Stakeholder-informed prioritization (SIP) is a lightweight technique designed to help child-involved groups quickly understand and prioritize research and design directions. However, outcomes of short prioritization discussions can vary with group composition. In this paper, we deployed SIP with 34 groups of children and parents (N = 94) at a large community event, focusing on how group composition, including group type (family-based vs. child-based) and familiarity (mixed-familiarity vs. fully familiar), shapes SIP experiences. Results suggest that child-based groups were associated with higher satisfaction, more active child participation, smaller in-group range in comfort, satisfaction, and participation level, and more verbal reasoning. Fully familiar groups reported higher comfort and showed more verbal reasoning and less facilitator involvement, while mixed-familiarity groups showed a smaller in-group range in participation levels. We discuss implications for designing and facilitating child-involved group prioritization sessions and provide practical guidance for using SIP as a rapid method in early-stage research.

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.

Online communities and their host platforms are mutually dependent yet conflict-prone. When platform policies clash with community values, communities have resisted through strikes, blackouts, and even migration to other platforms. Through such collective actions, communities have sometimes won concessions, but these have frequently proved to be temporary. Although previous research has investigated strike events and migration chains, the processes by which community-platform conflict unfolds remain obscure. How do community-platform relationships deteriorate? How do communities organize collective action? How do the participants proceed in the aftermath? We investigate a conflict between the Stack Exchange platform and community that occurred in 2023 around an emergency arising from the release of large language models (LLMs). Based on a qualitative thematic analysis of 2,070 messages from Meta Stack Exchange and 14 interviews with community members, we reveal how the 2023 conflict was preceded by a long-term deterioration in the community-platform relationship, driven in particular by the platform’s disregard for the community’s highly valued participatory role in governance. Moreover, the platform’s policy response to LLMs aggravated the community’s sense of crisis, triggering strike mobilization. We analyze how the mobilization was coordinated through a tiered leadership and communication structure, as well as how community members pivoted in the aftermath. Building on recent theoretical scholarship in social computing, we use Hirschman’s exit, voice, and loyalty framework to theorize the challenges of community-platform relations evinced in our data. Finally, we recommend ways that platforms and communities can institute participatory governance to be durable and effective.

As technology plays a growing role in children's lives, incorporating their perspectives is crucial for shaping future research and design.This paper introduces a lightweight technique, called Stakeholder-Informed Prioritization (SIP), designed to help childinvolved groups quickly get informed about and prioritize research and design directions.We demonstrate the use of SIP through a case study focused on Virtual Reality (VR), a technology that is rapidly gaining popularity among younger users but remains underexplored from their point of view.We piloted SIP with 34 groups of children (aged 8 to 18) and their families during a community event.Our results show that participants in these groups can successfully complete SIPs and are generally satisfied with the results.

As new machine learning methods demand larger training datasets, researchers and developers face significant challenges in dataset management. Although ethics reviews, documentation, and checklists have been established, it remains uncertain whether consistent dataset management practices exist across the community. This lack of a comprehensive overview hinders our ability to diagnose and address fundamental tensions and ethical issues related to managing large datasets. We present a systematic review of datasets published at the NeurIPS Datasets and Benchmarks track, focusing on four key aspects: provenance, distribution, ethical disclosure, and licensing. Our findings reveal that dataset provenance is often unclear due to ambiguous filtering and curation processes. Additionally, a variety of sites are used for dataset hosting, but only a few offer structured metadata and version control. These inconsistencies underscore the urgent need for standardized data infrastructures for the publication and management of datasets.

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.

Social media platforms are a place where people look for information and social support for mental health, resulting in both positive and negative effects on users. TikTok has gained notoriety for an abundance of mental health content and discourse. We present findings from a semi-structured interview study with 16 participants about mental health content and participants’ perceptions of community on TikTok. We find that TikTok’s community structure is permeable, allowing for self-discovery and understanding not found in traditional online communities. However, participants are wary of mental health information due to conflicts between a creator’s vulnerability and credibility. Our interviews suggest that the “For You Page" is a runaway train that encourages diverse community and content engagement but also displays harmful content that participants feel they cannot escape. We propose design implications to support better mental health, as well as implications for social computing research on community in algorithmic landscapes.

Peer production systems have frictions-mechanisms that make contributing more effortful-to prevent vandalism and protect information quality. Page protection on Wikipedia is a mechanism where the platform's core values conflict, but there is little quantitative work to ground deliberation. In this paper, we empirically explore the consequences of page protection on Internet Culture articles on Wikipedia (6,264 articles, 108 edit-protected). We first qualitatively analyzed 150 requests for page protection, finding that page protection is motivated by an article's (1) activity, (2) topic area, and (3) visibility. These findings informed a matching approach to compare protected pages and similar unprotected articles. We quantitatively evaluate the differences between protected and unprotected pages across two dimensions: editor engagement and contributor concentration. Protected articles show different trends in editor engagement and equity amongst contributors, affecting the overall disparity in the population. We discuss the role of friction in online platforms, new ways to measure it, and future work.

How does presenting comments in a news article affect the ways that readers engage with and retain information about news? This paper presents results from a controlled experiment investigating effects related to different strategies for promoting discussion at news websites (N=336 participants). The strategies include highlighting specific comments about a data visualization, providing prompts with the comments, and annotating prompts on the visualization. By comparison to a simple list of comments (baseline), our analysis found that annotations contributed to higher levels of participant engagement in the discussion, yet lower levels of knowledge retention related to the article. These findings raise new considerations about whether and how to integrate discussion content into news and points toward future content moderation systems that assist in representing and eliciting discussion at news websites.

Due to challenges around low-quality comments and misinformation, many news outlets have opted to turn off commenting features on their websites. The New York Times (NYT), on the other hand, has continued to scale up its online discussion resources to reach large audiences. Through interviews with the NYT moderation team, we present examples of how moderators manage the first ~24 hours of online discussion after a story breaks, while balancing concerns about journalistic credibility. We discuss how managing comments at the NYT is not merely a matter of content regulation, but can involve reporting from the "community beat" to recognize emerging topics and synthesize the multiple perspectives in a discussion to promote community. We discuss how other news organizations---including those lacking moderation resources---might appropriate the strategies and decisions offered by the NYT. Future research should investigate strategies to share and update the information generated about topics in the news through the course of content moderation.