv.org/html/2608.11433#S4.T7)summarizes the most frequent concepts extracted from posts labeled asStigmaandNo Stigma\.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\. These themes reflect narratives centered on guilt, self\-doubt, and psychological withdrawal following traumatic experiences\. In contrast,No Stigmaposts 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, indicating a focus on describing events and making sense of experiences rather than expressingInternalized Stigma\.

As shown in Table[7](https://arxiv.org/html/2608.11433#S4.T7), comments onStigma\-related posts emphasize validation and affirmation, including normalizing trauma responses, experience sharing, and empowering survivor identity, often alongside actionable guidance such as resource signposting and challenging harmful narratives\. In contrast, comments associated withNo Stigmaposts emphasize informational and guidance\-oriented interactions, with themes such as affirming emotional support, empathetic validation, defining and identifying sexual assault\-related concepts, and encouragement of professional mental health support\. Compared toStigma\-related responses, these comments place greater emphasis on clarification and practical advice rather than counteractingInternalized Stigma\.

To examine structural differences, we embedded LLooM\-derived concepts and visualized them using dimensionality reduction\. Figure[1](https://arxiv.org/html/2608.11433#S4.F1)\(a\) shows partial separation betweenStigmaandNo Stigmapost concepts, withStigmaclustering around internalized distress andNo Stigmaaround situational interpretation, indicating clear thematic differences\. In contrast, Figure[1](https://arxiv.org/html/2608.11433#S4.F1)\(b\) shows substantial overlap in comment\-related concepts\. WhileStigmasupport emphasizes validation andNo Stigmaleans toward informational guidance, boundaries are less distinct, suggesting shared support strategies across contexts\.

![Refer to caption](https://arxiv.org/html/2608.11433v1/heatmap1_posts_liwc.png)Figure 2\.Post\-level linguistic patterns across stigma\.Linguistic Analysis Using LIWC\.We grouped stigma into two broad categories:Felt Stigma\(includingInternalized, Experienced, Anticipated\), reflecting personal experiences, andStructural Stigma, capturing systemic barriers\. As shown in Figure[2](https://arxiv.org/html/2608.11433#S4.F2), posts tagged asInternalized, Experienced, andAnticipatedStigmaexhibit higherAuthenticity\(84\.20\) and lowerAnalyticscores \(10\.00\) thanStructuralStigma\(Authentic= 76\.49,Analytic= 16\.49\), reflecting a more emotionally confessional style compared to the more analytical framing of structural barriers\.No Stigmaposts fall between these extremes, suggesting a relatively neutral linguistic baseline\. At the comment level,Cloutscores are consistently high across all stigma categories and support types, indicating that responses generally adopt a confident, authoritative tone\.Esteem Supportexhibits the highestCloutacross categories \(76\.26, 77\.50, and 68\.24 forInternalized/Experienced/Anticipated,Structural, andNo Stigma\), suggesting that affirmation\-based responses are particularly assertive\.Group Interactionshows the highestAuthenticity, reflecting the personal and experiential nature of peer engagement, whileInformation Supporthas the highestAnalyticscores, consistent with its instructional and solution\-oriented role\.111see additional figures at[https://github\.com/ShirleneRose/Stigma\_SV/](https://github.com/ShirleneRose/Stigma_SV/)

Emotion Analysis\.To examine the emotional characteristics ofStigmaandNo Stigmacontent, we applied the NRC Emotion Lexicon, which captures eight basic emotions: anger, fear, sadness, disgust, anticipation, trust, joy, and surprise\([Plutchik 2001](https://arxiv.org/html/2608.11433#bib.bib40)\)\. Across posts, narratives labeled asInternalized, Experienced, Anticipated Stigmaexhibit relatively higher proportions of negative emotions such assadness,fear, andanger, reflecting the distress and personal vulnerability associated with lived experiences of stigma\. In contrast,Structuralposts show a more moderated emotional profile, with comparatively balanced levels of both negative and neutral emotions, indicating discussions that are less personal and more systemic in nature, whileNo Stigmaposts display higher levels oftrust,anticipation, andjoy, suggesting more neutral or forward\-looking conversations\. A similar pattern is observed in support comments, where responses across all categories are dominated bytrustandanticipation, but comments addressingInternalized/Experienced/Anticipatedcontent exhibit slightly higher negative affect, includingsadnessandfear, reflecting empathetic engagement with distressing experiences\. Support forStructuralcontent remains comparatively neutral, while responses toNo Stigmaposts maintain a more balanced and positive emotional distribution\. Overall, these results demonstrate that emotionally intense signals are most prominent in personallyExperienced Stigma, whereas structural andNo Stigmacontent exhibit comparatively moderated emotional patterns\.222See our GitHub repository for additional figures and results\.

Support Dynamics in Survivor Narratives\.Our results show that support patterns are largely consistent across stigma categories, with differences mainly in emphasis rather than the categories employed\.Information Supportis most prevalent, followed byEsteemandEmotional Support, reflecting a general focus on guidance and validation\. The differences are modest:Internalized, Experienced, andAnticipated Stigmareceive slightly moreEsteem Support\(26\.1%\),Structural Stigmashows higherTangible Support\(7%\), andNo Stigmaposts have the mostInformation Support\(38\.7%\)\. Overall, communities rely on stable support strategies, adjusting emphasis based on the disclosure type\.

## 5\.Related Work

Stigma has been widely conceptualized as a multidimensional social process involving labeling, stereotyping, and discrimination across social and institutional contexts\([Link and Phelan 2001](https://arxiv.org/html/2608.11433#bib.bib34)\), with subsequent work distinguishing mechanisms such as experienced, anticipated, and internalized stigma in health\-related domains including HIV, mental illness, and substance use\([Earnshaw and Chaudoir 2009](https://arxiv.org/html/2608.11433#bib.bib21);[Corrigan and Rao 2012](https://arxiv.org/html/2608.11433#bib.bib16);[Corrigan et al\. 2005](https://arxiv.org/html/2608.11433#bib.bib15);[Earnshaw 2020](https://arxiv.org/html/2608.11433#bib.bib20);[Smith et al\. 2016](https://arxiv.org/html/2608.11433#bib.bib45)\)\. While these frameworks establish stigma as a layered phenomenon affecting both individual perceptions and structural outcomes, they primarily focus on offline settings and do not examine how these mechanisms manifest in large\-scale online discourse\. Prior research on online platforms, particularly Reddit, shows that users frequently engage in self\-disclosure and seek advice in anonymous environments\([Andalibi et al\. 2016](https://arxiv.org/html/2608.11433#bib.bib6);[De Choudhury and De 2014](https://arxiv.org/html/2608.11433#bib.bib17);[Garg et al\. 2025](https://arxiv.org/html/2608.11433#bib.bib23);[Saxena et al\. 2025](https://arxiv.org/html/2608.11433#bib.bib41)\), with responses often containing diverse forms of social support, including emotional, informational, and peer\-based support expressed through linguistic patterns\([De Choudhury and Kiciman 2017](https://arxiv.org/html/2608.11433#bib.bib18);[Sharma and De Choudhury 2018](https://arxiv.org/html/2608.11433#bib.bib44);[Sharma et al\. 2020](https://arxiv.org/html/2608.11433#bib.bib43)\)\. Additional studies highlight structured interaction patterns in online health communication and community\-driven platforms\([Lopes and Da Silva 2019](https://arxiv.org/html/2608.11433#bib.bib35);[Thukral et al\. 2018](https://arxiv.org/html/2608.11433#bib.bib48)\), but have not explicitly modeled how different types of stigma are embedded within narratives or how they relate to specific forms of support\. Advances in computational methods, including natural language processing and large language models, have enabled large\-scale analysis of stigmatizing language and thematic structures in online discourse\([Bouzoubaa et al\. 2024a](https://arxiv.org/html/2608.11433#bib.bib9);[Giorgi et al\. 2024](https://arxiv.org/html/2608.11433#bib.bib25);[Straton et al\. 2020](https://arxiv.org/html/2608.11433#bib.bib47);[Lee and Kyung 2022](https://arxiv.org/html/2608.11433#bib.bib33);[Lam et al\. 2024](https://arxiv.org/html/2608.11433#bib.bib31);[Atreja et al\. 2025](https://arxiv.org/html/2608.11433#bib.bib7);[Margatina et al\. 2023](https://arxiv.org/html/2608.11433#bib.bib36)\), yet these approaches primarily focus on detection or representation learning rather than jointly capturing the multi\-dimensional nature of stigma and its interaction with support\. Our work addresses this gap through a hierarchical multi\-label framework that links stigma expressions in survivor narratives to support strategies in community responses\.

## 6\.Ethics and Limitations

This work examines sensitive discussions of sexual violence, so we take several steps to minimize potential harm\. Although the data are publicly available on Reddit, posts may contain deeply personal experiences, and users may not expect them to be used for research\. We therefore paraphrase all excerpts, remove potentially identifiable details, and only share post/comment IDs to avoid sharing the original content\. The annotations capture expressions in the text and should not be interpreted as clinical or psychological assessments of individuals\. In addition, the norms, moderation practices, and user populations of the selected subreddits may shape both survivor disclosures and community responses\. Our approach also has methodological limitations\. LLM\-based annotations may introduce systematic biases or miss nuanced and overlapping forms of stigma and support, and validation against manually annotated data reduces but does not eliminate these errors\. The proposed taxonomy may not capture all expressions of stigma and support across diverse communities\. Because the analysis is limited to English\-language Reddit data, its generalizability may be constrained by demographic, platform\-specific, and self\-selection biases, and the findings may not reflect evolving patterns over time\. Finally, the dataset is imbalanced across stigma categories\. Although this imbalance reflects the collected data, it limits comparisons for less common categories\. We therefore interpret findings related toStructural Stigmacautiously and call for larger, more balanced datasets in future work\.

## 7\.Discussion and Conclusion

This work provides an interaction\-centered account of stigma and support in online sexual violence discussions\. Prior research has established stigma as a multi\-dimensional social process and has shown that Reddit can serve as a space for disclosure, anonymity, and support\-seeking\([Earnshaw and Chaudoir 2009](https://arxiv.org/html/2608.11433#bib.bib21);[Andalibi et al\. 2016](https://arxiv.org/html/2608.11433#bib.bib6);[De Choudhury and De 2014](https://arxiv.org/html/2608.11433#bib.bib17);[Bouzoubaa et al\. 2025](https://arxiv.org/html/2608.11433#bib.bib10)\)\. OurSCOPEdataset links survivor narratives with the responses, enabling us to examine how disclosure framing and peer support are jointly shaped within socio\-technical spaces\.

A main finding of our work is the relative stability of community support norms\. AcrossStigmaandNo Stigmaposts, commenters rely on a broadly consistent repertoire ofInformation,Emotional Support, andEsteem Support, as well asTangible AssistanceandGroup Interaction, with differences arising mainly in emphasis rather than in kind\. This pattern suggests that community members draw on recognizable and durable response conventions that provide validation, guidance, and reassurance\. Our findings extend prior work on online support exchange by showing that these response norms are resilient across different stigma profiles\([De Choudhury and Kiciman 2017](https://arxiv.org/html/2608.11433#bib.bib18);[Sharma et al\. 2020](https://arxiv.org/html/2608.11433#bib.bib43);[Kraut and Resnick 2012](https://arxiv.org/html/2608.11433#bib.bib30);[Chancellor et al\. 2018](https://arxiv.org/html/2608.11433#bib.bib14)\)\. This resilience also highlights how platform affordances, such as pseudonymous threading, can foster care even around deeply marginalized experiences\([Dimond et al\. 2013](https://arxiv.org/html/2608.11433#bib.bib19)\)\.

The linguistic and emotional patterns further clarify why these support norms matter\. Posts labeled asInternalized,Experienced, andAnticipated Stigmaare more confessional and affectively intense, whereasStructural Stigmais framed in more analytical language\. Community replies, however, tend to stabilize the interaction through validation, affirmation, and practical guidance\. Theoretically, this suggests that online communities actively work to repair the “identity” that survivors anticipate or internalize\([Goffman 1963](https://arxiv.org/html/2608.11433#bib.bib26);[Link and Phelan 2001](https://arxiv.org/html/2608.11433#bib.bib34)\)\. Peer support does more than respond to distress; it helps make disclosures legible and credible\. This aligns with prior work on reciprocity, linguistic accommodation, and community participation\([Andalibi et al\. 2018](https://arxiv.org/html/2608.11433#bib.bib5);[De Choudhury and Kiciman 2017](https://arxiv.org/html/2608.11433#bib.bib18);[Sharma and De Choudhury 2018](https://arxiv.org/html/2608.11433#bib.bib44)\)\.

These results also suggest that moderation and support tools should preserve constructive responses such as validation, information, and belonging\. More broadly, they show that platform outcomes depend on interaction norms as well as content\([Gillespie 2018](https://arxiv.org/html/2608.11433#bib.bib24)\)\. For automated and human\-in\-the\-loop systems\([Seering et al\. 2019](https://arxiv.org/html/2608.11433#bib.bib42)\), 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\.

In conclusion, SCOPE contributes a dataset and computational framework for studying stigma and support together rather than separately\. The main contribution of this work is demonstrating that while stigma deeply shapes how survivors narrate their experiences, community responses remain structurally stable even as those narratives differ\. This interactional perspective offers a foundation for future research on online sexual violence discourse and for designing safer, more responsive digital support environments across platforms with varying affordances, anonymity structures, and moderation regimes\.

###### Acknowledgements\.

We thank Google for access to Gemini through the Gemini Academic Program Award\. We also thank the Reddit communities whose public discussions made this work possible\.

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