UMSI at CSCW 2025: Awards, Workshops and Papers
Friday, 10/17/2025
By Noor HindiThe 28th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW) will be held October 18-22 in Bergen, Norway. Several University of Michigan School of Information researchers will be presenting their work.
UMSI associate professor Nazanin Andalibi served as a workshop chair and invited mentor for the Doctoral Colloquium for this year’s conference, and research fellow Yao Lyu was part of the program committee.
Additionally, UMSI assistant professor Michaelanne Thomas is the recipient of this year’s Lasting Impact Award for her 2013 paper “Hollaback!: the role of storytelling online in a social movement organization.”
HONORABLE MENTION AWARDS
Yuling Sun, Sam Addison Ankenbauer, Yuchen Chen, Xiaojuan Ma, Zhifan Guo, Liang He
Aging in place refers to the enabling of individuals to age comfortably and securely within their own homes and communities. Continued community living creates a number of potential areas for design and, accordingly, various information and communication technologies have been employed to support older adult care. At the same time, human-led care services have been designed to support aging in place. Through a long-term ethnographic study that includes semi-structured interviews with 24 stakeholders, we consider these technology- and human-driven care infrastructures for aging in place, examining their origins, deployment, interactions with older adults, and challenges. In doing so, we reconsider the value of these different forms of older adult care, highlighting the various issues associated with using, for instance, health monitoring technology or appointment scheduling systems to care for older adults aging in place. We suggest that technology should take a ``supportive, not substitutive'' role in older adult care infrastructure and that designing for aging in place should not be synonymous with designing for independence but should, instead, consider the larger community and its dynamics.
Zoe Natalie Cullen, Angela Y Lee, Brenna Davidson, Jeff Hancock, Nicole Ellison
Personalized algorithms are central to how people discover information and engage with media online. Drawing on interviews and screen-sharing sessions with TikTok users (N=27), we extend the algorithmic crystal framework, which conceptualizes personalized algorithms as reflective surfaces through which users may interpret their experiences with content in relation to their own self-concepts. This research expands the framework to account for the interpersonal dynamics that emerge from user engagement with algorithmic feeds. We found that users who feel “seen” by the algorithm use its personalized content recommendations for social signaling: sharing content that represents themselves (“this is me”), acknowledges how they see others (“this is you”), and affirms shared identities (“this is us”). We also build on the concept of diffracted belonging—the experience of recognizing aspects of oneself in the content of diverse others—to explore how users interpret algorithmically-recommended content as reflective of the self. Our findings suggest that such moments of recognition may contribute to shifts in self-perception and support ongoing processes of identity development. Finally, we illustrate how users engage in the strategic refinement of their feeds to manage how they feel while using the platform. Our findings suggest that this process involves reflective, and sometimes effortful, negotiation with the algorithm, highlighting the co-produced nature of mood management in everyday human–algorithm interactions. Together, these findings underscore the interpersonal and psychological dynamics of interacting with personalized algorithms and provide insights into how social communication and identity work unfold in algorithmically-mediated environments.
Xinyue Chen, Kunlin Ruan, Kexin Phyllis Ju, Nathan Yap, Xu Wang
As AI tools become increasingly integrated into cognitively demanding tasks, like note-taking, questions remain about whether they enhance or compromise cognitive engagement. This paper investigates the "AI Assistance Dilemma" in note-taking, examining how varying levels of AI support impact user engagement and comprehension. In a within-subject experiment, we asked participants (N=30) to take notes during lecture videos under three conditions: \AutomatedAI (high assistance with structured notes), \IntermediateAI (moderate assistance with real-time summary, and \MinimalAI (low assistance with transcript). Results reveal that Intermediate AI yields the highest post-test scores and Automated AI the lowest. Participants, however, preferred the automated setup for its perceived ease of use and perceived lower cognitive effort, suggesting a discrepancy between preferred convenience and cognitive benefit. Our study provides insights on designing AI assistance that preserves cognitive engagement, offering implications for designing moderate AI support in cognitive tasks.
DEI RECOGNITION
Sena Kojah, Kentaro Toyama, Oliver Haimson
When ethnic minorities in the Global South use social media to document human rights abuses against them by majority groups or state actors, platforms often remove this content, thus suppressing these groups’ ability to raise awareness about their oppression. To understand ethnic minorities in the Global South’s experiences with content moderation, we conducted interviews with ethnic minority Nigerian journalists, activists, politicians, and lawyers, and digital ethnographic observation of social media posts about human rights violations. We found that participants use social media as immediate and urgent archival tools to document violence and human rights abuses against them and their communities, which would not be possible with traditional media due to systemic state exclusion. Further, we identify cultural complexities and context gaps that are frequently ignored by social media companies, who impose Global North norms and values when moderating diverse populations in the Global South. When ethnic minorities post about violence and human rights concerns, they often experience over-moderation, while harmful content that targets them with violence is often under-moderated. We argue for a specialized “social justice archive moderation” as a way to account for the documentary and archival functions that social media serves for marginalized populations in conflict zones. We argue that this approach helps to support ethnically, linguistically, culturally, and politically vulnerable populations when moderating Global South populations in high-stakes violence and human rights contexts.
Cassidy Pyle, Nazanin Andalibi
The historically controversial U.S. college admissions process is increasingly shaped by algorithmic systems, exacerbating the potential for controversies over admissions and their fairness. Despite their increased use, it is unclear how vendors who provide algorithmic admissions technologies legitimize them and how applicants perceive them. The present study combines 1) qualitative content analysis of admissions technology vendor websites and 2) interviews with college applicants, highlighting the distance between vendors’ claimed benefits for universities (e.g., increased decision-making efficiency) and applicants (e.g., “unbiased” decisions) and applicants’ perceived harms to themselves (e.g., undermining holistic review, impacting diversity, equity, and inclusion efforts). We consider the implications of algorithmic admissions decision-making, including privacy harms, discuss regulatory implications, and offer recommendations to guide algorithmic transparency efforts. However, we caution that transparency would not address some harms perceived by applicants, like inaccuracy and privacy violations.
METHODS RECOGNITION
Janet Johnson, Macarena Peralta, Mansanjam Kaur, Ruijie Sophia Huang, Sheng Zhao, Ruijia Guan, Shwetha Rajaram, Michael Nebeling
While Generative Artificial Intelligence (GenAI) is finding increased adoption in workplaces, current tools are primarily designed for individual use. Prior work established the potential for these tools to enhance personal creativity and productivity towards shared goals; however, we don't know yet how to best take into account the nuances of group work and team dynamics when deploying GenAI in work settings. In this paper, we investigate the potential of collaborative GenAI agents to augment teamwork in synchronous group settings through an exploratory study that engaged 25 professionals across 6 teams in speculative design workshops and individual follow-up interviews. Our workshops included a Mixed Reality prototype to simulate embodied collaborative GenAI agents capable of actively participating in group discussions. Our findings suggest that, if designed well, collaborative GenAI agents offer valuable opportunities to enhance team problem-solving by challenging groupthink, bridging communication gaps, and reducing social friction. However, teams' willingness to integrate GenAI agents depended on its perceived fit across a number of individual, team, and organizational factors. We outline the key design tensions around agent representation, social prominence, and engagement and highlight the opportunities spatial and immersive technologies could offer to modulate GenAI influence on team outcomes and strike a balance between augmentation and agency.
PAPERS
Wikipedia in Wartime: Experiences of Wikipedians Maintaining Articles About the Russia-Ukraine War
Laura Kurek, Ceren Budak, Eric Gilbert
How do Wikipedians maintain an accurate encyclopedia during an ongoing geopolitical conflict where state actors might seek to spread disinformation or conduct an information operation? In the context of the Russia-Ukraine War, this question becomes more pressing, given the Russian government’s extensive history of orchestrating information campaigns. We conducted an interview study with 13 expert Wikipedians involved in the Russo-Ukrainian War topic area on the English-language edition of Wikipedia. While our participants did not perceive there to be clear evidence of a state-backed information operation, they agreed that war-related articles experienced high levels of disruptive editing from both Russia-aligned and Ukraine-aligned accounts. The English-language edition of Wikipedia had existing policies and processes at its disposal to counter such disruption. State-backed or not, the disruptive activity created time-intensive maintenance work for our participants. Finally, participants considered English-language Wikipedia to be more resilient than social media in preventing the spread of false information online. We conclude by discussing sociotechnical implications for Wikipedia and social platforms.
Somayeh Molaei, Lionel Peter Robert, Nikola Banovic
Improving end-users’ understanding of decisions made by autonomous vehicles (AVs) driven by artificial intelligence (AI) can improve utilization and acceptance of AVs. However, current explanation mechanisms primarily help AI researchers and engineers in debugging and monitoring their AI systems, and may not address the specific questions of end-users, such as passengers, about AVs in various scenarios. In this paper, we conducted two user studies to investigate questions that potential AV passengers might pose while riding in an AV and evaluate how well answers to those questions improve their understanding of AI-driven AV decisions. Our initial formative study identified a range of questions about AI in autonomous driving that existing explanation mechanisms do not readily address. Our second study demonstrated that interactive text-based explanations effectively improved participants’ comprehension of AV decisions compared to simply observing AV decisions. These findings inform the design of interactions that motivate end-users to engage with and inquire about the reasoning behind AI-driven AV decisions.
Using Off-the-Shelf Harmful Content Detection Models: Best Practices for Model Reuse
Angela Schopke-Gonzalez, Siqi Wu, Sagar Kumar, Libby Hemphill
Supervised machine learning is a common approach for automated harmful content detection to support content moderation. This approach relies on data annotated by humans to train models to recognize classes of harmful content. For detection tasks, researchers or content moderation communities typically either design their own annotation tasks to generate training data for new harmful content detection models, or use off-the-shelf (OTS) pre-trained harmful content detection models. OTS model reuse can enable detection tasks in resource-constrained contexts and can help to reduce the environmental impact of training new models -- an energy-intensive process. However, given the plethora of OTS models now available for reuse, determining which OTS model to reuse for a particular task and how to use it can be challenging, especially given that many of these models have been developed for specific contexts that are not always easily transferred onto others. This work aims to provide best practices for reusing OTS models for harmful content detection tasks. By using content analysis and statistical methods to evaluate assumptions about OTS model utility and reusability, we show that model reusers cannot assume that a model claimed to detect a particular concept, will actually detect that concept. Instead, based on our findings, we offer a decision tree for how to assess whether an OTS model would be appropriate for reuse for a new harmful content detection task. This decision tree directs model reusers to critically assess concept definitions, annotation task design, and additional features specified in our content analysis codebook to identify expected model output, and consequently evaluate whether that OTS model is appropriate for reuse for a new detection task.
Time is On My Side: Dynamics of Talk-Time Sharing in Video-chat Conversations
Kaixiang Zhang, Justine Zhang, Cristian Danescu-Niculescu-Mizil
An intrinsic aspect of every conversation is the way talk-time is shared between multiple speakers. Conversations can be balanced, with each speaker claiming a similar amount of talk-time, or imbalanced when one talks disproportionately. Such overall distributions are the consequence of continuous negotiations between the speakers throughout the conversation: who should be talking at every point in time, and for how long? In this work we introduce a computational framework for quantifying both the conversation-level distribution of talk-time between speakers, as well as the lower-level dynamics that lead to it. We derive a typology of talk-time sharing dynamics structured by several intuitive axes of variation. By applying this framework to a large dataset of video-chats between strangers, we confirm that, perhaps unsurprisingly, different conversation-level distributions of talk-time are perceived differently by speakers, with balanced conversations being preferred over imbalanced ones, especially by those who end up talking less. Then we reveal that---even when they lead to the same level of overall balance---different types of talk-time sharing dynamics are perceived differently by the participants, highlighting the relevance of our newly introduced typology. Finally, we discuss how our framework offers new tools to designers of computer-mediated communication platforms, for both human-human and human-AI communication.
Threat Modeling Healthcare Privacy in the United States
Nora McDonald, Alan Luo, Phoebe Moh, Michelle Mazurek, Nazanin Andalibi
The landscape of digital privacy risks faced by individuals seeking abortions has grown increasingly complex following the overturn of Roe v. Wade. Reproductive healthcare providers are uniquely positioned to offer critical privacy guidance. We conducted interviews with 22 reproductive healthcare providers across the U.S. to explore their perceptions of privacy threats for abortion-seeking patients and the types of guidance they provide. Our findings show that providers are most concerned about privacy risks for vulnerable patientsÑ minors, individuals seeking gender-affirming care, and those in abusive relationshipsÑparticularly regarding information that could be intercepted by people close to them, such as partners or relatives. However, providers generally do not perceive government surveillance or hostile actors as major threats to abortion-seeking patients. We conclude with an updated notion of informed consent and preliminary recommendations for ways healthcare providers can revise their threat models to better support the privacy of abortion-seeking patients.
This paper critically examines flexible content creation conducted by Key Opinion Consumers (KOCs) on a prominent social media and e-commerce platform in China, Xiaohongshu (RED). Drawing on nine-month ethnographic work conducted online, we find that the production of the KOC role on RED is predicated on the interactions and negotiations among multiple stakeholders---content creators, marketers, consumer brands (corporations), and the platform. KOCs are instrumental in RED influencer marketing tactics and amplify the mundane and daily life content popular on the platform. They navigate the dynamics in the triangulated relations with other stakeholders in order to secure economic opportunities for producing advertorial content, and yet, the labor involved in producing such content is deliberately obscured to make it appear as spontaneous, ordinary user posts for the sake of marketing campaigns. Meanwhile, the commercial value of their work is often underestimated and overshadowed in corporate paperwork, platform technological mechanisms, and business models, resulting in and reinforcing inadequate recognition and compensation of KOCs. We illustrate the precarious nature of a form of creativity-driven digital employment through the case of KOCs on the RED platform by demonstrating how this work is made informal. This perspective offers a new lens to understand content creation labor that is indispensable yet unrecognized by the social media industry. We advocate for a contextualized and nuanced examination of how labor is valued and compensated and urge for better protections and working conditions for informal laborers like KOCs.
The Development of a New Measure of Collective Digital Literacy: Community Digital Capacity
Tawanna Dillahunt, Kerby Shedden, Mila Ekaterina Filipof, Soyoung Lee, Mustafa Naseem, Kentaro Toyama, Julie Hui
This article theorizes and proposes a novel construct, community digital capacity, to measure collective digital capacity at a community level. Community digital capacity is the extent to which the culture, infrastructure, and digital competence of family and community enable and support digital practices. We address a critical gap in individual digital literacy assessments and address limitations with existing theories that do not show digital inequities in the context of underlying systemic and structural challenges posed by one's social position. Building on insights from Computer Supportive Cooperative Work and Social Computing and Human-Computer Interaction for Development communities, we recognize that digital training initiatives must shift toward critical cultural and social practices that encourage full participation in community affairs. Accordingly, we created 28 items covering three domains---individual, social, and infrastructure. We conducted cognitive interviews with a public housing community to refine the items and capture the construct fully. We assessed their factor structure in two Southeastern Michigan cohorts. After dropping eight items based on contribution to Standardized Root Mean Square Residual (SRMR), the public housing residents exhibit a two-factor structure (SRMR=0.09) consisting of nearly independent factors for the individual and social domains, with all items loading positively on their respective domain. We contribute an initial measure for researchers and practitioners to assess community members' access to shared digital resources and support, offering a tool to assess broader social and structural factors contributing to the digital divide.
Tyler Musgrave, Alexis Bell, Sarita Schoenebeck
Since the beginning of the COVID-19 pandemic, videoconferencing platforms have become a staple to our social, educational, and work lives. Additionally, the United States continues to grapple with criminal justice reform, employing processes like restorative justice to replace antiquated punitive approaches to justice with a focus on the multifaceted needs of communities. Essential to the delivery of restorative justice, restorative justice practitioners are community members trained in the process and approaches of restorative practice. Like everyone else, restorative justice practitioners have transitioned from historically in-person facilitation to online facilitation, integrating videoconferencing platforms into their justice-related work. Diverse communities within Human-Computer Interaction (HCI) have been examined in light of their digital transitions and the utilization of various digital tools, including videoconferencing platforms prompted by the pandemic. However, the distinctive perspective of restorative justice practitioners in adapting restorative approaches to an online format is unexplored. Therefore, our research examines how restorative practitioners use videoconferencing platforms for justice-related interventions. To do so, we conducted six semi-structured focus group interviews with 22 US-based restorative facilitators to learn about their experiences with online delivery of restorative justice. Our research revealed that restorative facilitators uphold restorative values in their online delivery by embracing the restorative facilitation process and fostering dialogue. Furthermore, we posit that the Human-Computer Interaction (HCI) community can acquire valuable insights from restorative practitioners on nurturing and sustaining intimacy and connection online.
Soyoung Lee, Julie Hui, Tawanna Dillahunt
Digital skills are essential for engaging in employment, healthcare, education, and government services. However, the digital divide remains a social inequality, especially among marginalized populations. Through a community-engaged research approach, we conducted a digital skills learning intervention in a U.S. public housing community, where residents frequently face socioeconomic challenges and limited access to digital resources. Public housing is a community seldom explored in CSCW and HCI research and provides a unique context to study the ongoing digital skills gap. Through the lens of situated learning theory, we study how sociocultural factors impact the efficacy of a community-based computer skills learning intervention. Specifically, we examine how the public housing community organized various resources---online learning materials, instructors, peer social support, and on-the-job learning opportunities---for digital skills development. Notably, the training leveraged instructor critical care and peer support to develop a learning community between residents and leaders of the community NGO that continued beyond the formalized training program. We contribute to CSCW and HCI work on collective and assets-based approaches to enhancing digital capacity. Our work provides implications for building collective grassroots digital skills learning infrastructure that could create new digitally-engaged employment opportunities.
SocialSim: An Open Source Platform for Conducting Behavioral Intervention Research on Social Media
Zainab Agha, Matthew Martinez, Naima Samreen Ali, Dominic DiFranzo, Pamela J. Wisniewski
Understanding adolescent online behavior is critical for designing effective online safety interventions; yet, studying such behavior ethically and realistically remains a challenge. To address this, we present SocialSim, a social media simulation tool that builds on an open-source platform designed to enable in situ experimentation in a safe, controlled environment. SocialSim replicates key features of social platforms, including feed, profiles, friending, and private messaging while offering a flexible architecture for researchers to study user interactions and deploy behavioral interventions, such as real-time online safety nudges within direct messaging, which are unsupported in other platforms. We used this tool with a Wizard-of-Oz approach, combined with automated, as well as scripted interactions which allows researchers to simulate lifelike social media while maintaining a safe environment for youth. This demonstration presents the design, technical implementation, and future research implications for this simulation as a sandbox for evaluating online safety interventions, and as a generalizable platform for ethically studying human behavior in an ecologically valid social media environment.
Signals in the Noise: Decoding Unexpected Engagement Patterns on Twitter
Yulin Yu, Houming Chen, Daniel Romero, Paramveer Dhillon
Social media platforms offer users multiple ways to engage with content—likes, retweets, and comments—creating a complex signaling system within the attention economy. While previous research has examined factors driving overall engagement, less is known about why certain tweets receive unexpectedly high levels of one type of engagement relative to others. Drawing on Signaling Theory and Attention Economy Theory, we investigate these unexpected engagement patterns on Twitter\footnote{The social media platform has been renamed to `X,' however our study data is from the time-period prior to the name change.}, developing an ``unexpectedness quotient'' to quantify deviations from predicted engagement levels. Our analysis of over 600,000 tweets reveals distinct patterns in how content characteristics influence unexpected engagement. News, politics, and business tweets receive more retweets and comments than expected, suggesting users prioritize sharing and discussing informational content. In contrast, games and sports-related topics garner unexpected likes and comments, indicating higher emotional investment in these domains. The relationship between content attributes and engagement types follows clear patterns: subjective tweets attract more likes while objective tweets receive more retweets, and longer, complex tweets with URLs unexpectedly receive more retweets. These findings demonstrate how users employ different engagement types as signals of varying strength based on content characteristics, and how certain content types more effectively compete for attention in the social media ecosystem. Our results offer valuable insights for content creators optimizing engagement strategies, platform designers facilitating meaningful interactions, and researchers studying online social behavior.
Olivia K. Richards, Tiffany Veinot
Chronic disease management requires numerous family-based activities. Although HCI has investigated family-based chronic disease management, there is no systematic basis for technology design. Routines support adherence; thus, we used routines theory to: investigate chronic disease management activities in families; the roles of family, patients, and artifacts; activity routinization; and routine interrelationships. The 2-year study included 38 families managing type 2 diabetes and/or HIV/AIDS. Data collection involved individual and family group interviews, surveys, and home tours. Families performed 14 chronic disease management activities within five interrelated cycles, and one less-connected activity. Most families included both family members and patients in activitiesÑalthough this could be problematic. Activities were typically only moderately routinized and followed cyclical activity patterns joined by sequential or concurrent interdependence. A medication-taking routine ecology had coordination difficulties. Results surface design implications for a potentially powerful new class of technologies to support family-based chronic disease management routines.
MeetMap: Balancing AI Assistance and User Agency for Effective Real-Time Sense-Making in Meetings
Xinyue Chen, Nathan Yap, Xinyi Lu, Aylin Gunal, Xu Wang
Video meeting platforms display conversations linearly through transcripts or summaries. However, ideas during a meeting do not linearly emerge. We leverage LLMs to create dialogue maps in real-time to help people visually structure and connect ideas. Balancing the need to reduce the cognitive load on users during the conversation and give users sufficient control when using AI-generated content, we explore two human-AI collaborative methods. In Human-Map, AI generates summaries of conversations as nodes, and users create dialogue maps with the nodes. In AI-Map, AI produces dialogue maps where users can make edits. We ran a within-subject experiment with ten pairs of users, comparing the two MeetMap variants and a baseline. Users preferred MeetMap to traditional methods for note-taking, which aligned better with their mental models of conversations. Users liked the ease of use for AI-Map due to the low effort demands and appreciated the hands-on opportunity in Human-Map for sense-making. This work informs the future design of AI-assisted tools for real-time cognitive scaffolding in meetings by emphasizing the necessity to balance AI assistance with synchronicity and user agency to enhance collaborative sense-making.
Nadia Karizat, Nora McDonald, Nazanin Andalibi
The overturning of Roe v. Wade in 2022 by the U.S. Supreme Court in Dobbs v. Jackson exposed and exacerbated existing gaps in reproductive privacy. In the post-Roe era, aggressive surveillance by both government and private entities has made real and heightened concerns about privacy violations for people capable of pregnancy (PCOP). We investigate PCOPs' reproductive privacy concerns and the strategies they use to address these concerns post-Roe. We conducted semi-structured interviews with 18 adult cisgender women and transgender men in the U.S. Our findings show that for PCOPs, privacy risks are both persistent and anomalous, imposing what we conceptualize as reproductive privacy labor, a type of safety and data work that is, to them, both necessary and exhausting. We introduce the conceptual framework Sociotechnical Reproductive Privacy, outlining the relationships between actors, technologies, and identities that are implicated in the many contexts of reproductive privacy vulnerabilities post-Roe. We conclude with considerations for research and design, and explore the utility of approaches like refusal and regulation (e.g., technical, policy) in promoting sociotechnical reproductive privacy. This research underscores the urgent need to address the intersection of reproductive rights, privacy, and technology, offering insights into how affected individuals navigate and manage their reproductive health decisions in an increasingly surveilled sociotechnical landscape.
Olivia K. Richards, Allison Spiller, Carol F. Scott, Tiffany C. Veinot
The COVID-19 pandemic caused a global disruption of daily routines. Children with behavioral disabilities were particularly impacted, losing access to critical face-to-face behavioral health services. In response, many providers and parents attempted to recover these routine services, primarily with digital technology. In April-July 2020, we conducted a mixed-methods study with parents of children with behavioral disabilities. Using a six-week survey study followed by semi-structured interviews, we identified how disrupted behavioral health routines impacted children, and how care teams recovered these services using digital technology. The recovery of children's behavioral health services was delayed, resulting in negative consequences for the children. The stoppage of services undermined care teams' coordination mechanisms, necessitating they establish digital communication channels. This digital communication supported the recovery of some services, but not most.
A Systematic Literature Review of Infrastructure Studies in SIGCHI
Yao Lyu, Jie Cai, John M Carroll
Infrastructure is an indispensable part of human life. In the past decades, the Human-Computer Interaction (HCI) community has paid increasing attention to human interactions with infrastructure. In this paper, we conducted a systematic literature review on infrastructure studies in SIGCHI, one of the most influential communities in HCI. We collected a total of 174 primary studies; the corpus includes studies published between 2006 and 2023. Most of the studies are inspired by Susan Leigh Star’s notion of infrastructure. We discover three themes of infrastructure studies, including infrastructure as material, infrastructure as relationship, and infrastructure as practice. We foreground the overall trend of infrastructure studies in SIGCHI, which focuses on informal infrastructural activities in various socio-technical contexts. Especially, we discuss studies that problematize infrastructures and alert the HCI community about the underlying harmful side of infrastructure.
Kayah Williams, Sarita Schoenebeck
Federal e-government services are increasingly replacing traditional methods of interaction in providing immigrants access to information often vital to their immigration processes. As the nature of these services evolve to online systems, immigrants are confronted with navigating them. In this study, we examined the usability and efficacy of the United States Citizenship and Immigration Services website (USCIS.gov) through 28 semi-structured interviews with documented African immigrants, representing 7 countries and varying legal status classifications, such as permanent resident or citizen. Our findings describe common experiences, perceptions and difficulties navigating the U.S. Government’s online platform; contributing factors include inadequate virtual assistance from Emma - USCIS’s computer-generated chatbot, delayed or inconsistent system alerts about case status, and limited website navigation design for visually impaired users, leading to feelings of anxiety while waiting for pending filings. To conceptualize this unique interaction between immigrants and technology, we propose a “digital border wall”, the virtual space where an immigrant encounters the host country and directly accesses its systems, tools, and policies. As the employment of online immigration services by governing entities increase, so should research into the critical challenges they may impose on an already strained U.S. immigration system.
WORKSHOPS
Ridley Jones LeDoux, Seolha Lee, Pelle Tracey, Rachel Warren, Trine Rask Nielsen, Andrew Hamann
Governments at all levels use a dizzying variety of technology systems to coordinate work with each other, provide services, analyze data, and plan for the future. The use of these systems impacts all of us politically and personally, and their effective or ineffective functioning is bound up in immensely complex systems of power, ideology, and infrastructural investment. However, governments as design settings have been underexplored and undertheorized thus far in CSCW. In this one-day hybrid workshop, we will bring together a community of researchers who have an interest in organizational and political analyses of technology design and implementation in the public sector. In so doing, we will examine current and emerging CSCW approaches to understanding organizational realities of tech development and implementation in the public sector and compile resources and a research agenda to facilitate future research.
Design for Hope: Cultivating Deliberate Hope in the Face of Complex Societal Challenges
JaeWon Kim, Jiaying “Lizzy” Liu, Lindsay Popowski, Cassidy Pyle, Ahmer Arif, Gillian Hayes, Alexis Hiniker, Wendy Ju, Florian Mueller, Hua Shen, Sowmya Somanath, Casey Fiesler, Yasmine Kotturi
Design has the potential to cultivate hope in the face of complex societal challenges. These challenges are often addressed through efforts aimed at harm reduction and prevention---essential but sometimes limiting approaches that can unintentionally narrow our collective sense of what is possible. This one-day, in-person workshop builds on the first Positech Workshop at CSCW 2024 by offering practical ways to move beyond reactive problem-solving toward building capacity for proactive goal setting and generating pathways forward. We explore how collaborative and reflective design methodologies can help research communities navigate uncertainty, expand possibilities, and foster meaningful change. By connecting design thinking with hope theory, which frames hope as the interplay of ``goal-directed,'' ``pathways,'' and ``agentic'' thinking, we will examine how researchers might chart new directions in the face of complexity and constraint. Through hands-on activities including problem reframing, building a shared taxonomy of design methods that align with hope theory, and reflecting on what it means to sustain hopeful research trajectories, participants will develop strategies to embed a deliberately hopeful approach into their research.
Co-Constructing the Future of Digital Intimacy
Chris Geeng, Allison McDonald, Lucy Qin, Amna Batool, Oliver Haimson, Jevan Hutson, Elissa Redmiles, Zahra Stardust, Miranda Wei, Douglas Zytko, Annette Masterson
The Internet, artificial intelligence, and other emerging technologies have transformed the way humans can interact with each other and express romance, sex, and other forms of intimacy. Digital intimacy, including online dating, sexual/intimate content sharing, online sex work, and romantic chatbots, has grown ubiquitous. This can both be a source of great joy, such as when connecting remote partners and supporting sexual self-expression, and a source of harms, including but not limited to image-based sexual abuse, deepfakes, location privacy violations, and technology-enabled intimate partner violence. As new technologies continue to transform digital intimacy, this workshop aims to create a sex-positive space for digital intimacy researchers as we imagine and build a world where everyone is able to safely engage in consensual intimate activities with dignity and agency.
Beyond Information: Online Participatory Culture and Information Disorder
Nina Lutz, Stephen Prochaska, Laura Kurek, Marianne Aubin Le Quere, Jason Greenfield, Joseph Schafer, Phil Tinn, Daniel Thilo Schroeder, Shiva Darian, Sukrit Venkatagiri, Ahmer Arif, Anirban Sen, Joyojeet Pal, Kate Starbird
Information disorder (i.e. the proliferation of misinformation, disinformation, propaganda, and hate speech) is contributing to intensifying global democratic backsliding, and diminished abilities to understand and address difficult challenges across diverse domains, such as health, migration, and climate science. A contributing factor to information disorder is the internet’s participatory, collaborative, and remix culture, with platforms creating pathways for online audiences to create and spread problematic information. Researchers studying information disorder have become targets of disinformation and harassment campaigns, increasing burnout and underscoring the pressing need and ongoing challenges of conducting this research. These challenges stress the importance of scholars from diverse backgrounds coming together to build networks that increase both the quality of scholarship and capacities to protect and care for targeted researchers. In this CSCW workshop, attendees will identify which directions of empirical research, methods, perspectives, interventions, public communications, and other actions should be prioritized as the community seeks to continue combating information disorder in this difficult climate. Scholars will then share and reflect upon concerns and harms they have endured in pursuing this work, sharing resources that have helped them through these challenges, identifying new potential resources, and opportunities to support one another.
Augmenting Collaborative Problem-Solving: Exploring the Design and Use of GenAI for Groupwork
Janet Johnson, Steven Rick, Jens Emil Sloth Gronbaek, Emily Wong, Ming Yin, Michael Nebeling, Mark Klein, Mark Ackerman, Thomas Malone
Complex problem-solving and creative work in the real world are rarely individual endeavors and typically unfold within teams and group settings. While advancements in generative artificial intelligence (GenAI) have shown promise in augmenting creativity and productivity, these tools are primarily designed for individual use and overlook group dynamics and the collaborative aspects of teamwork. This workshop will provide a platform for researchers and practitioners to explore the design of future human-AI groups across four key themes: (1) the role of GenAI in group settings, (2) collaborative and multimodal interactions with GenAI, (3) evaluating GenAI’s influence within groups and designing for appropriate reliance, and (4) evolving group practices in the presence of GenAI. We hope to build a community and construct alignment across participants around how to pursue research that understands how GenAI can augment, undermine, or bring new practices to collaborative settings and groupwork.
Nadia Karizat, Nazanin Andalibi
Identity facets such as gender, sexuality, and race shape consent processes, including in dating. Increasingly, dating apps play important roles in consent exchange processes. Examining consent in gendered and racialized communities, as mediated by dating apps, is an overlooked yet important space for illuminating the interplay between identity, technology, and consent. We draw from a guided reflective writing questionnaire (N=20) and semi-structured interviews (N=13) with self-identified second- and subsequent Arab and Southwest Asian and North African (SWANA) diaspora generations in the U.S. We investigate participants’ online dating experiences with attention to consent-related values, behaviors, and experiences. Findings highlight the U.S. Arab/SWANA diaspora’s technocultures of consent – a conceptual framework we use to describe the understandings and practices of consent that are influenced, co-produced, or expressed by the interaction between technology and people. We demonstrate how the technocultures of consent conceptual framework reveals connections between individuals’ identities and social positions, consent-related beliefs and behaviors and technology design, norms, and expectations. We also introduce the concepts of networked consent and consent concept alignment tests, and offer design considerations to promote consent for all.
PANELS/SIGs
Guo Freeman, Elizabeth Mynatt, Cliff Lampe, Heloisa Candello, Kori Inkpen, Nitesh Goyal
Recently, we have witnessed emerging research agendas on explicating new opportunities, risks, and harm of generative AI from a CSCW perspective. Built upon these ongoing conversations and dialog, we believe that beyond merely discussing generative AI’s impacts on specific CSCW research directions, it is equally critical to promote in-depth discussions across academia and industry to reflect upon generative AI’s multidimensional impacts on the overall CSCW landscape, including: how we train and educate our next generation of CSCW and HCI researchers in the age of generative AI, how generative AI is affecting the tech industry trajectories and our students’ future job prospects in the tech industry, how we can better approach academia-industry collaboration to deal with such impacts, and how the growing focus on generative AI may (re)shape funding opportunities for future CSCW research topics. Featuring a group of panelists who are academic leaders in HCI/CSCW education and industry experts, the goal for this panel is to promote community-wide discussions and collective reflections on these key questions that are crucial for sustaining the future of CSCW.
Evaluating Research from and About the Global Majority
Farhana Shahid, Hellina Hailu Nigatu, Syed Ishtiaque Ahmed, Michaelanne Thomas, Abigail Oppong
CSCW and HCI research from and about the Global Majority is often misunderstood and undervalued in the current academic review processes. This SIG invites collective discussion on the often-overlooked issue of how the review process affects collective knowledge production about Global Majority communities. By centering the experiences of researchers working in the Global Majority, we aim to highlight the power dynamics and normative assumptions about non-WEIRD research subjects and researchers. We are an interdisciplinary group driven by concerns about equity, structural discrimination, and global disparities in knowledge production, and we hope to find solutions to these challenges. Through our SIG, we aspire to explore strategies to address WEIRD assumptions in the review process and co-learn with fellow researchers on how positionality affects the review process for and about the Global Majority.
Angela Schopke-Gonzalez, Kellie Dunn, Shaowen Bardzell, Federico Bomba, Barbara Nino Carreras, Makayla Lewis, Maria Murray
The last five years have resulted in substantial changes to how computing affects work, how work affects computing, and how work and computing operate in tandem to affect society. From advances in automation, artificial intelligence, and virtual/extended reality, to the entrenchment of hybrid and remote work arrangements, and the documented harmful societal impacts that computing work has produced, these changes to computing-work relationships raise concern and opportunities to reimagine these relationships in new ways. CSCW has an opportunity and a responsibility to ensure that the kinds of futures we imagine and enact benefit workers, communities, and future generations. Artistic research is well-positioned to help us not only understand, but imagine new pathways forward in response to pressing CSCW questions. By hosting a panel of experts in artistic methods well-equipped to help us imagine these futures, we expect to lay the groundwork for mutually respectful cross-disciplinary collaboration between arts and computing that makes more space in our field for different kinds of thinking, approaches to problems, and new imaginaries.
POSTERS
Erica Shusas, Andrea Forte
The rapid adoption of AI chatbots has sparked widespread debates about how their prevalence will impact higher education. This late-breaking work presents findings from an exploratory study comprised of a survey of 199 university students and 8 semi-structured follow-up interviews on university students' use and assessment of AI chatbots as an information resource compared with search engines for a variety of topics. We found university students from varied disciplines view chatbots as a valuable all-purpose information tool for most topics for several reasons, including their perceived versatility and single-answer response format and their provision of emotional support. We discuss the implications of these findings for design and future research directions.
Challenges of Providing Social Support on a Women-Centric Platform: Insights from REDnote
Na Li, Chuhao Wu, Hongyand Zhou, Huiran Yi, Jie Cai, John Carroll
Online peer support plays a critical role in helping women navigate personal and social challenges. While prior research has examined women's support behaviors on mainstream, male-dominated platforms, less is known about how such support unfolds in women-centric environments. This study investigates social support practices on REDnote. Through in-depth interviews with 18 female users, we explore how emotional support is shared and how users navigate a space shaped by both personal expression and commercial activity. Our findings reveal a dual role of commercialization: while product-centered content can foster community bonding, it also erodes trust when emotional narratives are used for marketing purposes. We identify key socio-technical barriers to support, including visibility concerns, privacy risks, and conflict avoidance. We offer recommendations to improve transparency, support safe interaction, and strengthen privacy controls, contributing to the development of more inclusive and supportive online communities.
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