bluuebunny/crossref_metadata_2025 huggingface dataset:
Understanding Online Discussion Across Difference: Insights from Gun Discourse on Reddit
Authors: Magu Rijul , Mathan Kumar Nivedhitha , Liu Yihe , Koo Xander , Yang Diyi , Bruckman Amy
Published: November 2024

When discussing difficult topics online, is it common to meaningfully engage with people from diverse perspectives? Why or why not? Could features of the online environment be redesigned to encourage civil conversation across difference? To investigate these questions, we need to explore them in a particular context. In this paper, we study discussions of gun policy on Reddit, with the overarching goal of developing insights into the potential of the internet to support understanding across difference. We use two methods: a clustering analysis of Reddit posts to contribute insights about
what
people discuss, and an interview study of twenty Reddit users to help us understand
why
certain kinds of conversation take place and others don't. We find that the discussion of gun politics falls into three groups: conservative pro-gun, liberal pro-gun, and liberal anti-gun. Each type of group has its own characteristic topics. While our subjects state that they would be willing to engage with others across the ideological divide, in practice they rarely do. Subjects are siloed into like-minded subreddits through a two-pronged effect, where they are simultaneously pushed away from opposing-view communities while actively seeking belonging in like-minded ones. Another contributing factor is Reddit's "karma" mechanism: fear of being downvoted and losing karma points and social approval of peers causes our subjects to hesitate to say anything in conflict with group norms. The pseudonymous nature of discussion on Reddit plays a complex role, with some subjects finding it freeing and others fearing reprisal from others not bound by face-to-face norms of politeness. Our subjects believe that content moderation can help ameliorate these issues; however, our findings suggest that moderators need different tools to do so effectively. We conclude by suggesting platform design changes that might increase discussion across difference.
Privacy for Groups Online: Context Matters
Authors: Choksi Madiha Zahrah , Balso Ero , Kreuter Frauke , Nissenbaum Helen
Published: November 2024

The pervasive influence of online activities in our lives, encompassing personal connections, professional engagements, and e-commerce, has amplified concerns about privacy. However, existing privacy research has predominantly concentrated on the individual level, paying less attention to the privacy practices and strategies adopted by online social groups. This research gap calls for a renewed focus on understanding and addressing privacy challenges specific to online group settings.

In this paper we explore the privacy needs of online groups through the lens of Contextual Integrity. We perform two complementary studies: semi-structured qualitative interviews of Facebook Groups users (n=17), and a large-scale survey of individuals organizing in groups on Facebook, Discord, and Reddit (n=4486). We investigate the privacy needs of different contextual groups, and locate the presence of contextual norms, contextual member roles, explicit and implicit rules, and privacy concerns. We trace how this complex interplay informs privacy expectations, needs, and negotiations across groups. We find that technical systems provide limited tools to effectively enforce group privacy, allowing individuals to compromise privacy norms. Based on these findings, we offer recommendations to support the design of privacy controls for online groups.
Please Understand My Disability: An Analysis of YouTubers' Discourse on Disability Challenges
Authors: Niu Shuo , Liu Li , Bian Yali
Published: November 2024

Video-sharing platforms offer a unique avenue for people with disabilities (PWDs) to highlight their experiences, including the challenges and accessibility barriers they face. While creators with disabilities effectively use these platforms to share their life struggles and advocate for societal changes, the scope of research exploring the nature of the discourse activities related to disability challenges remains limited. Our study addresses this gap by conducting a comprehensive qualitative content analysis of 468 videos posted by YouTubers with a range of disabilities, including vision, speech, mobility, hearing, and cognitive and neural impairments. Our findings reveal a predominant discussion on stigma and lack of support. YouTube is also used to share difficulties related to communication and systemic problems. Creators with disabilities also share experiences with technologies and public and private environments, through which they discuss accessibility issues and solutions. Building on our analysis, we propose future research directions aimed at enhancing the experience and support for disability communities on video-sharing platforms.
Digital Gig Work Under Unstable Energy Infrastructures: Invisible Workarounds and Alternative Imaginaries
Authors: Doggett Olivia , Gram Sarah , Jit Sophia , Harb Basel , Harb Houssam , Shafik Mohamad , Soden Robert , Ratto Matt
Published: November 2024

Digital upskilling and remote work are frequently presented as pathways for displaced communities who face political, economic crises and employment barriers. The viability and stability of available energy infrastructures for these communities is critical to the long-term success of these proposed job trajectories. In this paper, we investigate how 17 Syrian refugees living in Lebanon navigate their local unstable energy infrastructures to conduct digital gig work and receive digital training. We found that digital gig work and training are workarounds to the political and social inaccessibility of local labor markets for refugees and that participants rely on a series of material energy strategies to optimize their electricity access. We argue that to support refugees conducting digital gig work and training, we must recognize and account for ecological, social, and technological limitations and frailties in the development of technological solutions and draw our attention to how infrastructures are perpetually undergoing processes of breakdown, repair, and renewal. We argue that this attention to ongoing transformation and renewal creates new opportunities for productive and creative reconfiguration as well as modes through which CSCW may intervene in unstable energy contexts. From this perspective, we emphasize the importance of better resourcing displaced communities with information regarding energy access and supporting them in establishing and strengthening their own visions for alternative energy systems and employment pathways.
Nudging with Narrative Visualization: Communicating to a Young Adult Audience in the Pandemic
Authors: Errey Nina , Liang Christy Jie , Leong Tuck Wah , Chen Yongqing , Vally Hassan , Bennett Catherine M.
Published: November 2024

Effective narrative visualization communicates information by integrating story-telling and data visualization in a comprehensible, compelling manner. The compelling aspect of effective narrative visualization consequentially results in the potential to shift the attitude of an audience. However, there is much to understand about how narrative visualization can best be designed to influence target audiences. This paper focuses on an empirical experiment where we examined the effects of two communication strategies - anthropomorphism and personal identification - on a young adult audience. In particular, we wanted to understand which strategy, when integrated into narrative visualization, can nudge a specific audience's attitude towards greater consideration in the context of the COVID-19 pandemic. Our results indicated that the personal identification communication strategy was the most successful in nudging participants. This study contributes a better grasp of how technologies such as narrative visualization, using different communication strategies, can deliver more targeted messaging.
Intermediation: Algorithmic Prioritization in Practice in Homeless Services
Authors: Tracey Pelle , Garcia Patricia
Published: November 2024

Homelessness is a significant and growing crisis in the United States. In an effort to more efficiently and fairly distribute limited housing resources, jurisdictions across the US have adopted algorithmic prioritization systems to help select which unhoused people should receive resources. Given the impact of algorithmic prioritization on the lives of unhoused people, there is a need to more fully examine how these systems are implemented in practice by frontline workers such as social service workers. In this paper, we present a qualitative study that draws on interviews and artifact walkthroughs with fifteen social service workers to examine how they interacted with algorithmic prioritization systems as part of their job duties. We found that social service workers employed discretionary work practices to mediate between the rigid formats of algorithmic prioritization systems and the messy, situated realities of homelessness. We term these discretionary work practices 'intermediation' and provide four examples that illustrate how our interlocutors were able to maintain their commitment to advocacy and to express their expertise despite the automation of major aspects of their professional decision-making. These work practices, which we argue were motivated by care for clients, as well as a desire to preserve professional autonomy, lead us to conclude that discretion cannot easily be removed from bureaucratic systems and that removing discretion is not necessarily a desirable outcome.
CommSense: A Wearable Sensing Computational Framework for Evaluating Patient-Clinician Interactions
Authors: Wang Zhiyuan , Hassan Nusayer , LeBaron Virginia , Flickinger Tabor , Ling David , Edwards James , Wu Congyu , Boukhechba Mehdi , Barnes Laura E.
Published: November 2024

Quality patient-provider communication is critical to improve clinical care and patient outcomes. While progress has been made with communication skills training for clinicians, significant gaps exist in how to best monitor, measure, and evaluate the implementation of communication skills in the actual clinical setting. Advancements in ubiquitous technology and natural language processing make it possible to realize more objective, real-time assessment of clinical interactions and in turn provide more timely feedback to clinicians about their communication effectiveness. In this paper, we propose CommSense, a computational sensing framework that combines smartwatch audio and transcripts with natural language processing methods to measure selected "best-practice'' communication metrics captured by wearable devices in the context of palliative care interactions, including understanding, empathy, presence, emotion, and clarity. We conducted a pilot study involving N=40 clinician participants, to test the technical feasibility and acceptability of CommSense in a simulated clinical setting. Our findings demonstrate that CommSense effectively captures most communication metrics and is well-received by both practicing clinicians and student trainees. Our study also highlights the potential for digital technology to enhance communication skills training for healthcare providers and students, ultimately resulting in more equitable delivery of healthcare and accessible, lower cost tools for training with the potential to improve patient outcomes.
Challenges in Restructuring Community-based Moderation
Authors: Tran Chau , Take Kejsi , Champion Kaylea , Hill Benjamin Mako , Greenstadt Rachel
Published: November 2024

Content moderation practices and technologies need to change over time as requirements and community expectations shift. However, attempts to restructure the existing moderation practices can be difficult, especially for platforms that rely on their communities to moderate, because changes can transform the workflow and workload participants' reward systems. By examining the extensive archival discussions around a prepublication moderation technology on Wikipedia named Flagged Revisions, complemented by seven semi-structured interviews, we identify various challenges in restructuring community-based moderation practices. Thus, we find that while a new system might sound good in theory and perform well in terms of quantitative metrics, it may conflict with existing social norms. Furthermore, our findings underscore how the relationship between platforms and self-governed communities can hinder the ability to assess the performance of any new system and introduce considerable costs related to maintaining, overhauling, or scrapping any piece of infrastructure.
Transitioning Together: Collaborative Work in Adolescent Chronic Illness Management
Authors: Zehrung Rachael , Reddy Madhu , Chen Yunan
Published: November 2024

Adolescents with chronic illnesses need to learn self-management skills in preparation for the transition from pediatric to adult healthcare, which is associated with negative health outcomes for youth. However, few studies have explored how adolescents in a pre-transition stage practice self-management and collaborative management with their parents. Through interviews with 15 adolescents (aged 15-17), we found that adolescents managed mundane self-care tasks and experimented with lifestyle changes to be more independent, which sometimes conflicted with their parents' efforts to ensure their safety. Adolescents and their parents also performed shared activities that provided adolescents with the opportunity to learn and practice self-management skills. Based on our findings, we discuss considerations for technology design to facilitate transition and promote parent-adolescent collaboration in light of these tensions.
Therapy for Therapists: Design Opportunities to Support the Psychological Well-being of Mental Health Workers
Authors: Chandrasekaran Aishwarya , Currano Rebecca , Batool Vafa , Chen Kaiping , Murnane Elizabeth L , Sirkin David , Mauriello Matthew Louis
Published: November 2024

On-demand mental health services-including counseling, crisis hotlines, and peer support programs-are vital to the healthcare system, providing acute and ongoing support through telephone, online chats, and text messaging. Although such services have proven effective at reducing hopelessness, psychological pain, and suicidality, they put the providers of these services at high risk of burnout, secondary traumatic stress, and compassion fatigue. Our interviews with professionals from four mental health organizations revealed that while these workers have a strong motivation to help clients with mental health care needs, they face various challenges themselves, particularly regarding heavy caseloads, difficult crisis clients, and coping with repeated exposure to abuse and harassment. To overcome challenges, participants identify the need to be self-reliant and engage in self-care practices ranging from socializing with coworkers to yoga and meditation. Although organizations spend significant time training workers prior to their involvement with clients, the training typically lacks components on self-compassion and self-care. Designers might see technology as an opportunity to promote such practices; however, while technology is an integral part of their work routine, participants, irrespective of age, had misapprehensions regarding technology use in the mental health care space, including for managing their psychological well-being. We recommend design guidelines for HCI researchers, including developing contextualized just-in-time adaptive interventions to promote self-compassion and educating workers regarding the use of various technologies to manage their well-being.
Wisdom of Crowds: A Human-Machine-Things Cooperative Scheduling Method for Heterogeneous Mobile Crowdsensing
Authors: Liu Yimeng , Yu Zhiwen , Zhang Fengyuan , Cui Helei , Guo Bin
Published: November 2024

Relying on the development of crowdsourcing ideas and mobile crowd sensing (MCS) technology, many tasks that originally required a lot of manpower and material resources have been solved efficiently. However, with the development of urbanization, the traditional MCS systems have gradually been unable to cope with the demands of massive sensing tasks and high spatio-temporal sensing coverage. The challenges are as follows: 1) The scarcity of participants and the limitation of human motion rules lead to the existence of spatio-temporal blind spots in the process of collecting sensing data; 2) The single type of participants limits the sensing ability of the system and the types of data that can be collected, which affects sensing precision and quality. With the emergence of various intelligent sensing terminals in the city, the heterogeneous crowd sensing that integrates human, machine and things participants has become a new generation of sensing mode. In the spatio-temporal related sensing scenarioes, this article designs a new human-machine-things cooperative scheduling (HMT-CS) algorithm framework by comprehensively considering the diverse sensing skills, spatio-temporal trajectories, sensing costs of heterogeneous participants and system total budget constraints. The algorithm can match suitable heterogeneous participants for each task, which greatly improves the sensing quality, sensing fairness and overall utility of heterogeneous MCS systems. We combined multiple public real urban datasets to conduct an in-depth comparative analysis and comprehensive evaluation of the algorithm, and the results show that our method is superior to other baselines in all indicators.
Teen Talk: The Good, the Bad, and the Neutral of Adolescent Social Media Use
Authors: Alluhidan Abdulmalik , Akter Mamtaj , Alsoubai Ashwaq , Park Jinkyung Katie , Wisniewski Pamela
Published: November 2024

The debate on whether social media has a net positive or negative effect on youth is ongoing. Therefore, we conducted a thematic analysis on 2,061 posts made by 1,038 adolescents aged 15-17 on an online peer-support platform to investigate the ways in which these teens discussed popular social media platforms in their posts and to identify differences in their experiences across platforms. Our findings revealed four main emergent themes for the ways in which social media was discussed: 1) Sharing negative experiences or outcomes of social media use (58%, n = 1,095), 2) Attempts to connect with others (45%, n = 922), 3) Highlighting the positive side of social media use (20%, n = 409), and 4) Seeking information (20%, n = 491). Overall, while sharing about negative experiences was more prominent, teens also discussed balanced perspectives of connection-seeking, positive experiences, and information support on social media that should not be discounted. Moreover, we found statistical significance for how these experiences differed across social media platforms. For instance, teens were most likely to seek romantic relationships on Snapchat and self-promote on YouTube. Meanwhile, Instagram was mentioned most frequently for body shaming, and Facebook was the most commonly discussed platform for privacy violations (mostly from parents). The key takeaway from our study is that the benefits and drawbacks of teens' social media usage can co-exist and net effects (positive or negative) can vary across different teens across various contexts. As such, we advocate for mitigating the negative experiences and outcomes of social media use as voiced by teens, to improve, rather than limit or restrict, their overall social media experience. We do this by taking an affordance perspective that aims to promote the digital well-being and online safety of youth "by design."
"Something Fast and Cheap" or "A Core Element of Building Trust"? - AI Auditing Professionals' Perspectives on Trust in AI
Authors: Lassiter Tina B. , Fleischmann Kenneth R.
Published: November 2024

Artificial Intelligence (AI) auditing is a relatively new area of work. Currently, there is a lack of uniform standards and regulation. As a result, the AI auditing ecosystem is very diverse, and AI auditing professionals use a variety of different auditing methods. So far, little is known about how AI auditors approach the concept of trust in AI through AI audits, in particular regarding the trust of users. This paper reports findings from interviews with 19 AI auditing stakeholders to understand how AI auditing professionals seek to create calibrated trust in AI tools and AI audits. Themes identified included the AI auditing ecosystem, participants' experiences with AI auditing, and trust in AI audits and AI. The paper adds to the existing research on trust in AI and trustworthiness in AI by adding perspectives of key stakeholders regarding trust in AI Audits by users as an essential and currently less explored part of the trust in AI research. This paper shows how information asymmetry in respect to AI audits can decrease the value of audits for users and consequently their trust in AI systems. Study participants suggest key elements for rebuilding trust and suggest recommendations for the AI auditing industry, such as monitoring of auditors and effective communication about AI audits.
Sounds Good? Fast and Secure Contact Exchange in Groups
Authors: Putz Florentin , Haesler Steffen , Hollick Matthias
Published: November 2024

Trustworthy digital communication requires the secure exchange of contact information, but current approaches lack usability and scalability for larger groups of users. We evaluate the usability of two secure contact exchange systems: the current state of the art, SafeSlinger, and our newly designed protocol, PairSonic, which extends trust from physical encounters to spontaneous online communication. Our lab study (N=45) demonstrates PairSonic's superior usability, automating the tedious verification tasks from previous approaches via an acoustic out-of-band channel. Although participants significantly preferred our system, minimizing user effort surprisingly decreased the perceived security for some users, who associated security with complexity. We discuss user perceptions of the different protocol components and identify remaining usability barriers for CSCW application scenarios.
The Search for Paxlovid: Medication Acquisition as Anticipation Work After China's Zero-COVID Policy
Authors: Ankenbauer Sam , Yi Huiran
Published: November 2024

In early December 2022, China's zero-COVID policy came to an end. For Chinese citizens, an imaginary of safety predicated on collective action and state care was replaced with a general focus on individualized responsibility for maintaining one's health. This expedited policy reversal had the unintended effect of infecting around 80% of China's population in one month, leading to a period of confusion, anxiety, and infection. One way of obtaining safety in a precarious time was to acquire medical resources. Through an online ethnographic investigation, we trace the acquisition of Paxlovid, an antiviral medication that had its distribution within China complicated by geopolitical tension and supply issues. By attending to the precarity of Chinese citizens at home and abroad, we show how Paxlovid was desired in both the present tense for the presently ill and in the future tense as an anticipatory form of safety. We use the analytical lens of anticipation work to unpack individual, collective, and state anticipatory practices supported by various social media platforms within and across borders. In doing so, we highlight moments of friction between and among various actors to argue that attending to affect, precarity, and practices in moments of conflict is critical to understanding the complexities of computer-supported cooperative work.
Mitigating Gender Stereotypes Toward AI Agents Through an eXplainable AI (XAI) Approach
Authors: Duan Wen , McNeese Nathan , Freeman Guo , Li Lingyuan
Published: November 2024

People often apply gender stereotypes toward computerized agents. Rather than challenging these stereotypes, modern AI technologies controversially use them in creating AI agents that underline stereotypical gendered roles. This approach thus further reinforces the often male-dominated societal gender norms and disenfranchises women and gender non-binary individuals. While this issue has raised concerns in the HCI and CSCW communities, still little is known regarding how to mitigate the negative impacts of embedding gender stereotypes in AI agents. In this paper, we propose an eXplainable AI (XAI) approach to mitigating individuals' gender stereotypes toward AI agents. We conducted an online video vignette experiment with 350 participants randomly assigned to one of the eighteen conditions of a 3 (gender of the agent: woman, man, gender-neutral) x 3 (task gender: feminine, masculine, neutral) x 2 (presence or absence of AI explanation) between-subjects design. Our findings indeed suggest that XAI helped participants avoid applying gender stereotypes toward gendered AI agents, by increasing their understanding of how the agent came to its decision and decreasing their rating of the agent's humanlikeness. We contribute to CSCW research by providing a timely investigation into individuals' gender stereotypes toward the state-of-the-art AI agents and by advancing the empirical understanding of the cognitive processes and mechanisms underlying these gender stereotypes. We also demonstrate how eXplainable AI can effectively suppress the application of social characteristics (i.e., gender stereotypes) toward AI agents by disrupting the said cognitive processes. Insights from this study can inform how future AI technologies should be designed to create a progressive gender reality that will gradually reshape humans' experience and ingrained gender ideologies.
Extended Reality Trans Technologies: Bridging Digital and Physical Worlds to Support Transgender People
Authors: Haimson Oliver L. , DeGuia Aloe , Saber Rana , Brewster Kat
Published: November 2024

Extended reality (XR) technologies are becoming increasingly pervasive, and may have capacity to help marginalized groups such as transgender people. Drawing from interviews with n = 18 creators of trans technology, we examined how XR technologies do and can support trans people. We uncovered a number of creative ways that XR technologies support trans experiences. Trans technology creators are designing augmented reality (AR) and virtual reality (VR) systems that help people explore trans identity, experience new types of bodies, educate about and display trans stories and curated trans content, manipulate the physical world, and innovate gender-affirming surgical techniques. Additionally, we show how considering XR as an analogy for trans identity helps us to think about the fluidity and fluctuation inherent in trans identity in new ways, which in turn enables envisioning technologies that can better support complex and changing identities. Despite XR's potential for supporting trans people, current AR and VR systems face limitations that restrict their large-scale use, but as access to XR systems increase, so will their capacity to improve trans lives.
Articulation Work and Tinkering for Fairness in Machine Learning
Authors: Fahimi Miriam , Russo Mayra , Scott Kristen M. , Vidal Maria-Esther , Berendt Bettina , Kinder-Kurlanda Katharina
Published: November 2024

The field of fair AI aims to counter biased algorithms through computational modelling. However, it faces increasing criticism for perpetuating the use of overly technical and reductionist methods. As a result, novel approaches appear in the field to address more socially-oriented and interdisciplinary (SOI) perspectives on fair AI. In this paper, we take this dynamic as the starting point to study the tension between computer science (CS) and SOI research. By drawing on STS and CSCW theory, we position fair AI research as a matter of 'organizational alignment': what makes research 'doable' is the successful alignment of three levels of work organization (the social world, the laboratory, and the experiment). Based on qualitative interviews with CS researchers, we analyze the tasks, resources, and actors required for doable research in the case of fair AI. We find that CS researchers engage with SOI research to some extent, but organizational conditions, articulation work, and ambiguities of the social world constrain the doability of SOI research for them. Based on our findings, we identify and discuss problems for aligning CS and SOI as fair AI continues to evolve.
CUPID: Improving Battle Fairness and Position Satisfaction in Online MOBA Games with a Re-matchmaking System
Authors: Fan Ge , Zhang Chaoyun , Wang Kai , Li Yingjie , Chen Junyang , Xu Zenglin
Published: November 2024

The multiplayer online battle arena (MOBA) genre has gained significant popularity and economic success, attracting considerable research interest within the Human-Computer Interaction community. Enhancing the gaming experience requires a deep understanding of player behavior, and a crucial aspect of MOBA games is matchmaking, which aims to assemble teams of comparable skill levels. However, existing matchmaking systems often neglect important factors such as players' position preferences and team assignment, resulting in imbalanced matches and reduced player satisfaction. To address these limitations, this paper proposes a novel framework called CUPID, which introduces a novel process called ''re-matchmaking'' to optimize team and position assignments to improve both fairness and player satisfaction. CUPID incorporates a pre-filtering step to ensure a minimum level of matchmaking quality, followed by a pre-match win-rate prediction model that evaluates the fairness of potential assignments. By simultaneously considering players' position satisfaction and game fairness, CUPID aims to provide an enhanced matchmaking experience.

Extensive experiments were conducted on two large-scale, real-world MOBA datasets to validate the effectiveness of CUPID. The results surpass all existing state-of-the-art baselines, with an average relative improvement of 7.18% in terms of win prediction accuracy. Furthermore, CUPID has been successfully deployed in a popular online MOBA game. The deployment resulted in significant improvements in match fairness and player satisfaction, as evidenced by critical Human-Computer Interaction (HCI) metrics covering usability, accessibility, and engagement, observed through A/B testing. To the best of our knowledge, Cupid is the first re-matchmaking system designed specifically for large-scale MOBA games.
The Quest for
"How to do Hybrid right":
Moving Beyond Compensating Asymmetries to Experience-Driven Cooperation
Authors: Busboom Juliane , Boulus-Rodje Nina
Published: November 2024

Hybrid work has become increasingly popular in the aftermath of the Covid-19 pandemic. Despite its popularity, many organisations still strive to find an answer to "how to do hybrid cooperation right''. Enabling collaboration across digital and physical workspaces, participants, and practices is a great challenge, as it inevitably introduces asymmetric relations, incongruences in frames of reference, and misaligned ecosystems and technical infrastructures. The paper investigates approaches used to manage asymmetries in hybrid work settings and their impact on cooperation. Through a multi-sited ethnographic study, we reveal a prevalent reliance on mimicking tools and practices native to fully physical or fully digital settings in the hybrid space. These mimicking practices often arise because the hybrid work setting is viewed through a deficit lens, whereby it is perceived as lacking certain elements (e.g., body, voice, mobility) and access to modalities from especially the physical setting, thereby necessitating the need for compensation work. To provide vocabulary to conceptualize and articulate this type of practice, we introduce the concept
compensation work,
referring to work that is carried out to offset a deficiency or absence that has been identified, aiming to restore and re-establish a familiar state that has vanished. While compensating through mimicking is intuitive, such approaches assume that our known practices and interactions from the physical workspace can indeed be translated to another context, neglecting the fact that changing the medium inevitably impacts the message. Thus, cooperative practices and interactions taking place in the physical workspace do not remain the same when unfolding in the hybrid space. Finally, we suggest that in order to design technologies and practices that support hybrid cooperation we first need to acknowledge the hybrid workspace as a distinct "third space'' next to the fully remote and fully physical workspace. This includes following an experience-driven approach to hybrid cooperation, encouraging the design of innovative interactions that extend beyond merely compensating for asymmetries and leveraging the unique capabilities and affordances of these settings.
Citizen-Led Personalization of User Interfaces: Investigating How People Customize Interfaces for Themselves and Others
Authors: Alves Sérgio , Costa Ricardo , Montague Kyle , Guerreiro Tiago
Published: November 2024

User interface (UI) personalization can improve usability and user experience. However, current systems offer limited opportunities for customization, and third-party solutions often require significant effort and technical skills beyond the reach of most users, impeding the future adoption of interface personalization. In our research, we explore the concept of UI customization for the self and others. We performed a two-week study where nine participants used a custom-designed tool that allows websites' UI customization for oneself and to create and reply to customization assistance requests from others. Results suggest that people enjoy customizing for others more than for themselves. They see requests as challenges to solve and are motivated by the positive feeling of helping others. To customize for themselves, people need help with the creative process. We discuss challenges and opportunities for future research seeking to democratize access to personalized UIs, particularly through community-based approaches.
Analyzing Collaborative Challenges and Needs of UX Practitioners when Designing with AI/ML
Authors: Muralikumar Meena Devii , McDonald David W.
Published: November 2024

UX designers and researchers who work with AI/ML face different kinds of challenges throughout the design process. Though close collaborations with AI/ML developers and data scientists could address some of these challenges, such interdisciplinary collaborations are non-routine and hard to realize. In this work, we investigate barriers for effective collaboration with ML practitioners, how they affect UX practice of AI/ML applications, and what UX practitioners need to overcome these challenges. We conducted a qualitative study with 14 UX practitioners who are working on AI/ML products as designers or researchers. Our findings show that UX practitioners face challenges in communication, understanding the model and model development processes, establishing ways to collaborate, and reconciling model-centric metrics of evaluation with user-centric outcomes. They described various needs in terms of more visibility into model development processes, access to comprehensible and contextual model information, and hypothetical tools that can potentially support collaboration with ML practitioners and enhance UX design processes. We discuss implications of this research for designing collaborative tools and empowering UX practitioners.
Providing Context to the "Unknown": Patient and Provider Reflections on Connecting Personal Tracking, Patient-Reported Insights, and EHR Data within a Post-COVID Clinic
Authors: Chopra Shaan , Carroll Jeanne , Pater Jessica
Published: November 2024

COVID Long Haul (CLH) is an emerging chronic illness for which the healthcare system continues to seek a common understanding of symptoms, diagnosis, and treatment. CLH experiences can differ drastically, necessitating personalized care plans. Because patients interact with different clinicians during their CLH journey, it becomes important to ensure interoperability and understand clinical relevance of different data that can support clinicians in making appropriate recommendations. We conducted qualitative research where we interviewed 13 patients, conducted a focus group with 8 clinicians, and analyzed care plan follow-up records. We report patient and clinician expectations from and interactions with clinic data. We uncover logistical challenges, personal contexts, and health barriers impacting patient compliance. As researchers embedded in the clinical system, we identify the potential of using multiple patient data streams to support personalized treatment and clinical decisions. We discuss technology design opportunities and provide actionable recommendations for improving clinical workflows and cross-provider collaboration.
Studying Up Public Sector AI: How Networks of Power Relations Shape Agency Decisions Around AI Design and Use
Authors: Kawakami Anna , Coston Amanda , Heidari Hoda , Holstein Kenneth , Zhu Haiyi
Published: November 2024

As public sector agencies rapidly introduce new AI tools in high-stakes domains like social services, it becomes critical to understand how decisions to adopt these tools are made in practice. We borrow from the anthropological practice to "study up" those in positions of power, and reorient our study of public sector AI around those who have the power and responsibility to make decisions about the role that AI tools will play in their agency. Through semi-structured interviews and design activities with 16 agency decision-makers, we examine how decisions about AI design and adoption are influenced by their interactions with and assumptions about other actors within these agencies (e.g., frontline workers and agency leaders), as well as those above (legal systems and contracted companies), and below (impacted communities). By centering these networks of power relations, our findings shed light on how infrastructural, legal, and social factors create barriers and disincentives to the involvement of a broader range of stakeholders in decisions about AI design and adoption. Agency decision-makers desired more practical support for stakeholder involvement around public sector AI to help overcome the knowledge and power differentials they perceived between them and other stakeholders (e.g., frontline workers and impacted community members). Building on these findings, we discuss implications for future research and policy around actualizing participatory AI approaches in public sector contexts.
"Math is a pain!": Understanding challenges and needs of the Machine Learning community on Stack Overflow
Authors: Fang Zihan , Huang Yu
Published: November 2024

Stack Overflow (SO) is a widely recognized online question-and-answer platform for programming, which has also fostered a substantial community dedicated to machine learning (ML), providing a space for both novices and experts to exchange ideas and find solutions to ML-related problems. However, as a relative minority of this online programming platform, research has demonstrated lower engagement in the ML community, but it remains largely unexplored to understand what hinders the engagement and contribution from ML users' perspectives. This paper presents an empirical study based on 22 hours of semi-structured interviews and 131 survey responses with users on SO and reveals the key factors that may lead to the lower response rate and extended waiting time for ML questions on SO, which includes the unique quality requirement for posting ML questions, the discrepancy between time invested and benefits gained, the dispersed nature of the ML community across various platforms and the desired improvement for SO. Moreover, the qualitative study reveals a declining friendliness in SO's culture over time; the subsequent quantitative study corroborates that newcomers frequently encounter stress when posting and answering ML questions, even though this stress diminishes with increased experience. Additionally, we also explored the potential influence of generative AI tools (e.g., ChatGPT) on online question-and-answer platforms, specifically focusing on ML Q&A. We hope the results of this study can pave the way for enhancing the experience of ML users on online platforms, ultimately facilitating improved knowledge exchange and collaboration within the ML domain.
'Better than Google': Information Activism for LGBTQ+ Young Adults in a Rural Community
Authors: Jonas Anne , Vargas Stefani , Hardy Jean
Published: November 2024

For young lesbian, gay, bisexual, transgender, and queer people, and those belonging to other marginalized sexual and gender identities (LGBTQ+), accessing information to develop one's identity and community is essential. Yet information and resource access is a particularly complex process for LGBTQ+ youth living in rural areas and small towns without dense, visible LGBTQ+ networks. While efforts to remove LGBTQ+ resources and demonize LGBTQ+ people intensify, this study asks a group of young LGBTQ+ adults living in and around a college town in the rural Midwestern United States to document their experiences with navigating information landscapes in low-resource settings. Despite the remarkable everyday creativity required to craft their information practices, participants' current efforts produce several vulnerabilities that make local queer information networks precarious and often inaccessible. We lay out several patterns in this data, including experiences of both connectedness and disconnectedness, the role of organizations in information sharing, and the existence of both active and passive information search. Building on prior research that found increasing demand for multi-modal access to relevant information that is digitally enabled, but deeply embedded in localized social geography and LGBTQ+ community, we use this data and complementary interviews to identify problem areas and potential interventions to strengthen rural LGBTQ+ young people's information access. In response to the informational needs and desires of LGBTQ+ youth in this community, we propose a series of imaginative designs. These speculative artifacts are embedded in local communities to strengthen grassroots webs of knowledge. We focus on contributing solutions that fall under three problem areas in participants' information ecosystem: increasing information stumbling, building expert capacity and support, and bridging the disconnected to the connected. We propose speculative conceptual designs that address core challenges in LGBTQ+ information networks, working in the tradition of ''information activism'' to create and maintain access to otherwise precarious and ephemeral resources and spark social change.
Casual Competition by Design: A Study of the All Random All Mid (ARAM) Mode in League of Legends
Authors: Zhang Zinan , Kou Yubo
Published: November 2024

Competitive gaming, a long-standing study context for CSCW, has recently faced criticism due to its design emphasis on competition and achievement, which is associated with adverse phenomena such as player toxicity and anxiety. Recognizing this limit, game designers have proactively made design attempts to ameliorate these unintended consequences of competitive gaming. A notable example is the All Random All Mid (ARAM) mode in League of Legends (LoL), designed to introduce casualness into competitive gaming. To understand how players experience both casualness and competitiveness, a seemingly contradictory pair, we conducted an interview study with ARAM players, finding that ARAM supports 'casual competition' through decentering competition, diversifying interpersonal dynamics, and filling gaps in player needs. We further discuss how game design and player agency co-constitute casual competition, reflect on key aspects of competitive gaming design such as diversity and fairness, and provide implications for competitive gaming design, which may help combat toxicity.
Investigating Nudges toward Related Sellers on E-commerce Marketplaces: A Case Study on Amazon
Authors: Dash Abhisek , Chakraborty Abhijnan , Ghosh Saptarshi , Mukherjee Animesh , Gummadi Krishna P.
Published: November 2024

E-commerce marketplaces provide business opportunities to millions of sellers worldwide. Some of these sellers have special relationships with the marketplace by virtue of using their subsidiary services (e.g., fulfillment and/or shipping services provided by the marketplace) -- we refer to such sellers collectively as
Related Sellers.
When multiple sellers offer to sell the same product, the marketplace helps a customer in selecting an offer (by a seller) through (a) a default offer selection algorithm, (b) showing features about each of the offers and the corresponding sellers (price, seller performance metrics, seller's number of ratings etc.), and (c) finally evaluating the sellers along these features. In this paper, we perform an end-to-end investigation into how the above apparatus can nudge customers toward the
Related Sellers
on Amazon's four different marketplaces in India, USA, Germany and France. We find that given explicit choices, customers' preferred offers and algorithmically selected offers can be significantly different. We highlight that Amazon is adopting different performance metric evaluation policies for different sellers, potentially benefiting
Related Sellers.
For instance, such policies result in notable discrepancy between the actual performance metric and the presented performance metric of
Related Sellers.
We further observe that among the seller-centric features visible to customers, sellers' number of ratings influences their decisions the most, yet it may not reflect the true quality of service by the seller, rather reflecting the scale at which the seller operates, thereby implicitly steering customers toward larger
Related Sellers.
Moreover, when customers are shown the rectified metrics for the different sellers, their preference toward
Related Sellers
is almost halved. We believe our findings will inform and encourage further deliberation toward more effective governance of such design choices and policies adopted by e-commerce marketplaces.
Spirits in the Material World: Older Adults' Personal Curation of Memory Artifacts
Authors: Ankenbauer Sam Addison , Brewer Robin N.
Published: November 2024

Memory artifacts are personal and collective belongings that elicit deliberate or involuntary memories. They are significant as objects of continuity, vessels for identity, and links to past relationships and history-for individuals, families, and communities. Drawing from in-depth interviews and cultural probe sessions with 16 individuals over 65, we consider how older adults curate and interact with their personal artifacts that embody and inform memory. Participants' hands-on experiences with memory artifacts uncover a heterogeneous set of personal curation practices and identify tensions that result from the competing goals of creating a legible narrative or legacy for themselves, their family, and their communities. The transition from physical to digital memory artifacts often perpetuates tension but can also create moments of reflection. These findings contribute a set of design considerations for supporting curation practices. This paper joins and expands upon CSCW scholarship regarding the importance of memory artifacts and the ongoing challenges of retaining individual memory and history over time, which, if managed effectively, can benefit and sustain family and community history at large.