bluuebunny/crossref_metadata_2025 huggingface dataset:
Beyond Initial Removal: Lasting Impacts of Discriminatory Content Moderation to Marginalized Creators on Instagram
Authors: Register Yim , Grasso Izzi , Weingarten Lauren N. , Fury Lilith , Chinea Constanza Eliana , Malloy Tuck J. , Spiro Emma S.
Published: April 2024

Recent work has demonstrated how content moderation practices on social media may unfairly affect marginalized individuals, for example by censoring women's bodies and misidentifying reclaimed terms as hate speech. This study documents and explores the direct experiences of marginalized creators who have been impacted by discriminatory content moderation on Instagram. Collaborating with our participants for over a year, we contribute five co-constructed narratives of discriminatory content moderation from advocates in trauma-informed care, LGBTQ+ sex education, anti-racism education, and beauty and body politics. In sharing these detailed personal accounts, not only do we shed light on their experiences with being blocked, banned, or deleted unfairly, but we delve deeper into the lasting impacts of these experiences to their livelihoods and mental health. Reflecting on their stories, we observe that content moderation on social media is deeply entangled with the situated experiences of offline discrimination. As such, we document how each participant experiences moderation through the lens of their often intersectional identities. Using participatory research methods, we collectively strategize ways to learn from these individual accounts and resist discriminatory content moderation, as well as imagine possibilities for repair and accountability.
Beyond Just Money Transactions: How Digital P2P Payments (Re)shape Existing Offline Interpersonal Relationships
Authors: Li Lingyuan , Freeman Guo , Knijnenburg Bart
Published: April 2024

Money is a sensitive and complex component of everyday life, which can significantly affect people's relationships with each other. Recently, emerging digital peer-to-peer (P2P) payment applications continue to complicate how people deal with money with individuals they know in comparison to traditional payment methods such as physical money (i.e., cash). Through an online survey study (N=218), in this paper, we investigate significant differences between using digital P2P payments and traditional payment methods (i.e., physical money) in terms of perceptions of transactions by using a given payment method, perceptions of people's existing interpersonal relationships with their known contacts after such transactions, and future use intention of a given payment method. We thus offer empirical evidence to highlight multidimensional influences of using digital P2P payments on established offline interpersonal relationships compared to using traditional payment methods (i.e., physical money). We also provide crucial new insights to unpack digital P2P payments as a novel technology-mediated interpersonal dynamic in people's everyday lives, which goes beyond merely as a business payment model or a financial service. We additionally provide important directions for designing more supportive and socially satisfactory digital P2P payment platforms in the future by taking the interplay of financial exchanges and interpersonal relationships into consideration.
A Token Gesture: Non-Transferable NFTs, Digital Possessions and Ownership Design
Authors: Elsden Chris , Morgan Evan , Tallyn Ella , Black Suzanne R. , Disley Martin , Schafer Burkhard , Murray-Rust Dave , Speed Chris
Published: April 2024

This paper presents the design, deployment and qualitative study of a large-scale, public, generative art exhibition, through which passers-by could create artworks, and mint a non-fungible-token (NFT). Following the month-long exhibition, during which 229 anonymous participants produced artworks, 69 non-transferable NFTs were minted, we surveyed (33) and interviewed (14) expert and novice participants about their experiences. We explored contemporary challenges of owning digital things, and the extent to which NFTs, and 'Web3' technologies offer meaningful forms of ownership. Our findings describe how the inability to trade this NFT, and its unique circumstances of acquisition, made it meaningful in ways that extended beyond its immediate (limited) utility and offered participants something through which to construct identity. Reflecting on the aspirations, contradictions, and misconceptions of forms of ownership enabled by NFTs, we conclude with proposals for renewed attention in HCI to the nature of digital possessions, and the potential for 'ownership design'.
Burnout in Cybersecurity Incident Responders: Exploring the Factors that Light the Fire
Authors: Nepal Subigya , Hernandez Javier , Lewis Robert , Chaudhry Ahad , Houck Brian , Knudsen Eric , Rojas Raul , Tankus Ben , Prafullchandra Hemma , Czerwinski Mary
Published: April 2024

As concerns about employee burnout and skilled staff shortages in cybersecurity grow, our study aims to better understand the contributing factors to burnout in this field. Utilizing a mixed-methods approach, we analyze self-reported job and personal characteristics, along with digital activity data from 35 incident responders, identifying several factors such as high workload, time pressure, and lack of support from management. Our findings reveal that over half of the participants experience burnout (N=19), which is linked to increased workload, limited control, poor teamwork, and inadequate recognition. Burned-out responders often work more than 40 hours per week, have poor sleep quality, and engage in more email activities, meetings, and after-hour collaborations. Through our research, we also identify coping strategies individuals use to mitigate these stressors. Based on our findings, we provide practical recommendations to help organizations better support their cybersecurity incident response teams. While our study acknowledges limitations and suggests future research directions, it contributes significantly to understanding the challenges faced by cybersecurity incident responders. Our insights offer a comprehensive understanding of burnout factors in this domain and have broader implications for other high-stress work environments consistent with the interdisciplinary nature of CSCW.
Characterizing Usability Issue Discussions in Open Source Software Projects
Authors: Sanei Arghavan , Cheng Jinghui
Published: April 2024

Usability is a crucial factor but one of the most neglected concerns in open source software (OSS). While far from an ideal approach, a common practice that OSS communities adopt to collaboratively address usability is through discussions on issue tracking systems (ITSs). However, there is little knowledge about the extent to which OSS community members engage in usability issue discussions, the aspects of usability they frequently target, and the characteristics of their collaboration around usability issue discussions. This knowledge is important for providing practical recommendations and research directions to better support OSS communities in addressing this important topic and improve OSS usability in general. To help achieve this goal, we performed an extensive empirical study on issues discussed in five popular OSS applications: three data science notebook projects (Jupyter Lab, Google Colab, and CoCalc) and two code editor projects (VSCode and Atom). Our results indicated that while usability issues are extensively discussed in the OSS projects, their scope tended to be limited to efficiency and aesthetics. Additionally, these issues are more frequently posted by experienced community members and display distinguishable characteristics, such as involving more visual communication and more participants. Our results provide important implications that can inform the OSS practitioners to better engage the community in usability issue discussion and shed light on future research efforts toward collaboration techniques and tools for discussing niche topics in diverse communities, such as the usability issues in the OSS context.
Construction and Analysis of Collaborative Educational Networks based on Student Concept Maps
Authors: Freedman Hayden , Young Neil , Schaefer David , Song Qingyu , van der Hoek André , Tomlinson Bill
Published: April 2024

Network Analysis has traditionally been applied to analyzing interactions among learners in online learning platforms such as discussion boards. However, there are opportunities to bring Network Analysis to bear on networks representing learners' mental models of course material, rather than learner interactions. This paper describes the construction and analysis of collaborative educational networks based on concept maps created by undergraduates. Concept mapping activities were deployed throughout two separate quarters of a large General Education (GE) course about sustainability and technology at a large university on the West Coast of the United States. A variety of Network Analysis metrics are evaluated on their ability to predict an individual learner's understanding based on that learner's contributions to a network representing the collective understanding of all learners in the course. Several of the metrics significantly correlated with learner performance, especially those that compare an individual learner's conformity to the larger group's consensus. The novel network metrics based on collective networks of learner concept maps are shown to produce stronger and more reproducible correlations with learner performance than metrics traditionally used in the literature to evaluate concept maps. This paper thus demonstrates that Network Analysis in conjunction with collective networks of concept maps can provide insights into learners' conceptual understanding of course material.
Data Stewardship in Clinical Computer Security: Balancing Benefit and Burden in Participatory Systems
Authors: Tseng Emily , Bellini Rosanna , Lee Yeuk-Yu , Ramjit Alana , Ristenpart Thomas , Dell Nicola
Published: April 2024

The mass collection and reuse of social data requires a reimagining of privacy and consent, with particular attention to the (in)equitable distribution of benefits and burdens between researchers and subjects. Instrumenting frontline clinical services to collect and steward data might mitigate the exploitation inherent to data collection---with attention to how subjects can meaningfully participate in stewardship. We explore participatory data stewardship in the context of clinical computer security for survivors of intimate partner violence (IPV). Via semi-structured interviews with IPV support workers, we explore how data are produced within the IPV care ecosystem at the Clinic to End Tech Abuse (CETA). We then conduct design provocations with clients of IPV services and their support workers, exploring possibilities for participatory data mechanisms like open records and dynamic consent. We find participation in data stewardship may benefit clients through improved agency, self-reflection, and control of self-narrative, and that incurred burdens may be alleviated by enlisting trusted stewards. We close with future work for CSCW interrogating how knowledge of digital-safety harms can and should be produced from clinical encounters, towards more equitable ways of knowing.
Designing for Appropriate Reliance: The Roles of AI Uncertainty Presentation, Initial User Decision, and User Demographics in AI-Assisted Decision-Making
Authors: Cao Shiye , Liu Anqi , Huang Chien-Ming
Published: April 2024

Appropriate reliance is critical to achieving synergistic human-AI collaboration. For instance, when users over-rely on AI assistance, their human-AI team performance is bounded by the model's capability. This work studies how the presentation of model uncertainty may steer users' decision-making toward fostering appropriate reliance. Our results demonstrate that showing the calibrated model uncertainty alone is inadequate. Rather, calibrating model uncertainty and presenting it in a frequency format allow users to adjust their reliance accordingly and help reduce the effect of confirmation bias on their decisions. Furthermore, the critical nature of our skin cancer screening task skews participants' judgment, causing their reliance to vary depending on their initial decision. Additionally, step-wise multiple regression analyses revealed how user demographics such as age and familiarity with probability and statistics influence human-AI collaborative decision-making. We discuss the potential for model uncertainty presentation, initial user decision, and user demographics to be incorporated in designing personalized AI aids for appropriate reliance.
Designing Shared Information Displays for Agents of Varying Strategic Sophistication
Authors: Zhang Dongping , Hartline Jason , Hullman Jessica
Published: April 2024

Data-driven predictions are often perceived as inaccurate in hindsight due to behavioral responses. In this study, we explore the role of interface design choices in shaping individuals' decision-making processes in response to predictions presented on a shared information display in a strategic setting. We introduce a novel staged experimental design to investigate the effects of design features, such as visualizations of prediction uncertainty and error, within a repeated congestion game. In this game, participants assume the role of taxi drivers and use a shared information display to decide where to search for their next ride. Our experimental design endows agents with varying level-k depths of thinking, allowing some agents to possess greater sophistication in anticipating the decisions of others using the same information display. Through several extensive experiments, we identify trade-offs between displays that optimize individual decisions and those that best serve the collective social welfare of the system. We find that the influence of display characteristics varies based on an agent's strategic sophistication. We observe that design choices promoting individual-level decision-making can lead to suboptimal system outcomes, as manifested by a lower realization of potential social welfare. However, this decline in social welfare is offset by a reduction in the distribution shift, narrowing the gap between predicted and realized system outcomes, which potentially enhances the perceived reliability and trustworthiness of the information display post hoc. Our findings pave the way for new research questions concerning the design of effective prediction interfaces in strategic environments.
Diving into a Heritage Metaverse: Learning from End User-driven Experiences and Perspectives to Enhance Virtual Heritage Interpretation
Authors: Shim Hyeseung , Kim Eun Seo , Oh Kyung Taek , Shi Chung-Kon , Ahn Jaehong
Published: April 2024

This study aims to explore the potential of the metaverse to determine if it can be an effective medium for realizing an end-user-driven heritage interpretation platform. To this end, we conducted an in-depth user-study using a combination of qualitative experimental methods (cultural probes and a focus group interview) to obtain data on the thought processes, experiences, and responses of end-users within the metaverse called the Peaceful Hill, a heritage metaverse created by the National Museum of Korea and hosted on Zepeto, a metaverse platform. The twelve participants in their 20s were recruited for a five-day experiment. Through an inductive thematic study, four themes related to the end-user experience in the process of virtual heritage interpretation were identified, as follows: structural components as virtual wider settings, interactive components as life-logging experience, mediating components as an actor, and heritage interpretation components. Then, based on the visual network analysis, we further explored the multiple connections and reciprocal influences of the above themes to uncover differences and similarities in user-perceived experiences in the metaverse compared to existing virtual environments. In addition, we offered insights and design considerations for establishing a participatory heritage interpretation experience in the metaverse.
Emotion AI Use in U.S. Mental Healthcare: Potentially Unjust and Techno-Solutionist
Authors: Roemmich Kat , Corvite Shanley , Pyle Cassidy , Karizat Nadia , Andalibi Nazanin
Published: April 2024

Emotion AI, or AI that claims to infer emotional states from various data sources, is increasingly deployed in myriad contexts, including mental healthcare. While emotion AI is celebrated for its potential to improve care and diagnosis, we know little about the perceptions of data subjects most directly impacted by its integration into mental healthcare. In this paper, we qualitatively analyzed U.S. adults' open-ended survey responses (n = 395) to examine their perceptions of emotion AI use in mental healthcare and its potential impacts on them as data subjects. We identify various perceived impacts of emotion AI use in mental healthcare concerning 1) mental healthcare provisions; 2) data subjects' voices; 3) monitoring data subjects for potential harm; and 4) involved parties' understandings and uses of mental health inferences. Participants' remarks highlight ways emotion AI could address existing challenges data subjects may face by 1) improving mental healthcare assessments, diagnoses, and treatments; 2) facilitating data subjects' mental health information disclosures; 3) identifying potential data subject self-harm or harm posed to others; and 4) increasing involved parties' understanding of mental health. However, participants also described their perceptions of potential negative impacts of emotion AI use on data subjects such as 1) increasing inaccurate and biased assessments, diagnoses, and treatments; 2) reducing or removing data subjects' voices and interactions with providers in mental healthcare processes; 3) inaccurately identifying potential data subject self-harm or harm posed to others with negative implications for wellbeing; and 4) involved parties misusing emotion AI inferences with consequences to (quality) mental healthcare access and data subjects' privacy. We discuss how our findings suggest that emotion AI use in mental healthcare is an insufficient techno-solution that may exacerbate various mental healthcare challenges with implications for potential distributive, procedural, and interactional injustices and potentially disparate impacts on marginalized groups.
Engage Wider Audience or Facilitate Quality Answers? a Mixed-methods Analysis of Questioning Strategies for Research Sensemaking on a Community Q&A Site
Authors: He Changyang , Deng Yue , He Lu , Guo Qingyu , Zhang Yu , Lu Zhicong , Li Bo
Published: April 2024

Discussing research-sensemaking questions on Community Question and Answering (CQA) platforms has been an increasingly common practice for the public to participate in science communication. Nonetheless, how users strategically craft research-sensemaking questions to engage public participation and facilitate knowledge construction is a significant yet less understood problem. To fill this gap, we collected 837 science-related questions and 157,684 answers from Zhihu, and conducted a mixed-methods study to explore user-developed strategies in proposing research-sensemaking questions, and their potential effects on public engagement and knowledge construction. Through open coding, we captured a comprehensive taxonomy of question-crafting strategies, such as eyecatching narratives with counter-intuitive claims and rigorous descriptions with data use. Regression analysis indicated that these strategies correlated with user engagement and answer construction in different ways (e.g., emotional questions attracted more views and answers), yet there existed a general divergence between wide participation and quality knowledge establishment, when most questioning strategies could not ensure both. Based on log analysis, we further found that collaborative editing afforded unique values in refining research-sensemaking questions regarding accuracy, rigor, comprehensiveness and attractiveness. We propose design implications to facilitate accessible, accurate and engaging science communication on CQA platforms.
FamilyScope: Visualizing Affective Aspects of Family Social Interactions using Passive Sensor Data
Authors: Lee Hyunsoo , Jung Yugyeong , Shin Youwon , Park Hyesoo , Choi Woohyeok , Lee Uichin
Published: April 2024

This work presents FamilyScope, a sensor-based family informatics system that enables reflection upon family data collected from family activity scenarios (e.g., game playing and movie watching) that include affective aspects of a family's social interactions. We conducted a user study with ten families (n=30) in a smart home testbed to observe how our system supports data reflection of the affective and behavioral states among family members. Our findings showed that FamilyScope facilitated family data reflection on affective and behavioral aspects of family interactions. Overall, families reported that the system well reflected family members' general tendencies in terms of affective and behavioral responses and even helped them gain new insights about each other. Based on the findings, we provide practical design approaches for collective reflection in family informatics systems.
From Awareness to Action: Exploring End-User Empowerment Interventions for Dark Patterns in UX
Authors: Lu Yuwen , Zhang Chao , Yang Yuewen , Yao Yaxing , Li Toby Jia-Jun
Published: April 2024

The study of UX dark patterns, i.e., UI designs that seek to manipulate user behaviors, often for the benefit of online services, has drawn significant attention in the CHI and CSCW communities in recent years. To complement previous studies in addressing dark patterns from (1) the designer's perspective on education and advocacy for ethical designs; and (2) the policymaker's perspective on new regulations, we propose an end-user-empowerment intervention approach that helps users (1) raise the awareness of dark patterns and understand their underlying design intents; (2) take actions to counter the effects of dark patterns using a web augmentation approach. Through a two-phase co-design study, including 5 co-design workshops (N=12) and a 2-week technology probe study (N=15), we reported findings on the understanding of users' needs, preferences, and challenges in handling dark patterns and investigated the feedback and reactions to users' awareness of and action on dark patterns being empowered in a realistic in-situ setting.
Gender Biases in Error Mitigation by Voice Assistants
Authors: Mahmood Amama , Huang Chien-Ming
Published: April 2024

Commercial voice assistants are largely feminized and associated with stereotypically feminine traits such as warmth and submissiveness. As these assistants continue to be adopted for everyday uses, it is imperative to understand how the portrayed gender shapes the voice assistant's ability to mitigate errors, which are still common in voice interactions. We report a study (N=40) that examined the effects of voice gender (feminine, ambiguous, masculine), error mitigation strategies (apology, compensation) and participant's gender on people's interaction behavior and perceptions of the assistant. Our results show that AI assistants that apologized appeared warmer than those offered compensation. Moreover, male participants preferred apologetic feminine assistants over apologetic masculine ones. Furthermore, male participants interrupted AI assistants regardless of perceived gender more frequently than female participants when errors occurred. Our results suggest that the perceived gender of a voice assistant biases user behavior, especially for male users, and that an ambiguous voice has the potential to reduce biases associated with gender-specific traits.
Healthcare Voice AI Assistants: Factors Influencing Trust and Intention to Use
Authors: Zhan Xiao , Abdi Noura , Seymour William , Such Jose
Published: April 2024

AI assistants such as Alexa, Google Assistant, and Siri, are making their way into the healthcare sector, offering a convenient way for users to access different healthcare services. Trust is a vital factor in the uptake of healthcare services, but the factors affecting trust in voice assistants used for healthcare are under-explored and this specialist domain introduces additional requirements. This study explores the effects of different functional, personal, and risk factors on trust in and adoption of healthcare voice AI assistants (HVAs), generating a partial least squares structural model from a survey of 300 voice assistant users. Our results indicate that trust in HVAs can be significantly explained by functional factors (usefulness, content credibility, quality of service relative to a healthcare professional), together with security, and privacy risks and personal stance in technology. We also discuss differences in terms of trust between HVAs and general-purpose voice assistants as well as implications that are unique to HVAs.
"I hate you. I love you. I'm sorry. I miss you." Understanding Online Grief Expression Through Suicide Bereavement Letter-Writing Practices
Authors: Doyle Dylan Thomas , Brahm Charlie Blue R. , Brubaker Jed R.
Published: April 2024

When bereaved individuals seek online support in response to the suicide of a loved one, their expressions of grief take many forms. Although the intense grief expressions individuals bereaved by suicide commonly share in private therapeutic settings can be helpful in healing from traumatic loss, these same expressions may potentially cause harm to others when shared in a public online support community. In this study, we present a qualitative analysis of letters posted on the r/SuicideBereavement subreddit, and comments replying to those posts, to explore what diverse expressions of grief additionally demand of platform design. We find that letter posts contain potentially harmful grief expressions that, in this community, generate mutual support among community members. Informed by our findings, this study considers the design challenges for online platforms as they simultaneously support users receiving support and healing through sharing certain grief expressions, while also supporting users who will be harmed by exposure to those grief expressions. Taking inspiration from offline therapy modalities, we consider the design implications of creating specialized online grief support spaces for diverse grief expressions.
Investigating VTubing as a Reconstruction of Streamer Self-Presentation: Identity, Performance, and Gender
Authors: Wan Qian , Lu Zhicong
Published: April 2024

VTubers, or Virtual YouTubers, are live streamers who create streaming content using animated 2D or 3D virtual avatars. In recent years, there has been a significant increase in the number of VTuber creators and viewers across the globe. This practice has drawn research attention into topics such as viewers' engagement behaviors and perceptions, however, as animated avatars offer more identity and performance flexibility than traditional live streaming where one uses their own body, little research has focused on how this flexibility influences how creators present themselves. This research thus seeks to fill this gap by presenting results from a qualitative study of 16 Chinese-speaking VTubers' streaming practices. The data revealed that the virtual avatars that were used while live streaming afforded creators opportunities to present themselves using inflated presentations and resulted in inclusive interactions with viewers. The results also unveiled the inflated, and often sexualized, gender expressions of VTubers while they were situated in misogynistic environments. The socio-technical facets of VTubing were found to potentially reduce sexual harassment and sexism, whilst also raising self-objectification concerns.
"It Felt Like Having a Second Mind": Investigating Human-AI Co-creativity in Prewriting with Large Language Models
Authors: Wan Qian , Hu Siying , Zhang Yu , Wang Piaohong , Wen Bo , Lu Zhicong
Published: April 2024

Prewriting is the process of discovering and developing ideas before writing a first draft, which requires divergent thinking and often implies unstructured strategies such as diagramming, outlining, free-writing, etc. Although large language models (LLMs) have been demonstrated to be useful for a variety of tasks including creative writing, little is known about how users would collaborate with LLMs to support prewriting. The preferred collaborative role and initiative of LLMs during such a creative process is also unclear. To investigate human-LLM collaboration patterns and dynamics during prewriting, we conducted a three-session qualitative study with 15 participants in two creative tasks: story writing and slogan writing. The findings indicated that during collaborative prewriting, there appears to be a three-stage iterative Human-AI Co-creativity process that includes Ideation, Illumination, and Implementation stages. This collaborative process champions the human in a dominant role, in addition to mixed and shifting levels of initiative that exist between humans and LLMs. This research also reports on collaboration breakdowns that occur during this process, user perceptions of using existing LLMs during Human-AI Co-creativity, and discusses design implications to support this co-creativity process.
Making Data Work Count
Authors: Chandhiramowuli Srravya , Taylor Alex S. , Heitlinger Sara , Wang Ding
Published: April 2024

In this paper, we examine the work of data annotation. Specifically, we focus on the role of counting or quantification in organising annotation work. Based on an ethnographic study of data annotation in two outsourcing centres in India, we observe that counting practices and its associated logics are an integral part of day-to-day annotation activities. In particular, we call attention to the presumption of total countability observed in annotation - the notion that everything, from tasks, datasets and deliverables, to workers, work time, quality and performance, can be managed by applying the logics of counting. To examine this, we draw on sociological and socio-technical scholarship on quantification and develop the lens of a 'regime of counting' that makes explicit the specific counts, practices, actors and structures that underpin the pervasive counting in annotation. We find that within the AI supply chain and data work, counting regimes aid the assertion of authority by the AI clients (also called requesters) over annotation processes, constituting them as reductive, standardised, and homogenous. We illustrate how this has implications for i) how annotation work and workers get valued, ii) the role human discretion plays in annotation, and iii) broader efforts to introduce accountable and more just practices in AI. Through these implications, we illustrate the limits of operating within the logic of total countability. Instead, we argue for a view of counting as partial - located in distinct geographies, shaped by specific interests and accountable in only limited ways. This, we propose, sets the stage for a fundamentally different orientation to counting and what counts in data annotation.
Making The Future School: An Analysis of Teens' Collaborative Digital Fabrication Project
Authors: Hartikainen Heidi , Ventä-Olkkonen Leena , Cortés Orduña Marta , Sanchez Milara Ivan , Käsmä Marjukka , Kuure Leena
Published: April 2024

Maker culture emphasizes informal, networked, self- and peer-led learning, motivated by fun and self-fulfillment, and learning through mistakes. However, studies often describe activities involving a lot of guidance. As guidance may lead to both the process and outcomes influenced by those guiding the activity, it is also important to examine how projects evolve without the supporters' push. We take a nexus analytical approach to explore 1) how teens' background, interactions with others, and available materials and spaces are at play when they shape their ideas and outputs in a collaborative maker project with minimal support, and 2) how these factors can be considered to better support learning in such projects. The work carried out by five teens in a local FabLab was based on peer collaboration within changing apprentice mentor pairs. When they ideated and prototyped for a future school, researchers provided minimal support, mainly online. Without their push, teens quickly modified tasks which both guided and narrowed down their thinking. Their experiences from school environment and the FabLab space served as an inspiration as they designed solutions to overcome current problems in the current school setting or digital fabrication process. When making, they gravitated towards processes they had previous experience of or which they enjoyed, perhaps partly affected by the competing discourses of maker culture and expertise we observed to be present in the FabLab space. While building on existing skillsets, this may leave the potential of novel trajectories and expanding one's skills and competences unexplored. Teens' self-esteem improved as they turned from apprentices to mentors for their peers. This allowed them to process their learning from a different perspective. In general, our results paint a detailed picture of the roles of different participants in a maker project and provide an example of how familiar discourses of education persist in teens' designs. Looking at the project through the lens of Nexus analysis contributes to an increased understanding of the space for action and participants' histories, and interactions on the process, valuable for researchers studying DF and making, and practitioners working in makerspaces and FabLabs with different user groups. On a more practical level, our work contributes to a deeper understanding on how to include teens into non-guided and community-driven maker practices. We provide practical implications on how to support learning in collaborative making projects in informal learning situations, and regarding the role of the different actors and the environment in such situations. We also identify avenues for future research in this area.
Meeting Effectiveness and Inclusiveness: Large-scale Measurement, Identification of Key Features, and Prediction in Real-world Remote Meetings
Authors: Hosseinkashi Yasaman , Tankelevitch Lev , Pool Jamie , Cutler Ross , Madan Chinmaya
Published: April 2024

Workplace meetings are vital to organizational collaboration, yet relatively little progress has been made toward measuring meeting effectiveness and inclusiveness at scale. The recent rise in remote and hybrid meetings represents an opportunity to do so via computer-mediated communication (CMC) systems. Here, we share the results of an effective and inclusive meetings survey embedded within a CMC system in a diverse set of companies and organizations. We correlate the survey results with objective metrics available from the CMC system to identify the generalizable attributes that characterize perceived effectiveness and inclusiveness in meetings. Additionally, we explore a predictive model of meeting effectiveness and inclusiveness based solely on objective meeting attributes. Lastly, we show challenges and discuss solutions around the subjective measurement of meeting experiences. To our knowledge, this is the largest data-driven study conducted after the pandemic peak to measure, understand, and predict effectiveness and inclusiveness in real-world meetings at an organizational scale.
Missed Opportunities for Human-Centered AI Research: Understanding Stakeholder Collaboration in Mental Health AI Research
Authors: Yoo Dong Whi , Woo Hayoung , Pendse Sachin R. , Lu Nathaniel Young , Birnbaum Michael L. , Abowd Gregory D. , De Choudhury Munmun
Published: April 2024

In the mental health domain, patient engagement is key to designing human-centered technologies. CSCW and HCI researchers have delved into various facets of collaboration in AI research; however, previous research neglects the individuals who both produce the data and will be most impacted by the resulting technologies, such as patients. This study examines how interdisciplinary researchers and mental health patients who donate their data for AI research collaborate and how we can improve human-centeredness in mental health AI research. We interviewed patient participants, AI researchers, and clinical researchers in a federally funded mental health AI research project. We used the concept of boundary objects to understand stakeholder collaboration. Our findings reveal that the social media data provided by patient participants functioned as boundary objects that facilitated stakeholder collaboration. Although the collaboration appeared to be successful, we argue that building consensus, or understanding each other's perspectives, can improve the human-centeredness of mental health AI research. Based on the findings, we provide suggestions for human-centered mental health AI research, working with data donors as domain experts, making invisible work visible, and privacy implications.
Moving Towards Mobility Justice: Challenges and Considerations for Supporting Advocacy
Authors: Agrawaal Taneea S , Sabie Samar , Soden Robert
Published: April 2024

In response to climate change and continued urbanization, urban transportation systems around the world are undergoing transitions to promote lower-emission vehicles, public transit, biking and walking. However, mobility is a complex issue that raises important questions of social justice as a result of its connections to numerous aspects of everyday life and the broader social and political contexts. Drawing on interviews with transportation advocates across Canada, we identify four ways in which the design and use of existing mobility tools and technologies perpetuate mobility injustices, and deepen the divide between urban planners and the public. Looking across these arguments, we note path-dependence in transportation knowledge infrastructures as a common barrier to mobility justice advocacy that can be difficult to recognize or overcome. Finally, we consider tactics that research in HCI and CSCW might pursue as part of efforts to unsettle path-dependence and reorient transportation planning towards mobility justice.
On Being an Expert: Habitus as a Lens for Understanding Privacy Expertise
Authors: Elmimouni Houda , Baumer Eric P. S. , Forte Andrea
Published: April 2024

How do privacy experts and lay persons differ? We investigate this question using data about the use of privacy-enhancing technologies and strategies from 128 surveys and 17 follow-up interviews with two populations: privacy experts (i.e., privacy researchers and professionals) and privacy laypersons. Findings reveal that both experts and laypersons use common privacy strategies, but experts employ a broader range of strategies, favor open source technologies, and possess a more technical understanding of internet privacy risks and technologies such as onion routing and tracking algorithms. We characterize these differences in terms of sociological habitus, where experts and laypersons differ not only in technical skill but also in broader conceptualizations and practices. From this characterization, we make recommendations for technology design practices, as well as legal and pedagogical implications, that can help decenter the experiences of privacy experts and accommodate privacy laypersons' preferences and habits.
Patent Applications as Glimpses into the Sociotechnical Imaginary: Ethical Speculation on the Imagined Futures of Emotion AI for Mental Health Monitoring and Detection
Authors: Karizat Nadia , Vinson Alexandra H. , Parthasarathy Shobita , Andalibi Nazanin
Published: April 2024

Patent applications provide insight into how inventors imagine and legitimize uses of their imagined technologies; as part of this imagining they envision social worlds and produce sociotechnical imaginaries. Examining sociotechnical imaginaries is important for emerging technologies in high-stakes contexts such as the case of emotion AI to address mental health care. We analyzed emotion AI patent applications (N=58) filed in the U.S. concerned with monitoring and detecting emotions and/or mental health. We examined the described technologies' imagined uses and the problems they were positioned to address. We found that inventors justified emotion AI inventions as solutions to issues surrounding data accuracy, care provision and experience, patient-provider communication, emotion regulation, and preventing harms attributed to mental health causes. We then applied an ethical speculation lens to anticipate the potential implications of the promissory emotion AI-enabled futures described in patent applications. We argue that such a future is one filled with mental health conditions' (or 'non-expected' emotions') stigmatization, equating mental health with propensity for crime, and lack of data subjects' agency. By framing individuals with mental health conditions as unpredictable and not capable of exercising their own agency, emotion AI mental health patent applications propose solutions that intervene in this imagined future: intensive surveillance, an emphasis on individual responsibility over structural barriers, and decontextualized behavioral change interventions. Using ethical speculation, we articulate the consequences of these discourses, raising questions about the role of emotion AI as positive, inherent, or inevitable in health and care-related contexts. We discuss our findings' implications for patent review processes, and advocate for policy makers, researchers and technologists to refer to patent (applications) to access, evaluate and (re)consider potentially harmful sociotechnical imaginaries before they become our reality.
Priming through Persuasion: Towards Secure Password Behavior
Authors: Paudel Rizu , Al-Ameen Mahdi Nasrullah
Published: April 2024

Users tend to create weak passwords even for the important accounts. The prior research shed light on user's insecure password behavior, and why the interventions, including requirement specification (e.g., password composition policies) and feedback systems (e.g., password meters) fail in practice. To this end, we propose and evaluate the concept: priming-through-persuasion in the realm of secure password creation. In particular, we created visual designs, aimed at priming users about the repercussions of weak passwords before their password creation. We base our designs on two forms of persuasion methods: pathos and logos. Pathos appeals to people's emotion in order to persuade them towards an expected behavior, where logos-based rhetoric appeals to a person's sense of reason. We conducted a lab study including participatory design and semi-structured interview with 20 participants. We updated our designs in an iterative manner based on the feedback from our participants in the lab study. To evaluate our updated designs, we conducted a between-subject online study with 131 participants over Amazon Mechanical Turk. Our study provides insight into how the use of persuasion techniques contributed to user attachment and engagement with the design, as well as the comprehension of the conveyed message about password vulnerabilities. Our findings lead to the guideline for future research on leveraging the priming-through-persuasion to complement the existing techniques in encouraging users towards secure behavior.
ProcessGallery: Contrasting Early and Late Iterations for Design Principle Learning
Authors: Yen Yu-Chun Grace , E Jane L. , Jin Hyoungwook , Li Mingyi , Lin Grace , Pan Isabelle Yan , Dow Steven P.
Published: April 2024

Traditional design galleries enable users to search for examples based on surface attributes (e.g., color or style), and largely obscure underlying principles (e.g., hierarchy or readability). We conducted three studies to explore how galleries could be constructed to help novices learn key design principles. Study 1 revealed that novices gain perspective by observing how designs evolve throughout a process. Study 2 found that novices are better at identifying design issues when viewing iterations that show improvements for just one principle at a time, rather than multiple. Building on these insights, we created ProcessGallery, a tool that enables users to browse contrasting pairs of early-and-late iterations of designs that highlight key improvements organized by design principles. In Study 3, a within-subjects experiment, sixteen participants iterated on a seed design after viewing examples in ProcessGallery versus a traditional gallery. Using ProcessGallery, participants found more appropriate examples, assessed designs better, and preferred ProcessGallery for learning compared to a traditional gallery.
Profiling the Offline and Online Risk Experiences of Youth to Develop Targeted Interventions for Online Safety
Authors: Alsoubai Ashwaq , Razi Afsaneh , Agha Zainab , Ali Shiza , Stringhini Gianluca , De Choudhury Munmun , Wisniewski Pamela J.
Published: April 2024

We conducted a study with 173 adolescents (ages 13-21), who self-reported their offline and online risk experiences and uploaded their Instagram data to our study website to flag private conversations as unsafe. Risk profiles were first created based on the survey data and then compared with the risk-flagged social media data. Five risk profiles emerged: Low Risks (51% of the participants), Medium Risks (29%), Increased Sexting (8%), Increased Self-Harm (8%), and High Risk Perpetration (4%). Overall, the profiles correlated well with the social media data with the highest level of risk occurring in the three smallest profiles. Youth who experienced increased sexting and self-harm frequently reported engaging in unsafe sexual conversations. Meanwhile, high risk perpetration was characterized by increased violence, threats, and sales/promotion of illegal activities. A key insight from our study was that offline risk behavior sometimes manifested differently in online contexts (i.e., offline self-harm as risky online sexual interactions). Our findings highlight the need for targeted risk prevention strategies for youth online safety.
SealMates: Improving Communication in Video Conferencing using a Collective Behavior-Driven Avatar
Authors: Armstrong Mark , Yang Chi-Lan , Skiers Kinga , Lim Mengzhen , Gunasekaran Tamil Selvan , Wang Ziyue , Narumi Takuji , Minamizawa Kouta , Pai Yun Suen
Published: April 2024

The limited nonverbal cues and spatially distributed nature of remote communication make it challenging for unacquainted members to be expressive during social interactions over video conferencing. Though it enables seeing others' facial expressions, the visual feedback can instead lead to unexpected self-focus, resulting in users missing cues for others to engage in the conversation equally. To support expressive communication and equal participation among unacquainted counterparts, we propose SealMates, a behavior-driven avatar in which the avatar infers the engagement level of the group based on collective gaze and speech patterns and then moves across interlocutors' windows in the video conferencing. By conducting a controlled experiment with 15 groups of triads, we found the avatar's movement encouraged people to experience more self-disclosure and made them perceive everyone was equally engaged in the conversation than when there was no behavior-driven avatar. We discuss how a behavior-driven avatar influences distributed members' perceptions and the implications of avatar-mediated communication for future platforms.