Participation is important to Human–Computer Interaction; yet, there are few empirical accounts of how it can be enacted in practice when working with at-risk communities under safety and precarity concerns. This paper offers an account of a six-year, community-led collaboration with an organization supporting individuals affected by forced marriage in Switzerland, centered on the design, development, and handover of a digital self-help tool. Rather than centering the artifact, we use its creation as an analytic lens to examine how design justice principles were enacted and negotiated through concrete decisions, boundaries, and compromises within a highly sensitive, care-centered context. Our account illustrates how justice-oriented participation can be sustained through rights-based and trust-negotiated forms of engagement, even when participation is necessarily uneven, selective, unsafe, or time-limited. This paper contributes process-level insights for HCI researchers and practitioners designing with at-risk communities, foregrounding shared decision-making, community ownership, and responsible handover within existing ecologies of care.

Intimate partner violence (IPV) is defined as “abuse or aggression that occurs in a romantic relationship." IPV survivors face barriers when help-seeking, such as epistemic injustice – secondary victimization from dismissal and indifference when disclosing, misdirection, or inappropriate interventions. Survivors may leverage generative AI to make sensitive disclosures and access hermeneutic resources. However, these tools mediate outcomes for IPV survivors through novel manifestations of epistemic injustice. Using mixed-methods, we investigated hermeneutic resource provision by large-language models (LLMs). We evaluated LLM responses to IPV disclosures on three axes: hermeneutic resource provision, readability, and risk. Prompts were derived from a content analysis of IPV and generative AI discussions in 5 abuse subreddits. We contribute a taxonomy of 7 uses of generative AI in the experience of IPV, empirical illustration of epistemic inequity, and considerations for evaluating epistemic harm in generative AI. Content Warning: This study contains descriptions of abuse and violence.

Suicide is a leading cause of death in the United States. Global safe reporting guidelines for news reports of suicide intend to mitigate associations of increased suicide incidence and stigma. However, recent research suggests more latent patterns in news beyond the guidelines could still contribute to suicide outcomes such as inhibited help-seeking and isolation. Using the Theory of Spiral of Silence to center isolation, we take a mixed-methods approach to analyze 22,021 articles (2020-2024) and use a zero-shot learning large language model (LLM) classifier to detect suppression of four structural circumstances of suicide: financial/job, legal, school, and access to physical/mental healthcare. We find that circumstance disclosure by news publishers diverges by political leaning, financial (p = 0.016), legal (p < 0.001), and school (p < 0.001); and by regionality, legal (p < 0.001) and health (p < 0.001). We qualify mechanisms of suppression using topic modeling and content sharing networks (CSNs). The spiral of silence lens highlights that left leaning publishers are more likely to disclose systemically or socially collective circumstances. In contrast, right leaning outlets suppress those and instead disclose instances that blame individuals for their experiences. Our work highlights how news reporting can downplay structural factors contributing to suicide. Content Warning: This paper discusses suicide deaths reported in news articles and may be sensitive to readers.

Discussions of Violence Against Women (VAW) in publicly accessible forums like online news media can influence the perceptions of people and organizations. Language reinforcing stigma around VAW can result in negative consequences such as unethical representation of survivors and trivialization of the act of violence. In this work, we study the presence of stigmatized framings in news media and how it differs based on media attributes like regionality, political leaning, veracity, and latent communities of news sources. We also investigate the interactions between VAW-based stigma and 14 issue-generic policies used to describe political communications. We found that articles from national, right-leaning, and conspiratorial news sources contain more stigma compared to their counterparts. Furthermore, alignment of articles to the issue-generic policies offers the highest explanation for the presence of stigma in news articles. We discuss implications for institutions to improve safe reporting guidelines on VAW.

Social computing scholars have long known that people do not interact with knowledge in straightforward ways, especially in digital environments. While policies around knowledge are essential for targeting misinformation, they are value-laden; in choosing how to present information, we undermine non-traditional, often non-Western, ways of knowing. Epistemic injustice is the systemic exclusion of certain people and methods from the knowledge canon. Epistemic injustice chips away at one's testimony and vocabulary until they are stripped of their due right to know and understand. In this paper, we articulate how epistemic injustice in sociotechnical applications leads to material harm. Inspired by a hybrid collaborative autoethnography of 14 CSCW practitioners, we present three cases of epistemic injustice in sociotechnical applications: online transgender healthcare, identity sensemaking on r/bisexual, and Indigenous ways of knowing on r/AskHistorians. We further explore signature tensions across our autoethnographic materials and relate them to previous CSCW research areas and personal non-technological experiences. We argue that epistemic injustice can serve as a unifying and intersectional lens for CSCW research by surfacing dimensions of epistemic community and power. Finally, we present a call to action of three changes the CSCW community should make to move toward its own goals of research justice. We call for CSCW researchers to center individual experiences, bolster communities, and remediate issues of epistemic power as a means towards epistemic justice. In sum, we recount, synthesize, and propose solutions for the various forms of epistemic injustice that CSCW sites of study---including CSCW itself---propagate.

Gender-based violence (GBV) is an ongoing public health issue. Prevention practice and research on GBV contend with an incomplete understanding of its public health burden due to gender-exclusive definitions of GBV and inhibited survivor disclosure. Prior work in CSCW and HCI has explored sensitive disclosures and GBV conversations but excludes examination of conversation dynamics in surfacing knowledge of GBV. We used a mixed-methods approach to understand the phenomenon characterized by Muted Group Theory as a mechanism inhibiting disclosure in the context of GBV discussions on Reddit. Using an iterative process informed by literature of GBV research on cis-gender women, girls, trans, and non-binary populations, we developed comprehensive keywords to obtain, annotate, and analyze 298 posts and 10,369 comments about GBV across 7 subreddits. We found that muting faced by survivors offline, precluding reporting, is replicated on the Reddit platform. This study surfaced 5 categories of muting in discussions of GBV situated by variations in communication norms between dominant and non-dominant groups. These findings were supported by analysis of linguistic attributes that inform an Ensemble Classifier's detection of muting in conversations of GBV. The results offer that muting is a harmful occurrence in online disclosures of GBV that is mediated by existing moderation practices. This work contributes an expanded understanding of GBV conversations online, muting as a feature of those conversations, and an initial foray into detection to inform muting prevention.