Relationship platforms (e.g., dating apps) are crucial tools for sapphics (trans women, cisgender women, and nonbinary people who are attracted to other sapphics). However, current platforms are not designed in a way that accounts for sapphic lived experience, especially the lived experience of sapphics who hold multiple marginalized identity characteristics. Even on platforms that do exist for sapphics, transgender women and nonbinary people are often subject to discrimination, fetishization, and stigmatization. To aid in the design of platforms that better serve the needs of multiply marginalized sapphics, we engaged a diverse group of 25 sapphics in six rounds of community discussion on key topics for relationship platform design. Based on participant discussions, we identify key challenges when designing for multiply marginalized sapphics around relationship structures, gender and sexuality classification, and safety priorities for interaction. We present two design priorities alongside community-sourced design directions which can help future designers address these challenges: identity-centric safety and community-based information formats.

Getting training data for machine learning (ML) prediction of mental illness on social media data is labor intensive. To work around this, ML teams will extrapolate proxy signals, or alternative signs from data to evaluate illness status and create training datasets. However, these signals' validity has not been determined, whether signals align with important contextual factors, and how proxy quality impacts downstream model integrity. We use ML and qualitative methods to evaluate whether a popular proxy signal, diagnostic self-disclosure, produces a conceptually sound ML model of mental illness. Our findings identify major conceptual errors only seen through a qualitative investigation -- training data built from diagnostic disclosures encodes a narrow vision of diagnosis experiences that propagates into paradoxes in the downstream ML model. This gap is obscured by strong performance of the ML classifier (F1 = 0.91). We discuss the implications of conceptual gaps in creating training data for human-centered models, and make suggestions for improving research methods.

Interpretive scholars generate knowledge from text corpora by manually sampling documents, applying codes, and refining and collating codes into categories until meaningful themes emerge. Given a large corpus, machine learning could help scale this data sampling and analysis, but prior research shows that experts are generally concerned about algorithms potentially disrupting or driving interpretive scholarship. We take a human-centered design approach to addressing concerns around machine-assisted interpretive research to build Scholastic, which incorporates a machine-in-the-loop clustering algorithm to scaffold interpretive text analysis. As a scholar applies codes to documents and refines them, the resulting coding schema serves as structured metadata which constrains hierarchical document and word clusters inferred from the corpus. Interactive visualizations of these clusters can help scholars strategically sample documents further toward insights. Scholastic demonstrates how human-centered algorithm design and visualizations employing familiar metaphors can support inductive and interpretive research methodologies through interactive topic modeling and document clustering.

The study of eating disorders online has a long tradition within CSCW and HCI scholarship. Research within this body of work highlights the types of content people with eating disorders post as well as the ways in which individuals use online spaces for acceptance, connection, and support. However, despite nearly a decade of research, online eating disorder scholarship in CSCW and HCI rarely accounts for the ways gender shapes online engagement. In this paper, we present empirical results from interviews with 14 trans people with eating disorders. Our findings illustrate how working with gender as an analytic lens allowed us to produce new knowledge about the embodiment of participation in online eating disorder spaces. We show how trans people with eating disorders use online eating disorder content to inform and set goals for their bodies and how, as gender minorities within online eating disorder spaces, trans people occupy marginal positions that make them more susceptible to harms, such as threats to eating disorder validity and gender authenticity. In our discussion, we consider life transitions in the context of gender and eating disorders and address how online eating disorder spaces operate as social transition machinery. We also call attention to the labor associated with online participation as a gender minority within online eating disorder spaces, outlining several design recommendations for supporting the ways trans people with eating disorders use online spaces. CONTENT WARNING: This paper is about the online experiences of trans people with eating disorders. We discuss eating disorders, related content (e.g., thinspiration) and practices (e.g., binge eating, restriction), and gender dysphoria. Please read with caution.

Large datasets or 'big data' corpora are typically the domain of quantitative scholars, who work with computational tools to derive numerical and descriptive insights. However, recent work asks how computational tools and other technologies, such as AI, can support qualitative scholars in developing deep and complex insights from large amounts of data. Addressing this question, Jiang et al. found that qualitative scholars are generally opposed to incorporating AI in their practices of data analysis. In this paper, we provide nuance to these earlier findings, showing that the stage of qualitative analysis matters for how scholars believe AI can and should be used. Through interviews with 15 CSCW and HCI qualitative researchers, we explore how AI can be included throughout different stages of qualitative analysis. We find that qualitative scholars are amenable to working with AI in diverse ways, such as for data exploration and coding, as long as it assists rather than automates their analytic work practice. Based on our analysis, we discuss how incorporating AI into qualitative research can shift some analytic practices, and how designing for human-AI collaboration in qualitative analysis necessitates considering tradeoffs in scale, abstraction, and task delegation.

For individuals with mental illness, social media platforms are considered spaces for sharing and connection. However, not all expressions of mental illness are treated equally on these platforms. Different aggregates of human and technical control are used to report and ban content, accounts, and communities. Through two years of digital ethnography, including online observation and interviews, with people with eating disorders, we examine the experience of content moderation. We use a constructivist grounded theory approach to analysis that shows how practices of moderation across different platforms have particular consequences for members of marginalized groups, who are pressured to conform and compelled to resist. Above all, we argue that platform moderation is enmeshed with wider processes of conformity to specific versions of mental illness. Practices of moderation reassert certain bodies and experiences as 'normal' and valued, while rejecting others. At the same time, navigating and resisting these normative pressures further inscribes the marginal status of certain individuals. We discuss changes to the ways that platforms handle content related to eating disorders by drawing on the concept of multiplicity to inform design.

Despite historical precedence and modern prevalence, mental illness and associated disorders are frequently aligned with notions of deviance and, by association, abnormality. The view that mental illness deviates from an implicit social norm permeates the CHI community, impacting how scholars approach research in this space. In this paper, we challenge community and societal norms aligning mental illness with deviance. We combine semi-structured interviews with digital ethnography of public Instagram accounts to examine how Instagram users express mental illness. Drawing on small stories research, we find that individuals situate mental illness within their everyday lives and negotiate their tellings of experience due to the influence of various social control structures. We discuss implications for incorporating 'the everyday' into the design of technological solutions for marginalized communities and the ways in which researchers and designers may inadvertently perpetuate and instantiate stigma related to mental illness.

As highlighted in recent work on remix in online content creation communities, people commonly take and appropriate digital content for new activities. Less is known, however, about how people repurpose digital content as part of work. We report findings from an interview study with 19 individuals in which we explored how digital content in the workplace becomes a material for remix. Our analysis emphasizes (i) how digital content is obtained from colleagues for remix, (ii) how content is made available for remix by others, and (iii) how digital content is transformed for remix. In attending to these broader processes of remix, we consider the roles of workplace technologies, such as those for file sharing, as well as social norms that mediate access, remix, and acknowledgement. We draw implications for design of technology that emphasize support for individuals in making digital content available for remix, and raising awareness of the context of that content.

There is a growing emphasis on designing with people with diverse health experiences rather than designing for them. Yet, collaborative design becomes difficult when working with individuals with health conditions (e.g., stroke, cancer, abuse, depression) that affect their ability or willingness to engage alongside researchers and verbally express themselves. The present paper analyzes how the clinical practice of art therapy engages these individuals in co-creative, visual expression of ideas, thoughts, and experiences. Drawing on interviews with 22 art therapists and over two years of field work in a clinical setting, we detail how art therapists view making as expression for people with complex communication needs. Under this view, we argue that art therapy practice can inspire collaborative design engagements by understanding materials as language, creating space for expression, and sustaining expressions in a broader context. We discuss practical and ethical implications for design work involving individuals with complex communication needs.

In CSCW and HCI, work examining expression of mental health and illness on social media frequently aims to classify content, quantify visual trends, and predict user states. This approach to analysis is a form of the coded gaze, a type of algorithmic 'way of seeing' coined with respect to artificial intelligence techniques. The coded gaze classifies content through researcher- and machine-labeled categories, relying on a series of theoretical assumptions that influence how values pertaining to mental health and illness become inscribed in data. In this paper, we build upon this research to support alternative methods of data interpretation. We join manual collection of Instagram posts with semi-structured interviews and digital ethnography over six months to understand how Instagram users express their experiences with mental health and illness. We argue that individuals negotiate claims to mental health and illness through visibility and signaling, the boundaries between mental health and illness are porous and blurred, and reposting and remix are a form of participation. We discuss practical and ethical implications for studying the expression of mental health and illness online.

Traumatic Brain Injury (TBI) can affect all aspects of an individual's life, including physical ability, communication, and mental health, and present chronic health conditions that persist throughout the lifespan. Although prior work documents a decrease in social interaction following brain injury, little is known about how individuals with TBI engage in social behavior during their recovery, how others in their lives participate, and how these interactions occur in both online and offline contexts. We examine these issues through an interview study involving individuals with TBI, as well as caregivers and social contacts of individuals with TBI. Our analysis identifies the concept of social re-emergence, a non-linear process of developing a new social identity that involves withdrawing from social life, developing goals for social participation, disclosing health information for social support and acceptance, and attaining social independence.

When social networking sites give users granular control over their privacy settings, the result is that some content across the site is public and some is not. How might this content--or characteristics of users who post publicly versus to a limited audience--be different? If these differences exist, research studies of public content could potentially be introducing systematic bias. Via Mechanical Turk, we asked 1,815 Facebook users to share recent posts. Using qualitative coding and quantitative measures, we characterize and categorize the nature of the content. Using machine learning techniques, we analyze patterns of choices for privacy settings. Contrary to expectations, we find that content type is not a significant predictor of privacy setting; however, some demographics such as gender and age are predictive. Additionally, with consent of participants, we provide a dataset of nearly 9,000 public and non-public Facebook posts.

Copyright law is increasingly relevant to everyday interactions online, from social media status updates to artists showcasing their work. This is especially true in creative spaces where rules about reuse and remix are notoriously gray. Based on a content analysis of public forum postings in eight different online communities featuring different media types (music, video, art, and writing), we found that copyright is a frequent topic of conversation and that much of this discourse stems from problems that copyright causes for creative activities. We identify the major types of problems encountered, including chilling effects that negatively impact technology use. We find that many challenges can be explained by lack of knowledge about legal or policy rules, including breakdowns in user expectations for the sites they use. We argue that lack of clarity is a pervasive usability problem that should be considered more carefully in the design of user-generated content platforms.

Once referred to by the Supreme Court as the "metaphysics" of law, many parts of copyright policy are historically confusing. Therefore, it isn't surprising that in communities where amateur content creators work within a legal gray area, copyright is a frequent topic of conversation. Here, people with often little knowledge of the letter of the law are asking and answering complex legal questions in the context of their creative activities. Working from a content analysis of public forum conversations in eight different online communities, we have examined these questions and answers more closely. By studying these interactions, what can we learn about how people engage with the law and how non-expert advice affects behavior and knowledge?