We examine how people experience two choices in the design of generative ghosts, AI systems that are trained on data of the dead: representation, where an AI speaks about a deceased person in the third person, and reincarnation, where the AI speaks as the deceased in the first person. Through a qualitative user study with 16 participants, we explore how each shaped authenticity, affect, and risk. Reincarnation was preferred for its immediacy, but participants shared fears of over-reliance. Representation was preferred for engaging with memory over conversational presence, though participants often ignored this distinction, engaging in dialogue despite third-person framing. Across both modes, participants privileged affective resonance over factual fidelity. We conclude by showing how factors such as tone, language, and conversational rhythm – factors unique to the user’s memory of the deceased – shape interactions with generative ghosts, and argue that those interactions are always collaborative.

Artifacts encode memories and shape how one is remembered after death. Older adults, given their life stage, are often engaged in intentional end-of-life planning, making their perspectives on curation for remembrance post-mortem valuable. While HCI has examined older adults’ curation of memory artifacts (e.g., to support well-being), little is known about their curation preferences with the intent of shaping how one is remembered after death. Drawing on interviews with older adults, our work provides insights into how individuals think about remembrance after death, including who is remembered (subject) and who remembers (audience), and how physical, temporal, or relational traces captured in artifacts mediate remembrance. These findings offer design implications for legacy crafting systems to support remembrance that transcends the self, account for the intentional legacy crafting work for distinct audiences, and support hybrid artifacts that allow memories to move across physical and digital spaces.

Technology-supported group work, and research surrounding it, has evolved considerably since scholars first conferred on the topic in 1982. Generative AI in particular has instilled swift and considerable change to how we work and who (or what) we work with, as have other emerging technologies like augmented and virtual reality (AR/VR). This issue of the Proceedings of the ACM on Human-Computer Interaction features contributions across a wide range of topics pertaining to and orbiting group work, which encapsulate both time-tested and exciting new themes in how computing technology supports, expands, and challenges the ways individuals interact and collaborate. Like previous GROUP editions, this issue features design fiction contributions as well as note-style and full-length papers. Topics featured across these articles are expansive in terms of the technologies studied, their context of use, and their impact on interaction. This issue explores misinformation on local communities, AI as a collaborative assistant, and storytelling among families, as just a few examples.

What happens to a person's data after they pass away?Designing for digital legacy requires input from individuals across all life stages, as motivations to plan vary with age.Yet, the specific perspectives that older adults have on end-of-life data management have not been investigated in depth.Through interviews with 16 older adults, we examine their preferences and motivations for managing everyday digital data (e.g., text messages, social media, photos) after death.Our findings surface several implications for end-of-life data planning, including creating awareness about digital legacy and associated risks.We also unpack and discuss how older adults' life stage and familiarity with end-of-life planning uniquely positions them to identify barriers and opportunities in managing digital legacy, such as how post-mortem data can encode societal norms of a period or be donated for the greater good. CCS Concepts• Human-centered computing → Human computer interaction (HCI); • Social and professional topics → Seniors.

Healthcare providers play a crucial role in developing care plans for patients at the end of their lives. As value elicitation becomes increasingly integrated into advance care planning, digital tools have been proposed to support providers. However, the development of these tools has primarily focused on facilitating communication of values from patients to providers. To address providers' specific needs and barriers, such as using values to create and manage medical care plans, we conducted an exploratory study capturing provider perspectives. Through interviews with 18 palliative care providers, we identified what they consider the typical value elicitation process, its associated challenges, and their visions for an ideal future process. To guide future research in determining priority areas for technology intervention, we synthesized our findings into a conceptual model that visualizes the tasks of providers in value elicitation. Comparing our model with previous models focused on patient and caregiver tasks, we found that provider perspectives introduce additional tasks in value elicitation that warrant attention: the creation, member-checking, and formal documentation of care plans by providers. Based on our findings, we argue that future work should focus on designing digital tools that support the provider-specific tasks of care plan creation and management to improve value elicitation.

Though online platforms have begun to include support for end-of-life needs, their development has been piecemeal and varies from platform to platform. Recent social computing research has examined end-of-life support on social media platforms and platforms specifically designed for grief and remembrance. However, understanding the functional end-of-life support provided by a wider array of platforms is needed to identify the most urgent design priorities beyond social media and remembrance-specific platforms. In this study, we present the results of a large-scale, multi-platform analysis of end-of-life support, summarizing the current state and identifying gaps as of April 2023. This study helps identify priorities to guide platform design and future research by identifying the current state of existing end-of-life support and gaps in that support.

Data workers play a key role in the big data industry. Clients hire data workers to collect and annotate data with human identity concepts, like demographic categories or clothing items. Often, such workers are treated as computational-they are expected to quickly and objectively conduct their work, with the goal of having huge, unbiased datasets for training models. Computer vision is especially interested in fair and impartial data due to biases and unethical practices in the field. However, far from impartial, data workers imbue computer vision data with ''biases'' beyond correct versus incorrect answers. Data workers embed their own specific positional perspectives about identity concepts in both collection and annotation processes. Through interviews and ethnographic observations of data workers (freelance and business process outsourcing (BPO) employees), we show how worker positionality influences decisions during data work. We also show the unintended outcomes, like social biases, that occur when positionality is not explicitly attended to in client instructions. We discuss how employing a lens of positionality in data work reveals the gulfs between data worker perspectives and client expectations, which are colored by a web of positional actors beyond isolated data workers. We propose positional (il)legibility as an approach to data work that embraces the reality of positionality in classification practices and addresses the failures of positivist bias mitigation practices.

In a world with shrinking queer spaces, dating apps serve as a useful way to find romantic or sexual partners, make friends, and develop connections in an often fragmented community. However, dating app structures may exclude queer users more than their cisgender heterosexual counterparts. In this paper, we examine how gender and sexuality are formatted as data in dating app profiles and algorithmically curated matchmaking functions. Based on an analysis of ten popular dating apps, we found gender and sexuality were often divided into descriptive labels and functional categories, which systematically marginalize or exclude queer identities from the matching process despite appearing inclusive. We present a framework for identity on dating apps, disentangling identity into a multi-layered construct of data schemas, profiles, algorithms, and platform ideology, thus enabling designers of data-driven systems to identify salient factors for users, even beyond queer identities and dating apps.

Large language models (LLMs) are the new hot trend being rapidly integrated into products and services—often, in chatbots. LLM-powered chatbots are expected to respond to any number of topics, including topics central to gender identity . In light of rising anti-trans discourse, we examined how two popular LLMs responded to real-world English-language questions about trans identity taken from Quora. We employed reflexive analysis that centered our situated knowledges of the trans community. We found that LLMs return pro-trans responses, even when presented with highly transphobic user prompts. While we also found highly transphobic LLM responses, we found that anti-trans sentiment in LLMs was often subtle, requiring a deep positional understanding from diverse trans stakeholders to interpret. Based on these findings, we recommend diverging from current “value-neutral” approaches that validate transphobia by taking an “all sides” approach. We provide considerations for both the evaluation and design of LLMs that center positional expertise.

While colonization has sociohistorically impacted people’s identities across various dimensions, those colonial values and biases continue to be perpetuated by sociotechnical systems. One category of sociotechnical systems–sentiment analysis tools–can also perpetuate colonial values and bias, yet less attention has been paid to how such tools may be complicit in perpetuating coloniality, although they are often used to guide various practices (e.g., content moderation). In this paper, we explore potential bias in sentiment analysis tools in the context of Bengali communities who have experienced and continue to experience the impacts of colonialism. Drawing on identity categories most impacted by colonialism amongst local Bengali communities, we focused our analytic attention on gender, religion, and nationality. We conducted an algorithmic audit of all sentiment analysis tools for Bengali, available on the Python package index (PyPI) and GitHub. Despite similar semantic content and structure, our analyses showed that in addition to inconsistencies in output from different tools, Bengali sentiment analysis tools exhibit bias between different identity categories and respond differently to different ways of identity expression. Connecting our findings with colonially shaped sociocultural structures of Bengali communities, we discuss the implications of downstream bias of sentiment analysis tools.

To support people at the end of life as they create management plans for their assets, planning approaches like estate planning are increasingly considering data. HCI scholarship has argued that developing more effective planning approaches to support end-of-life data planning is important. However, empirical research is needed to evaluate specific approaches and identify design considerations. To support end-of-life data planning, this paper presents a qualitative study evaluating two approaches to co-designing end-of-life data plans with participants. We find that asset-first inventory-centric approaches, common in material estate planning, may be ineffective when making plans for data. In contrast, heavily facilitated, mission-driven, relationship-centric approaches were more effective. This study expands previous research by validating the importance of starting end-of-life data planning with relationships and values, and highlights collaborative facilitation as a critical part of successful data planning approaches.

There has been a great deal of scholarly attention on issues of identity-related bias in machine learning. Much of this attention has focused on data and data workers, workers who do annotation tasks. Yet tech workers—like engineers, data scientists, and researchers—introduce their own “biases” when defining “identity” concepts. More specifically, they instill their own positionalities, the way they understand and are shaped by the world around them. Through interviews with industry tech workers who focus on computer vision, we show how workers embed their own positional perspectives into products and how positional gaps can lead to unforeseen and undesirable outcomes. We discuss how worker positionality is mutually shaped by the contexts in which they are embedded. We provide implications for researchers and practitioners to engage with the positionalities of tech workers, as well as those in contexts outside of development that influence tech workers.

LGBTQ+ people have received increased attention in HCI research, paralleling a greater emphasis on social justice in recent years. However, there has not been a systematic review of how LGBTQ+ people are researched or discussed in HCI. In this work, we review all research mentioning LGBTQ+ people across the HCI venues of CHI, CSCW, DIS, and TOCHI. Since 2014, we find a linear growth in the number of papers substantially about LGBTQ+ people and an exponential increase in the number of mentions. Research about LGBTQ+ people tends to center experiences of being politicized, outside the norm, stigmatized, or highly vulnerable. LGBTQ+ people are typically mentioned as a marginalized group or an area of future research. We identify gaps and opportunities for (1) research about and (2) the discussion of LGBTQ+ in HCI and provide a dataset to facilitate future Queer HCI research.

Clinical group bereavement therapy often promotes narrative sharing as a therapeutic intervention to facilitate grief processing. Increasingly, people turn to social media to express stories of loss and seek support surrounding bereavement experiences, specifically, the loss of loved ones from suicide. This paper reports the results of a computational linguistic analysis of narrative expression within an online suicide bereavement support community. We identify distinctive characteristics of narrative posts (compared to non-narrative posts) in linguistic style. We then develop and validate a machine-learning model for tagging narrative posts at scale and demonstrate the utility of applying this machine-learning model to a more general grief support community. Through comparison, we validate our model's narrative tagging accuracy and compare the proportion of narrative posts between the two communities we have analyzed. Narrative posts make up about half of all total posts in these two grief communities, demonstrating the importance of narrative posts to grief support online. Finally, we consider how the narrative tagging tool presented in this study can be applied to platform design to more effectively support people expressing the narrative sharing of grief in online grief support spaces.

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.

When bereaved individuals seek online support in response to the suicide of a loved one, their expressions of grief take many forms. Although the intense grief expressions individuals bereaved by suicide commonly share in private therapeutic settings can be helpful in healing from traumatic loss, these same expressions may potentially cause harm to others when shared in a public online support community. In this study, we present a qualitative analysis of letters posted on the r/SuicideBereavement subreddit, and comments replying to those posts, to explore what diverse expressions of grief additionally demand of platform design. We find that letter posts contain potentially harmful grief expressions that, in this community, generate mutual support among community members. Informed by our findings, this study considers the design challenges for online platforms as they simultaneously support users receiving support and healing through sharing certain grief expressions, while also supporting users who will be harmed by exposure to those grief expressions. Taking inspiration from offline therapy modalities, we consider the design implications of creating specialized online grief support spaces for diverse grief expressions.

Access to mortality data is critical for researchers for various reasons, including the development of life-saving medical interventions. However, high-quality mortality data is typically unavailable for researchers to access in a timely manner due to bottlenecks and inefficiencies in the coordination of managing mortality data across a vast information ecosystem. To identify opportunities to improve access to mortality data, we conducted a qualitative interview study with 20 experts with direct knowledge and experience with mortality data systems. From these interviews, we mapped the mortality data ecosystem and found that policy constraints are the main underlying cause of bottlenecks and inefficiencies. We argue that policy intervention is required to optimize the coordination of mortality data between databases and between organizations. To support researcher access to mortality data, we present guiding principles for designers seeking to improve the mortality data ecosystem. These principles contribute to CSCW scholarship focused on the challenges of coordination across large information ecosystems and the tradeoffs between technology and policy when designing such systems.

After a break-up, people are left with data representative of their lost relationship - pictures, posts, and connections that exist because of that relationship. As part of breaking up and moving on, people often make decisions about managing that data. Prior work has identified two broad types of curatorial philosophies people adopt in data management: archivists and revisionists. However, what drives individuals to one approach remains unknown and is difficult to design sociotechnical systems for. Through focus group interviews with couples still together, we present a decision-making framework for data management. We outline factors that can influence individuals' decision to act as an archivist or revisionist in the wake of a break-up. From our data and framework, we identify six implications for design to improve user experiences in the wake of a break-up, and from those implications, offer concrete suggestions for design for social media platforms.

This article considers how subjectivities are enlivened in algorithmic systems. We first review related literature to clarify how we see “subjectivities” as emerging through a tangled web of processes and actors. We then offer two case studies exemplifying the emergence of algorithmic subjectivities: one involving computational topic modeling of blogs written by parents with children on the autism spectrum and one involving algorithmic moderation of social media content. Drawing on these case studies, we then articulate a series of qualities that characterizes algorithmic subjectivities. We also compare and contrast these qualities with a number of related concepts from prior literature to articulate how algorithmic subjectivities constitute a novel theoretical contribution, as well as how it offers a focal lens for future empirical investigation and for design. In short, this article points out how certain worlds are being made and/or being made possible via algorithmic systems, and it asks Human–Computer Interaction (HCI) to consider what other worlds might be possible.

To more effectively support the dying and bereaved in end-of-life contexts, over the past two decades HCI and social computing scholars have sought to understand digital legacy. In this paper, we argue that it is time to take stock of digital legacy scholarship, examining what we know, what gaps remain, and what areas are imperative for future work. Through a Grounded Theory Literature Review, we identify four foci in digital legacy research to date: how identity is navigated in the passing of digital legacy, how digital legacies are engaged with, how digital legacies are put to rest, and how technology interfaces with offline legacy technologies. Based on our analysis, we present a model depicting how digital legacy research examines a lifecycle of data as it is passed down. This model identifies that digital legacy data moves through three stages: encoding, accessing, and dispossessing. The model illustrates gaps in current research and charts possible inflection points for future social computing research. Specifically, we highlight the importance of multi-user and multi-generational networks of people in end-of-life scenarios. Additionally, the model exhibits emerging theoretical findings and major concepts in the nascent field of digital legacy research.

The collection, organization, and retrieval of data about queer individuals and their identities challenge the creators and curators of highly structured database systems. While prior research in archival studies and demographics has examined processes of collecting and storing queer identities, they do not examine the complexities of highly democratized platforms that lack top-down mandates that often structure archival schemas. To examine the representation of queer people on open-platform databases, we performed a trace ethnography and thematic analysis of Wikidata, an open collaboration, highly structured database. We specifically examined the creation of, changes to, discussions around, and impacts of properties that encode queer identities, such assexual orientation andsex or gender. We found that changes often have unexpected impacts, that contributors struggled to determine vocabulary for queer identities which were accurate across the diverse cultural contexts of the Wikidata community, that the recording of queer identities could cause a stigmatizing effect for LGBTQ+ individuals, with further concerns of spreading rumors or outing closeted people, and that contributors proposing changes which would cause biased representations of queer people. Our analysis demonstrates inherent and unaddressed frictions when translating queer identities to the confines of a structured database. We conclude by discussing ways that the highly bottom-up, collaborative nature of platforms such as Wikidata, often seen as a major strength, can be vulnerable to individuals or small groups derailing and filibustering changes they disagree with on politically charged topics such as queer identities.

Content moderation research typically prioritizes representing and addressing challenges for one group of stakeholders or communities in one type of context. While taking a focused approach is reasonable or even favorable for empirical case studies, it does not address how content moderation works in multiple contexts. Through a systematic literature review of 86 content moderation articles that document empirical studies, we seek to uncover patterns and tensions within past content moderation research. We find that content moderation can be characterized as a series of tradeoffs around moderation actions, styles, philosophies, and values. We discuss how facilitating cooperation and preventing abuse, two key elements in Grimmelmann’s definition of moderation, are inherently dialectical in practice. We close by showing how researchers, designers, and moderators can use our framework of tradeoffs in their own work, and arguing that tradeoffs should be of central importance in investigating and designing content moderation.

It is difficult to design systems that honor the complex and often contradictory emotions that can be surfaced by sensitive encounters with recommender systems. To explore the design and ethical considerations in this space, we interviewed 20 people who had recently seen sensitive content through Facebook’s Memories feature. Interviewees typically described how (1) expectedness, (2) context of viewing, and (3) what we describe as “affective sense-making” were important factors for how they perceived “bittersweet” content, a sensitizing concept from our interviews that we expand upon. To address these user needs, we pose provocations to support critical work in this area and we suggest that researchers and designers: (1) draw inspiration from no/low-technology artifacts, (2) use empirical research to identify contextual features that have negative impacts on users, and (3) conduct user studies on affective sense-making.

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.

After a romantic relationship ends, individuals are left to deal with the digital remnants of the relationship. These possessions and connections pose difficulties for users -- they are identity markers of an identity that one may no longer want legible to their online audiences. Further, they can cause upsetting moments that might impede moving on from the break-up. Through interviews with 11 women who had had a recent break-up, this empirical study examined how people managed their online identity after their break-up. We found that people took different actions towards their possessions and connections in service of creating a post-break-up identity. Using Brubaker & Hayes's (2011) representational framework, we find users attempting to deal with connections as if they were possessions, creating tensions that our current systems are ill-suited to address. Turning to Hogan's (2010) framework of the online identity 'exhibition', we see users creating exhibitions for an audience of one -- themselves -- while also making decisions about who is allowed to see their public-facing exhibitions. We conclude by arguing that existing tools are ill-designed to support competing desires to present authentic past and future online identities and offer design suggestions for consideration.

Gender input forms act as gates to accessing information, websites, and services online. Non-binary people regularly have to interact with them, though many do not offer non-binary gender options. This results in non-binary individuals having to either choose an incorrect gender category or refrain from using a site or service—which is occasionally infeasible (e.g., when accessing health services). We tested five different forms through a survey with binary and non-binary participants (n = 350) in three contexts—a digital health form, a social media website, and a dating app. Our results indicate that the majority of participants found binary “male or female” forms exclusive and uncomfortable to fill out across all contexts. We conclude with design considerations for improving gender input forms and consequently their underlying gender model in databases. Our work aims to sensitize designers of (online) gender web forms to the needs and desires of non-binary people.

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.

Legacy Contact is a Facebook feature that allows account holders to choose someone to manage their profile after they have died. This study evaluates the Legacy Contact setup process, and asks how users form and communicate expectations about what an active post-mortem manager's responsibilities would be. We conducted 30 semi-structured interviews with adult Facebook account holders in the US who either configured their own settings or were chosen for future delegation by someone else. We found that account holders chose the Facebook Friend they felt closest to, and that selection was often reciprocated. Both account holders and legacy contacts felt confident that they had communicated (or could communicate) well about the legacy contact's responsibilities, but their expectations did not align with the actual functionality of the Legacy Contact system. We argue that the misalignment of user expectations and system functionality indicates a failed setup process. Our findings suggest that post-mortem management systems require a setup process that is fundamentally different from the quick-clickthrough standards of everyday interaction design. The ramifications of a post-mortem manager's expectations not being met during a time of grief, points to the urgent need for a setup process that prompts thoughtfulness and deliberation, and forms accurate expectations for account holders and future post-mortem managers alike.

Qualitative inductive methods are widely used in CSCW and HCI research for their ability to generatively discover deep and contextualized insights, but these inherently manual and human-resource-intensive processes are often infeasible for analyzing large corpora. Researchers have been increasingly interested in ways to apply qualitative methods to "big" data problems, hoping to achieve more generalizable results from larger amounts of data while preserving the depth and richness of qualitative methods. In this paper, we describe a study of qualitative researchers' work practices and their challenges, with an eye towards whether this is an appropriate domain for human-AI collaboration and what successful collaborations might entail. Our findings characterize participants' diverse methodological practices and nuanced collaboration dynamics, and identify areas where they might benefit from AI-based tools. While participants highlight the messiness and uncertainty of qualitative inductive analysis, they still want full agency over the process and believe that AI should not interfere. Our study provides a deep investigation of task delegability in human-AI collaboration in the context of qualitative analysis, and offers directions for the design of AI assistance that honor serendipity, human agency, and ambiguity.

The proliferation of harmful content on online social media platforms has necessitated empirical understandings of experiences of harm online and the development of practices for harm mitigation. Both understandings of harm and approaches to mitigating that harm, often through content moderation, have implicitly embedded frameworks of prioritization-what forms of harm should be researched, how policy on harmful content should be implemented, and how harmful content should be moderated. To aid efforts of better understanding the variety of online harms, how they relate to one another, and how to prioritize harms relevant to research, policy, and practice, we present a theoretical framework of severity for harmful online content. By employing a grounded theory approach, we developed a framework of severity based on interviews and card-sorting activities conducted with 52 participants over the course of ten months. Through our analysis, we identified four Types of Harm (physical, emotional, relational, and financial) and eight Dimensions along which the severity of harm can be understood (perspectives, intent, agency, experience, scale, urgency, vulnerability, sphere). We describe how our framework can be applied to both research and policy settings towards deeper understandings of specific forms of harm (e.g., harassment) and prioritization frameworks when implementing policies encompassing many forms of harm.

Coming out and being visible online can offer transgender and/or non-binary people benefits not found elsewhere. However, it also can expose them to negative reactions and bad experiences. Through an analysis of 15 semi-structured interviews, we investigate the experiences of transgender and/or non-binary people coming out across social media sites (SMSs). We found that participants employed strategies around disclosure and visibility to limit the consequences of coming out and to access support. Using trans theory on visibility, we discuss how online spaces present metaphorical "doors" to resources, support, and recognition---but can also be "traps" for those that do not meet the expectations of the space. We discuss how visibility empowered participants to create "trapdoors" to new spaces within SMSs where they could create positive outcomes for themselves and their communities. We close with considerations for designers as they create online spaces, and present a broader call to action for the HCI community around designing online spaces.

Race and gender have long sociopolitical histories of classification in technical infrastructures-from the passport to social media. Facial analysis technologies are particularly pertinent to understanding how identity is operationalized in new technical systems. What facial analysis technologies can do is determined by the data available to train and evaluate them with. In this study, we specifically focus on this data by examining how race and gender are defined and annotated in image databases used for facial analysis. We found that the majority of image databases rarely contain underlying source material for how those identities are defined. Further, when they are annotated with race and gender information, database authors rarely describe the process of annotation. Instead, classifications of race and gender are portrayed as insignificant, indisputable, and apolitical. We discuss the limitations of these approaches given the sociohistorical nature of race and gender. We posit that the lack of critical engagement with this nature renders databases opaque and less trustworthy. We conclude by encouraging database authors to address both the histories of classification inherently embedded into race and gender, as well as their positionality in embedding such classifications.

Contemporary American experiences of death and mourning increasingly extend onto social network sites, where friends gather to memorialize the deceased. That "everyone grieves in their own way'' may be true, but it forecloses important questions about how people evaluate these expressions, their relationship to others who are grieving, and impacts on their own experiences of grief. Drawing from mixed-methods research conducted over five years, we describe how individuals position themselves within and evaluate expressions of networked grief. We start by identifying five orientations -- reinforcing, supporting, transferring, objecting, and isolating -- that describe how individuals evaluate actions of grievers, position themselves relative to the network, and act when they encounter grief. We then describe factors and tensions that influence how individuals arrive at these orientations. Based on our findings, we argue that the design of social media can be sensitized to diverse needs by adopting a situated perspective within a dynamic post-mortem network.

Online communities provide support for those who are vulnerable, such as LGBTQ people while coming out. Research shows that social support and personal narrative construction are important when recovering from personal crises and traumatic events. As an online community focused on writing fanfiction and also consisting of a large number of LGBTQ members, transformative fandom provides an opportunity to examine the relationship between support, crisis, and narrative. Through an interview study with 31 LGBTQ fanfiction authors, our findings mirror Herman's model of trauma recovery: these spaces self-organize to support recovery work through constructing "community narratives" that help LGBTQ people establish safety when exploring their identity and build LGBTQ support structures without publicly outing themselves before they are ready, challenge stereotypes, and support others through reshaping existing media that perpetuate inaccurate or harmful LGBTQ narratives. These online communities embody "selective visibility"--that is, though not specifically designed as support structures for identity work and recovery, their design allows people to selectively find and create communities of support for stigmatized issues that they might be unable to safely seek out in other spaces. Based on lessons learned, we generate insights that can inform the design of safe support spaces online.

Online community moderators are on the front lines of combating problems like hate speech and harassment, but new modes of interaction can introduce unexpected challenges. In this paper, we consider moderation practices and challenges in the context of real-time, voice-based communication through 25 in-depth interviews with moderators on Discord. Our findings suggest that the affordances of voice-based online communities change what it means to moderate content and interactions. Not only are there new ways to break rules that moderators of text-based communities find unfamiliar, such as disruptive noise and voice raiding, but acquiring evidence of rule-breaking behaviors is also more difficult due to the ephemerality of real-time voice. While moderators have developed new moderation strategies, these strategies are limited and often based on hearsay and first impressions, resulting in problems ranging from unsuccessful moderation to false accusations. Based on these findings, we discuss how voice communication complicates current understandings and assumptions about moderation, and outline ways that platform designers and administrators can design technology to facilitate moderation.

Every day on social media, people see streams of content curated by algorithms that leverage their relationships, preferences, and identities. However, algorithms can oversimplify the complexity of people's social contexts. Consequently, algorithms can present content to people in ways that are insensitive to their circumstances. Through 19 in-depth interviews, our empirical study examines instances of contextually insensitive content through the lens of people's upsetting encounters with content about their ex-romantic partners on Facebook. We characterize the encounters our participants had with content about their exes, including where on Facebook it occurred, the types of social connections involved in the content, and participants' perceptions of why the content appeared. Based on our findings, we describe the "social periphery"---the complex social networks and data that enable inferred connections around otherwise explicit relationships---and discuss the design challenges that the periphery presents designers.

Investigations of facial analysis (FA) technologies-such as facial detection and facial recognition-have been central to discussions about Artificial Intelligence's (AI) impact on human beings. Research on automatic gender recognition, the classification of gender by FA technologies, has raised potential concerns around issues of racial and gender bias. In this study, we augment past work with empirical data by conducting a systematic analysis of how gender classification and gender labeling in computer vision services operate when faced with gender diversity. We sought to understand how gender is concretely conceptualized and encoded into commercial facial analysis and image labeling technologies available today. We then conducted a two-phase study: (1) a system analysis of ten commercial FA and image labeling services and (2) an evaluation of five services using a custom dataset of diverse genders using self-labeled Instagram images. Our analysis highlights how gender is codified into both classifiers and data standards. We found that FA services performed consistently worse on transgender individuals and were universally unable to classify non-binary genders. In contrast, image labeling often presented multiple gendered concepts. We also found that user perceptions about gender performance and identity contradict the way gender performance is encoded into the computer vision infrastructure. We discuss our findings from three perspectives of gender identity (self-identity, gender performativity, and demographic identity) and how these perspectives interact across three layers: the classification infrastructure, the third-party applications that make use of that infrastructure, and the individuals who interact with that software. We employ Bowker and Star's concepts of "torque" and "residuality" to further discuss the social implications of gender classification. We conclude by outlining opportunities for creating more inclusive classification infrastructures and datasets, as well as with implications for policy.

Augmented reality (AR) applications can leverage the full space of an environment to create immersive experiences. However, most empirical studies of interaction in AR focus on interactions with objects close to the user, generally within arms reach. As objects move farther away, the efficacy and usability of different interaction modalities may change. This work explores AR interactions at a distance, measuring how applications may support fluid, efficient, and intuitive interactive experiences in room-scale augmented reality. We conducted an empirical study (N = 20) to measure trade-offs between three interaction modalities-multimodal voice, embodied freehand gesture, and handhelds devices-for selecting, rotating, and translating objects at distances ranging from 8 to 16 feet (2.4m-4.9m). Though participants performed comparably with embodied freehand gestures and handheld remotes, they perceived embodied gestures as significantly more efficient and usable than device-mediated interactions. Our findings offer considerations for designing efficient and intuitive interactions in room-scale AR applications.

User-generated content is central to social computing scholarship. However, researchers and practitioners often presume that these users are alive. Failing to account for mortality is problematic in social media where an increasing number of profiles represent those who have died. Identifying mortality can empower designers to better manage content and support the bereaved, as well as promote high-quality data science. Based on a computational linguistic analysis of post-mortem social media profiles and content, we report on classifiers developed to detect mortality and show that mortality can be determined after the first few occurrences of post-mortem content. Applying our classifiers to content from two other platforms also provided good results. Finally, we discuss trade-offs between models that emphasize pre- vs. post-mortem precision in this sensitive context. These results mark a first step toward identifying mortality at scale, and show how designers and scientists can attend to mortality in their work.

Animated GIFs are increasingly popular in text-based communication. Finding the perfect GIF can make conversations funny, interesting, and engaging, but GIFs also introduce potentials for miscommunication. Through 24 in-depth qualitative interviews, this empirical, exploratory study examines the nuances of communication practices with animated GIFs to better understand why and how GIFs can send unintentional messages. We find participants leverage contexts like source material and interpersonal relationship to find the perfect GIFs for different communication scenarios, while these contexts are also the primary reason for miscommunication and some technical usability issues. This paper concludes with a discussion of the important role that different types of context play in the use and interpretations of GIFs, and argues that nonverbal communication tools should account for complex contexts and common ground that communication media rely on.

HCI is focused on improving the interactions we have with technology and innovating new types of interactions, as well as expanding the types of people for whom those interactions are designed. Central to these efforts is the simultaneously empowering and contested construct of the "user." This paper examines what the construct of the user highlights, as well as what it conceals. We introduce post-userism, a perspective that simultaneously acknowledges the limits of, and proposes alternatives to, the central construct of the user as proxy for the "human" in HCI. Drawing on developments across the historical trajectory of HCI, we articulate how the user is enacted across four different levels of representation-systems, interface, design process, and the ideology and identify situations where the user breaks down. Synthesizing prior work, we offer a series of strategies for grappling with such situations. In doing so, we seek to overcome the limitations imposed by the user and develop a language that will aid in evolving the foundations of HCI by asking what, exactly, we place at the center of our scholarship and design.

Most social media platforms are persistent in nature, enabling users to re-visit content at their discretion. Platforms with design features that support ephemeral communications, such as Snapchat, have become increasingly popular. During the course of our empirical study, we interviewed 15 Snapchat users about their experiences and practices. Our data reveal that Snapchat users experienced different types of loss, including media, meaning, and context loss, and developed workarounds to deal with those losses, including preemptive action and collaborative saving practices. Our findings revealed a conflict between the user's expectation of the affordance the ephemeral platforms would provide, and the actions user's followed.

As social media platforms become a larger part of sharing life, they have by necessity become a part of sharing death. In life, pop culture fans can have parasocial (one-sided, mediated) relationships with celebrities. Yet when fans of departed celebrities express their grief in public comment threads, conversations often result in disagreements about how to grieve. These disagreements consistently appear in response to the deaths of public figures, and have been broadly labeled "grief policing." We performed a thematic analysis of public Facebook comments responding to the deaths of Alan Rickman, David Bowie, and Prince. Our findings describe prominent grief policing practices and explain how commenters may be importing norms from other contexts when shared spaces consist of transient interactions that make norm formation difficult. Our findings contribute to a broader understanding of how conflicting norms affect discourse in transient online spaces. Approaching online incivility through a lens of conflicting social norm enforcement may open doors for improvements in public discourse online.

Post-mortem profiles on social network sites serve as both an archive of the deceased person's life and a gathering place for friends and loved ones. Many existing systems utilize inheritance as a model for post-mortem data management. However, the social and networked nature of personal data on social media, as well as the memorializing practices in which friends engage, indicate that other approaches are necessary. In this paper, we articulate the design choices made throughout the development of Legacy Contact, a post-mortem data management solution designed and deployed at Facebook. Building on the duties and responsibilities identified by Brubaker et al., we describe how Legacy Contact was designed to honor last requests, provide information surrounding death, preserve the memory of the deceased, and facilitate memorializing practices. We provide details around the design of the Legacy Contact selection process, the functionality provided to legacy contacts after accounts have been memorialized, and changes made to post-mortem profiles.

Digital artifacts on social media can challenge individuals during identity transitions, particularly those who prefer to delete, separate from, or hide data that are representative of a past identity. This work investigates concerns and practices reported by transgender people who transitioned while active on Facebook. We analyze open-ended survey responses from 283 participants, highlighting types of data considered problematic when separating oneself from a past identity, and challenges and strategies people engage in when managing personal data in a networked environment. We find that people shape their digital footprints in two ways: by editing the self-presentational data that is representative of a prior identity, and by managing the configuration of people who have access to that self-presentation. We outline the challenging interplay between shifting identities, social networks, and the data that suture them together. We apply these results to a discussion of the complexities of managing and forgetting the digital past.

In this work, we describe several themes that can be useful for designing tangible technology in the context of death and mourning. We explore the effectiveness of physical and digital artifacts in the process honoring a loved one who has passed away. We employ a speculative prototype called Penseive Box to explore the intersection of tangible digital memorialization practices. Using this prototype to elicit reflections on personal memorialization practices, we interviewed several individuals who had recently lost a loved one, and present the results of our initial analysis here.

Social computing technologies, such as social networking sites (SNSs), often privilege people who fit within expected, static categories. Thus, users embarking on major identity changes, such as gender transition, often encounter stress when using SNSs to interact with their online social networks. To address this problem and reflect on the design of SNSs and other social computing systems, we present the results of a comprehensive online survey of transgender and gender non-conforming SNS users. Our findings indicate that although Facebook can be a stressful place for gender transition due to difficulties of transition disclosure, support from one's Facebook network can help to mitigate some of this stress. We examine Facebook both as a site of stress and as a site of support. Better understanding the relationships between stress, disclosure, and support on SNSs for these particular users can inform technology design that will benefit people who struggle with navigating a wide range of major identity changes online.

Following the deaths of notable people, Wikipedians incorporate this new knowledge by updating or creating biographical articles. Drawing on literature from death studies and peer production, we demonstrate how the creation of these "wiki-bituaries" requires complex coordination work and highlight processes of commemoration and memorialization within socio-technical systems. Using the corpus of 6,132 articles about people who died in 2012, we examine the network relationships and contribution dynamics of users who perform this work and identify behavioral and content dynamics on the biographical articles about the deceased. The collaborations that emerge from posthumous editing of these biographies are sites of significant activity that coalesce into complex but temporary collaborations. Based on these findings, we argue that Wikipedia has re-imagined the obituary into a genre for creating memory spaces in which the death of a subject prompts a form of "death work" involving the collective re-evaluation of article content and a transition into a new mode of data stewardship.

This paper extends research on the giving and inheriting of digital artifacts by examining social network site accounts post-mortem. Given the important role that social network sites play in online bereavement practices, we conducted a series of in-depth qualitative interviews to explore issues around inheritance and post-mortem data management of Facebook accounts. We found that participants focused less on ownership of the data, and instead on the duties and potential conflicts associated with maintaining an account post-mortem. Subsequently, we argue for 'stewardship' as an alternative to inheritance for framing post-mortem data management practices. Analysis of post-mortem data management activities highlights how stewards are accountable and responsible to the deceased and various survivors. However, weighing competing responsibilities is complicated by varied relationships with disparate survivors, as well as the inability to consult with the deceased. Based on our findings, we claim that post-mortem solutions need to account for the needs of stewards in addition to those of the deceased and survivors. We suggest that a model of stewardship better accounts for the interpersonal responsibilities that accompany online data than inheritance alone.

The HIV/AIDS crisis of the 1980s fundamentally changed sexual practices of men who have sex with men (MSM) in the U.S., including increased usage of sexual health-related (SHR) language in personal advertisements. Analyzing online personal ads from Craigslist, we found a substantial increase in SHR language, from ~23% in 1988 to over 53% today, echoing continuing concern about rising HIV rates. We argue that SHR language in Craigslist ads can be used as a sensor to provide insight into HIV epidemiology as well as discourse among particular communities. We show a positive significant relationship between prevalence rate of HIV in an ad's location and use of SHR language in that location. Analysis highlights the opportunity for SHR information found in Craigslist personal ads to serve as a data source for HIV prevention research. More broadly, we argue for mining large-scale user-generated content to inform HCI design of health and other systems, and explore use of such data to examine temporal changes in language to facilitate improved user-interface design.

Location-aware mobile applications have become extremely common, with a recent wave of mobile dating applications that provide relatively sparse profiles to connect nearby individuals who may not know each other for immediate social or sexual encounters. These applications have become particularly popular among men who have sex with men (MSM) and raise a range of questions about self-presentation, visibility to others, and impression formation, as traditional geographic boundaries and social circles are crossed. In this paper we address two key questions around how people manage potentially stigmatized identities in using these apps and what types of information they use to self-present in the absence of a detailed profile or rich social cues. To do so, we draw on profile data observed in twelve locations on Grindr, a location-aware social application for MSM. Results suggest clear use of language to manage stigma associated with casual sex, and that users draw regularly on location information and other descriptive language to present concisely to others nearby.

Based on an 18-month qualitative study that included the creation and testing of design considerations and a prototype location-based information system (LBIS), this research provides empirical insight into the daily practices of a wide variety of individuals working to address food insecurity in one U.S. county. Qualitative fieldwork reveals that nonprofit organizations in the food assistance ecology engage in location-based information practices that could be enhanced by the design of a LBIS. Two practices that would benefit from a collaborative LBIS are 1) practices of matching in which nonprofit workers help individuals who are seeking assistance to food resources and 2) practices of distribution in which nonprofit workers help organizations access and deliver food resources to clients. In order to support such practices across organizations the cooperative design component of this research suggests that an LIBS should: support the role of intermediaries who engage in practices of matching and distribution; provide interactive mapping tools that match resources to need; enable organizations to control visibility over specific data; and document work and impact. This research further suggests that designers should explore the wide variety of spatial patterns that must align and overlap such that ecologies of nonprofit organizations might synergistically work together to address pressing social needs.

Gas sensors have the potential to assist cooking by providing feedback on the cooking process and by further automating cooking. In this work, we explored the potential use of gas sensors to monitor food during the cooking process. Focusing on dry cooking, we collected gas emissions using 13 sensors during trials in which food was cooked to various degrees of doneness. Using decision tree classifiers, we were able to predict doneness for waffles and popcorn with 73% and 85% accuracy, respectively. We reflect on the potential reasons for this variation and the ways in which gas sensors might reliably be used in ubicomp applications to support cooking.

Even with the investment of significant resources, video communication in professional settings has not gained mass appeal. This contrasts with the consumer space where, despite limited resources and low quality solutions, services such as Skype have seen widespread adoption. In this paper, we explore the behavior and attitudes of individuals who actively use video communication in both their personal and professional lives. We highlight similarities and differences across these two domains, with particular focus on the interpersonal relationships, spaces, and activities that each domain supports and enables. We conclude by discussing how our study leads to a new perspective that focuses on the shared experiences enabled by video communication.

The proliferation of social network sites has resulted in an increasing number of profiles representing deceased users. In this paper, we present the results of a mixed-methods empirical study of 205,068 comments posted to 1,369 MySpace profiles of users who have died. Our results reveal interesting practices surrounding issues of authorship and audience, temporal patterns in posting, and continued social networking with the dead. These results suggest that post-mortem commenting behavior blends memorializing practices with existing practices and communication patterns for social network sites. We conclude by outlining future directions for research and implications for the understanding and use of social network sites in light of a deeper understanding of post-mortem comments.

As use of, and experiences with, social media continue to grow, the systems of representation that underlie their use become increasingly influential. In this paper, we present results from empirical studies of two online communities-Facebook and craigslist Missed Connections-that highlight the ways in which this underlying infrastructure and the user practices on these sites are inherently intertwined. We make particular use of a framework first introduced by Agre that focuses on the influence of eight underlying features of computing practice: ontology, standards, instrumentation, authentication, interpretation, selection, bias, and performance. The results of this analysis indicate how representational systems do more than simply represent the physical world; they are deeply intertwined with the social and material practices of everyday life.