bluuebunny/crossref_metadata_2025 huggingface dataset:
The role of automated controls and streamlined compliance in managing risks in digital finance
Authors: Shibahathulla T. K. , Ali Mohammed Ashraf , Gayyor Osama , Ghaffari Abu Zar
Published: November 2024

This study investigates the role of automated controls and streamlined compliance in managing risk in digital finance institutions. Through a mixed-methods approach, combining quantitative analysis and qualitative insights from leading Indian digital finance companies (CRED, ZEST MONEY and PHONE PAY), the research examines the effectiveness of advanced technologies in enhancing risk management practices. The study found strong positive correlations between the adoption of automated controls and key performance metrics, including the fraud detection rate (0.68), regulatory reporting accuracy (0.72), and overall risk management effectiveness (0.75). Regression analysis revealed that automated control implementation was the strongest predictor of risk management effectiveness. A comparative analysis of different digital finance models highlights the varying challenges and benefits of implementing these technologies. This study proposes a holistic, future-proof framework that integrates AI, machine learning and blockchain for real-time risk monitoring, automated regulatory reporting and dynamic compliance management. This framework addresses the limitations of the current fragmented approaches and offers a scalable solution that is adaptable to evolving regulatory landscapes. The findings of this study have significant implications for practitioners and policymakers, emphasizing the need for investment in strong automated systems and adaptive regulatory frameworks. While acknowledging the limitations in sample size and longitudinal scope, this research provides a foundation for future studies and offers practical insights for enhancing risk management in the rapidly evolving digital finance sector. The study concludes that effective integration of automated controls and streamlined compliance is crucial for ensuring the resilience, integrity and sustainable growth of the digital finance ecosystem.
Machine Learning-Based Drug Repositioning of Novel Human Aromatase Inhibitors Utilizing Molecular Docking and Molecular Dynamic Simulation
Authors: Bodun Damilola Samuel , Abdulwahab Adeniyi Ayinde , Adegbenro Temitope Ibukunoluwa , Chidinma Emidun Olayinka , Osunnaya Samuel Aduramurewa , Alonge Mary Oluwatominsin , Diyaolu Zainab Olaide , Abdulazeez Muhammad Temitope , Abolade Rachael Oluwakamiye
Published: November 2024

In the continuous search for potential drug-like molecules for diseases, drug repurposing offers a solution by identifying new roles for the existing drugs. For breast cancer, which is the leading cause of death among women, discovering a new drug with fewer side effects can take more than a year, and drug repurposing provides a faster alternative. In this study, we used machine learning (ML) and structure-based screening to evaluate molecules from the ChEMBL library against Human Aromatase, an enzyme that is involved in the conversion of androgens to estrogen. The virtual screening output identified 148 potential compounds. These compounds were further subjected to detailed structure-based virtual screening, followed by assessment of induced fit docking, quantum-based docking and ADMET properties. The top compound, CHEMBL502014 and the co-crystallized ligand (reference) were evaluated through MD simulations for 100 ns. The results indicated that the CHEMBL502014-Aromatase complex demonstrated better conformational stability throughout the simulation compared to the reference ligand. Based on these analyses, we propose CHEMBL502014 and other top compounds with known activities against various proteins as potential candidates for anti-cancer activity against breast cancer. These compounds should be further evaluated using experimental assays to confirm their effectiveness against breast cancer.
Rhythm of Work: Mixed-methods Characterization of Information Workers Scheduling Preferences and Practices
Authors: Sun Lu , Mok Lillio , Sen Shilad , Sarrafzadeh Bahareh
Published: November 2024

As processes around hybrid work, spatially distant collaborations, and work-life boundaries grow increasingly complex, managing workers' schedules for synchronous meetings has become a critical aspect of building successful global teams. However, gaps remain in our understanding of workers' scheduling preferences and practices, which we aim to fill in this large-scale, mixed-methods study of individuals' calendars in a multinational organization. Using interviews with eight participants, survey data from 165 respondents, and telemetry data from millions of meetings scheduled by 211 thousand workers, we characterize scheduling preferences, practices, and their relationship with each other and organizational factors. We find that temporal preferences can be broadly classified as either cyclical, such as suitability of certain days, or relational, such as dispersed meetings, at various time scales. Furthermore, our results suggest that these preferences are disconnected from actual practice--albeit with several notable exceptions--and that individual differences are associated with factors like meeting load, time-zones, importance of meetings to job function, and job titles. We discuss key themes for our findings, along with the implications for calendar and scheduling systems and socio-technical systems more broadly.
"Just Like, Risking Your Life Here": Participatory Design of User Interactions with Risk Detection AI to Prevent Online-to-Offline Harm Through Dating Apps
Authors: Datey Isha , Zytko Douglas
Published: November 2024

Social computing platforms facilitate interpersonal harms that manifest across online and physical realms such as sexual violence between online daters and sexual grooming through social media. Risk detection AI has emerged as an approach to preventing such harms, however a myopic focus on computational performance has been criticized in HCI literature for failing to consider how users should interact with risk detection AI to stay safe. In this paper we report an interview study with woman-identifying online daters (n=20) about how they envision interacting with risk detection AI and how risk detection models can be designed pursuant to such interactions. In accordance with this goal, we engaged women in risk detection model building exercises to build their own risk detection models. Findings show that women anticipate interacting with risk detection AI to augment - not replace - their personal risk assessment strategies. They likewise designed risk detection models to amplify their subjective and admittedly biased indicators of risk. Design implications involve the notion of personalizable risk detection models, but also ethical concerns around perpetuating problematic stereotypes associated with risk.
Supporting Organizations in Improving Employee Bulk E-mail --- A Tool Design and Evaluation Study
Authors: Kong Ruoyan , Sun Ruixuan , Zhang Charles Chuankai , Yuan Ye , Konstan Joseph A.
Published: November 2024

Organizations often send bulk emails to employees to make them aware of policy changes, organization plans, and events. Many of these emails, however, are long digests with many separate messages that waste employees' time and reduce their awareness. This study introduces CommTool--a prototype tool to help organizational communicators better understand their emails' performance and cost. We first interviewed 5 communicators and identified the need to measure the performance of each message within bulk email. Then we iteratively designed and deployed an organizational bulk email evaluation platform (CommTool), which enables communicators to get diverse message-level metrics such as reading time, relevance rate, comments, etc. We evaluated these designs through a 2-month field deployment with 5 communicators and 149 organization employees. We found that 1) the message-level metrics, such as reading time and relevance rate, helped communicators understand their audience and design bulk emails; 2) the cost and reputation metrics did not influence the organization leaders' decisions. We summarize with suggestions on designing organizational bulk email evaluation platforms that provide message-level performance and cost information.
Entangled Independence: From Labor Rights to Gig "Empowerment" Under the Algorithmic Gaze
Authors: Anjali Anwar Ira , Garcia Patricia , Hui Julie
Published: November 2024

This study offers a critical analysis of how the Indian gig platform, Urban Company (UC), mobilizes the promise of empowerment to assemble, discipline and algorithmically entangle its gig labor force. Much of the scholarship on gig work has described how the (mis)classification of gig workers as independent contractors allows gig corporations to abdicate their employment responsibilities, thereby disentangling the traditional employer-employee relationship. By contrast, UC promises to invest in it's Indian gig workforce, with offerings such as health insurance and financial loans to counter the enduring socio-economic precarity of the Indian labor market. However, through a critical content analysis of UCs' public media, we find that UC's organization of such ''social security'' benefits rests on an insidious system of algorithmic evaluation and classification. Access to benefits is conditional, determined by the platform's algorithmic control mechanisms, which classify workers as worthy or unworthy on a shifting basis. Mobilizing Agre's analysis of business discourses of ''empowered work'', we highlight how UC's algorithmic classification of workers creates a system of ''conditional empowerment.'' This system reflects a critical shift in the labor process, from rights-based labor regimes to a neoliberal social order where individuals must constantly prove their worth.
Value Tensions in OpenStreetMap: Openness, Membership, and Policy in Online Communities
Authors: Chauhan Aarjav , Sarkar Dipto , Agrawaal Taneea S. , Soden Robert
Published: November 2024

The social life and long-term trajectories of online peer production communities are shaped and animated in part by value tensions that arise when distributed, heterogeneous participants are brought together into collaboration. This study of OpenStreetMap (OSM) draws upon values-based approaches to investigate how peer production communities enact their values and navigate tensions between them. We examine how conflicts within the community over the rise of corporate participation in OSM provided a stage for the articulation and enactment of community values, shedding light on the broader dynamics and trajectory of the platform and its participants. The contributions of this work include reflections on how increasing corporate participation in OSM intersects with discourses about the emancipatory potential of emerging mapping technologies, insights into the challenges of scaling membership in peer production communities, and exploring the role of values in understanding the social life and governance of online communities.
RAI Guidelines: Method for Generating Responsible AI Guidelines Grounded in Regulations and Usable by (Non-)Technical Roles
Authors: Constantinides Marios , Bogucka Edyta , Quercia Daniele , Kallio Susanna , Tahaei Mohammad
Published: November 2024

Many guidelines for responsible AI have been suggested to help AI practitioners in the development of ethical and responsible AI systems. However, these guidelines are often neither grounded in regulation nor usable by different roles, from developers to decision makers. To bridge this gap, we developed a four-step method to generate a list of responsible AI guidelines; these steps are: (1) manual coding of 17 papers on responsible AI; (2) compiling an initial catalog of responsible AI guidelines; (3) refining the catalog through interviews and expert panels; and (4) finalizing the catalog. To evaluate the resulting 22 guidelines, we incorporated them into an interactive tool and assessed them in a user study with 14 AI researchers, engineers, designers, and managers from a large technology company. Through interviews with these practitioners, we found that the guidelines were grounded in current regulations and usable across roles, encouraging self-reflection on ethical considerations at early stages of development. This significantly contributes to the concept of 'Responsible AI by Design'- a design-first approach that embeds responsible AI values throughout the development lifecycle and across various business roles.
"Mirror, mirror in the call": Exploring the Ambivalent Nature of the Self-view in Video Meeting Systems with Self-Reported & Eye-Tracking Data
Authors: Seitz Julia , Benke Ivo , Maedche Alexander
Published: November 2024

Video meeting systems offer great potential for work and life, but they can also have negative effects. One reason is the presence of technical stimuli that do not exist in the physical world. A prominent example is the self-view feature, a mirrored image of oneself shown during the video meeting. The self-view feature comes with a trade-off between the advantage of enhancing control and the disadvantage of increasing cognitive load of its users. So far, research is scarce when it comes to understanding this ambivalent nature and studies mostly relied on self-reported data without considering the actual interaction with the self-view. To address this gap, we conducted an experimental study with 57 participants and two design variants (with/without self-view), analyzed user perceptions through surveys and interviews, and explored gaze patterns using eye-tracking technology. Results reveal varying perceptions of cognitive load and control among self-view users and between the design variants, highlighting its ambivalent nature. We see differences in how participants interact with the self-view. In a cluster analysis, we identify three user groups (Benefiting Users, Cognitively Challenged Users, Control Losing Users). These groups also show differences in visual behavior, especially median fixation duration, and user characteristics. Based on our findings, we outline design recommendations for more flexible and intelligent design solutions by considering user groups and their individual differences.
Metaverse Perspectives from Japan: A Participatory Speculative Design Case Study
Authors: Hohendanner Michel , Ullstein Chiara , Miyamoto Dohjin , Huffman Emma F , Socher Gudrun , Grossklags Jens , Osawa Hirotaka
Published: November 2024

Currently, the development of the metaverse lies in the hands of industry. Citizens have little influence on this process. Instead, to do justice to the pluralism of (digital) societies, we should strive for an open discourse including many different perspectives on the metaverse and its core technologies such as AI. We utilize a participatory speculative design (PSD) approach to explore Japanese citizens' perspectives on future metaverse societies, as well as social and ethical implications. Our contributions are twofold. Firstly, we demonstrate the effectiveness of PSD in engaging citizens in critical discourse on emerging technologies like the metaverse by presenting our workshop framework and participants' processes. Secondly, we identify key themes from participants' perspectives, providing insights for culturally sensitive design and development of virtual environments. Our analysis shows that participants imagine the metaverse to have the potential to solve a variety of societal issues; for example, breaking down barriers of physical environments for communication, social interaction, crisis preparation, and political participation, or tackling identity-related issues. Regarding future metaverse societies, participants' imaginations raise critical questions about human-AI relations, technical solutionism, politics and technology, globalization and local cultures, and immersive technologies. We discuss implications and contribute to expanding conversations on metaverse developments.
A Cross Community Comparison of Muting in Conversations of Gendered Violence on Reddit
Authors: Foriest Jasmine C , Mittal Shravika , Bray Kirsten , Tran Anh-Ton , De Choudhury Munmun
Published: November 2024

Gender-based violence (GBV) is an ongoing public health issue. Prevention practice and research on GBV contend with an incomplete understanding of its public health burden due to gender-exclusive definitions of GBV and inhibited survivor disclosure. Prior work in CSCW and HCI has explored sensitive disclosures and GBV conversations but excludes examination of conversation dynamics in surfacing knowledge of GBV. We used a mixed-methods approach to understand the phenomenon characterized by Muted Group Theory as a mechanism inhibiting disclosure in the context of GBV discussions on Reddit. Using an iterative process informed by literature of GBV research on cis-gender women, girls, trans, and non-binary populations, we developed comprehensive keywords to obtain, annotate, and analyze 298 posts and 10,369 comments about GBV across 7 subreddits. We found that muting faced by survivors offline, precluding reporting, is replicated on the Reddit platform. This study surfaced 5 categories of muting in discussions of GBV situated by variations in communication norms between dominant and non-dominant groups. These findings were supported by analysis of linguistic attributes that inform an Ensemble Classifier's detection of muting in conversations of GBV. The results offer that muting is a harmful occurrence in online disclosures of GBV that is mediated by existing moderation practices. This work contributes an expanded understanding of GBV conversations online, muting as a feature of those conversations, and an initial foray into detection to inform muting prevention.
(Dis)placed Contributions: Uncovering Hidden Hurdles to Collaborative Writing Involving Non-Native Speakers, Native Speakers, and AI-Powered Editing Tools
Authors: Xiao Yimin , Chen Yuewen , Yamashita Naomi , Chen Yuexi , Liu Zhicheng , Gao Ge
Published: November 2024

Content creation today often takes place via collaborative writing. A longstanding interest of CSCW research lies in understanding and promoting the coordination between co-writers. However, little attention has been paid to individuals who write in their non-native language and to co-writer groups involving them. We present a mixed-method study that fills the above gap. Our participants included 32 co-writer groups, each consisting of one native speaker (NS) of English and one non-native speaker (NNS) with limited proficiency. They performed collaborative writing adopting two different workflows: half of the groups began with NNSs taking the first editing turn and half had NNSs act after NSs. Our data revealed a 'late-mover disadvantage' exclusively experienced by NNSs: an NNS's ideational contributions to the joint document were suppressed when their editing turn was placed after an NS's turn, as opposed to ahead of it. Surprisingly, editing help provided by AI-powered tools did not exempt NNSs from being disadvantaged. Instead, it triggered NSs' overestimation of NNSs' English proficiency and agency displayed in the writing, introducing unintended tensions into the collaboration. These findings shed light on the fair assessment and effective promotion of a co-writer's contributions in language diverse settings. In particular, they underscore the necessity of disentangling contributions made to the ideational, expressional, and lexical aspects of the joint writing.
Attention is All They Need: Exploring the Media Archaeology of the Computer Vision Research Paper
Authors: Goree Samuel , Appleby Gabriel , Crandall David , Su Norman Makoto
Published: November 2024

Research papers, in addition to textual documents, are a designed interface through which researchers communicate. Recently, rapid growth has transformed that interface in many fields of computing. In this work, we examine the effects of this growth from a media archaeology perspective, through the changes to figures and tables in research papers. Specifically, we study these changes in computer vision over the past decade, as the deep learning revolution has driven unprecedented growth in the discipline. We ground our investigation through interviews with veteran researchers spanning computer vision, graphics, and visualization. Our analysis focuses on the research attention economy: how research paper elements contribute towards advertising, measuring, and disseminating an increasingly commodified "contribution." Through this work, we seek to motivate future discussion surrounding the design of both the research paper itself as well as the larger sociotechnical research publishing system, including tools for finding, reading, and writing research papers.
Reconfiguring Data Relations: Institutional Dynamics around Data in Local Governance
Authors: Lee Seolha , Dourish Paul
Published: November 2024

Data infrastructures are major sites through which city government operates and therefore of special interest to critical scholars of technology. Yet ways that these infrastructures are embedded into the institutional fabric of government make them difficult to navigate and often resistant to abstract critique and generalized analysis. We present an ethnographic study of a design effort launched by a team inside a city government to build and deploy a tool with a specific focus on equity and justice. By integrating data from multiple sources and bringing it together in a spatial framework, the team sought to bring neighborhood voices, often marginalized in planning efforts, into the center of the conversation. Tracing the negotiations over data's value in this process, we unveil various tensions involved in this effort, within and between different groups, and how the group of city employees navigated these tensions, mainly by adjusting the relations of the groups around data -- which we call ''data relations.'' Narratives in organizational communications were key in both revealing and mobilizing the data relations constituting the institutional data infrastructure. With the empirical findings, we argue that tracing the data relations can be an effective way for the CSCW community to understand the institutional data politics and make interventions in the systems in power.
EduLive: Re-Creating Cues for Instructor-Learners Interaction in Educational Live Streams with Learners' Transcript-Based Annotations
Authors: Fang Jingchao , Park Jeongeon , Kim Juho , Wang Hao-Chuan
Published: November 2024

Educational live streaming has become a complement to in-person teaching. While synchronous instructor-learner communication is useful, the technology-mediated nature of live streaming can obscure many interaction cues (e.g., learners' facial expressions and body language), which dampens the instructors' ability to respond to remote learners' needs. We explore the opportunity of leveraging real-time transcripts generated from instructors' audio as a basis for re-creating interaction cues. Transcripts can be leveraged to reveal the content of live streams in a form that learners can trace back and annotate, and such annotations can be further aggregated and presented to instructors as signals to assist them in tracking learners' engagement. By designing and evaluating our proof-of-concept prototype system, EduLive, we show that instructors benefited from the summative information extracted from learners' annotations, and the context provided by the transcript enhanced their ability to answer learners' questions. Our system contributes to the design space of social annotations in CSCW by employing social annotations in educational live streaming scenarios.
Chirp: The Impact of Private Online Self-Disclosure on Perceived Social Support
Authors: Massachi Talie , Roy John , Choi Lauren , Hoefer Gabriela , Wallace Shaun , Huang Jeff
Published: November 2024

As social media continues to grow as a space for emotional self-disclosure, it is important to understand whether self-disclosure acts as a causal factor impacting positive outcomes for users. Thus we developed Chirp, an anonymous social media sandbox space designed to explore the underlying effects of disclosure within online spaces. Users in Chirp are prompted to self-disclose moods and emotions using emojis. Through a between-subjects study among a cohort of first-year undergraduate student users on Chirp, we evaluate the effect of self-disclosure within semi-private online spaces on social support. While Chirp use does not show a significant increase in measured feelings of social support, user responses suggest that self-disclosure in Chirp may provide more social support than typical social media use or simple mood tracking over a two-week period. Our findings indicate that even in pseudo-anonymous, low-bandwidth communication platforms, self-disclosure may cause increased feelings of social support. This work highlights the impact of communication in semi-private online spaces on perceived social support.
Format Matters: Comparing the Inclusiveness and Effectiveness of Hybrid and Remote Meetings
Authors: Yarmand Matin , Kreamer Liana , Suri Siddharth , Jaffe Sonia
Published: November 2024

The surge in remote and hybrid work has provided many benefits in terms of flexibility and autonomy, but it also presents challenges when it comes to team collaboration and meetings. Using a mixed-methods field study, we compare hybrid and remote meeting configurations to better understand how to improve the inclusiveness and effectiveness of these different meeting modalities. Our findings indicate that overall, fully-remote meetings are the most inclusive and effective. Fully-remote meetings are perceived as more inclusive of remote attendees despite their lower chat usage compared to hybrid meetings. We also find that people's preferred format varies depending on the purpose or type of meeting and the skill set of the meeting moderator. We provide recommendations for improving meeting inclusion and effectiveness in both hybrid and remote meetings, such as training moderators and integrating chats with the main meeting.
TreeQuestion: Assessing Conceptual Learning Outcomes with LLM-Generated Multiple-Choice Questions
Authors: Cheng Zirui , Xu Jingfei , Jin Haojian
Published: November 2024

The advances of generative AI have posed a challenge for using open-ended questions to assess conceptual learning outcomes, as it is increasingly common for students to use tools like ChatGPT to generate long textual answers. However, teachers still have to spend substantial time reading the answers and inferring students' learning outcomes. We present TreeQuestion, a human-in-the-loop system designed to help teachers create a set of multiple-choice questions to assess students' conceptual learning outcomes. When a teacher seeks to assess students' comprehension of specific concepts, TreeQuestion taps into the wealth of knowledge embedded within large language models and generates a set of multiple-choice questions organized in a tree-like structure. We evaluated TreeQuestion with 96 students and 10 teachers. Results indicated that students achieved similar performance in multiple-choice questions generated by TreeQuestion and open-ended questions graded by teachers. Meanwhile, TreeQuestion could reduce teachers' efforts in creating and grading the multiple-choice questions in contrast to manually generated open-ended questions. We estimate that in a hypothetical class with 20 students, using multiple-choice questions from TreeQuestion may require only 4.6% of the time compared to open-ended questions for assessing learning outcomes.
Hear Me Out: Supporting Citizens to Create Comprehensible Contributions on Urban Participation Platforms
Authors: Borchers Marten , Soroko Daria , Tavanapour Navid , Bittner Eva
Published: November 2024

Urbanization has increased societal tensions and led to the growth of citizen participation in urban planning, which is often conducted in computer-supported environments and progressively online to include a high number of citizens. Past projects have shown that digital participation creates new challenges and that collaborative online discussions do not achieve the quality of on-site scenarios, as the interactions, in-depth exchange of opinions, and quality of contributions vary. Within a design science research project, we examine
how to support citizens in creating comprehensible contributions on urban participation platforms.
We identify issues, formulate meta-requirements, and derive design principles to implement feasible prototypes that we evaluated in focus groups. Our findings extend the existing research about urban participation platforms in civic tech, urban informatics, and planning, with validated design principles that specify AI-based feedback and further features to increase interactions and support citizens in producing more specific contributions.
Together in the Mikrokosmos: Exploring Disabled Embodied Experiences in Designed Sociotechnical Worlds
Authors: Ringland Kathryn E. , Carstensdottir Elin , Tung Matthew , Seetharaman Bhavani , Eagle Tessa , Baltaxe-Admony Leya Breanna , Weatherwax Kevin
Published: November 2024

For geographically dispersed communities, including professional, academic, hobbyist, and fandom communities, in-person events and meetups play a crucial role in the function and health of the whole community. Building off prior literature, we take a nuanced look at large scale community events that blend online and offline interactions across digital and physical embodied experiences in order to build understanding of how this knowledge can be leveraged to build more accessible hybrid modalities for community events. Building on this foundation, this paper presents a comprehensive study using ethnographic methods to examine large scale community events, specifically stadium music concerts of varying modalities (e.g., in-person, live-streamed). Using a disability lens, we explore how such community events are experienced and technologically mediated, as well as how the community and individuals design and construct their own sociotechnical worlds across multiple modalities and embodied experiences. By centering the disabled embodied experience, we examine how community members navigate the concert environment, time, and the social experience of concert attendance. Based on our findings, we highlight the nuanced complexities of inclusive community-centered large scale, multi-modal events.
An Empirical Study on Social Anxiety in a Virtual Environment through Mediating Variables and Multiple Sensor Data
Authors: Kim Eunji , Jin Seungwan , Han Kyungsik
Published: November 2024

Social anxiety disorder is a psychological condition characterized by excessive nervousness in social situations, such as interpersonal interactions. Exposure therapy has shown benefits in its treatment, and virtual reality (VR) technology has gained much attention for reducing physical and psychological distance and providing additional quantitative evidence from the data generated by standard VR devices (e.g., head-mounted display). Clinical psychology studies have highlighted the importance of mediating variables of social anxiety; however, existing VR-based social anxiety studies have neglected such variables with respect to user experience and data analysis in the context of VR, although these variables could provide insights into the design and use of VR for the treatment of social anxiety. In this study, we focused on two representative mediating variables of social anxiety: (1)
the gap between self-presentation motivation and expectancy
, and (2)
self-focused attention
. We used sensor data (e.g., head movement, eye movement, eye gaze, and psychological signals) to investigate the impact of these variables on users' anxiety responses in VR. We developed VR-based Social Anxiety Support Tool (VRST) that reflects the theoretical design elements of effective anxiety provocation. Based on the results of a user study with 30 participants, we confirmed that the mediating variables were associated with social anxiety in the VR environment. We also found that the mediating variables were associated with eye gaze, eye pupil, head movement, and body temperature. Our study results provide researchers, designers, and practitioners with empirical evidence and implications for the use of VR technology and sensor data in the mental health context.
Gaining Technological Autonomy and Soci-emotional Support: A Case Study of How and Why Chinese Older Adults Engage with a Semi-acquaintance Online Community
Authors: Qian Zhigu , Fu Jiaojiao , Zhou Yangfan
Published: November 2024

Older adults are often underserved and marginalized in technology engagement due to their reluctance and the barriers they face in adopting and engaging with mainstream technology. However, Pinxiaoquan, a social feature of an e-commerce platform in China, has gained a large number of older users. This work investigates how and why Chinese older adults use Pinxiaoquan, aiming to unveil the underlying logic and inspire technology-inclusive design for older adults. To this end, we conducted a mixed-methods qualitative study over two years, which included online observation, and semi-interview. We found that Pinxiaoquan's success among Chinese older adults is mainly due to its ability to inspire technological autonomy and provide social-emotional support. Rather than simply lowering technical barriers or asking them to seek assistance outside the platform, Pinxiaoquan builds a semi-acquaintance online community based on location and social ties that allows older adults to realize technical support mutually. Pinxiaoquan also fulfills their social-emotional needs, such as free online expression, memory creation and preservation, relationship expansion and maintenance, and a sense of value. Our research contributes to the HCI community by highlighting the importance of improving older adults' technology autonomy following their specific social and cultural background and social-emotional needs. This work also provides unique insights and implications for building inclusive technology for the growing aging population.
Application of Prompt Learning Models in Identifying the Collaborative Problem Solving Skills in an Online Task
Authors: Zhu Mengxiao , Wang Xin , Wang Xiantao , Chen Zihang , Huang Wei
Published: November 2024

Collaborative problem solving (CPS) competence is considered one of the essential 21st-century skills. To facilitate the assessment and learning of CPS competence, researchers have proposed a series of frameworks to conceptualize CPS and explored ways to make sense of the complex processes involved in collaborative problem solving. However, encoding explicit behaviors into subskills within the frameworks of CPS skills is still a challenging task. Traditional studies have relied on manual coding to decipher behavioral data for CPS, but such coding methods can be very time-consuming and cannot support real-time analyses. Scholars have begun to explore approaches for constructing automatic coding models. Nevertheless, the existing models built using machine learning or deep learning techniques depend on a large amount of training data and have relatively low accuracy. To address these problems, this paper proposes a prompt-based learning pre-trained model. The model can achieve high performance even with limited training data. In this study, three experiments were conducted, and the results showed that our model not only produced the highest accuracy, macro F1 score, and kappa values on large training sets, but also performed the best on small training sets of the CPS behavioral data. The application of the proposed prompt-based learning pre-trained model contributes to the CPS skills coding task and can also be used for other CSCW coding tasks to replace manual coding.
Challenges and Opportunities for Tool Adoption in Industrial UX Research Collaborations
Authors: Kang Daye , Rzeszotarski Jeffrey M.
Published: November 2024

UX research practitioners analyze qualitative data to comprehend users' needs and synthesize design implications for software systems. Collaborating with multiple stakeholders is inevitable for these professionals and adds additional pressure to their already laborious data analysis tasks. In this paper, we investigate how these practitioners' multi-stakeholder collaboration affects their data analysis practices. Specifically, we investigate the challenges qualitative UX research (QUXR) practitioners face, limitations of the current qualitative data analysis (QDA) support tools they use, and design implications for future QDA tools to address the limitations. Through semi-structured interviews combined with diagramming activities with thirteen industry QUXR practitioners, we have revealed that collaboration becomes a bigger challenge than data analysis and that it often leads to (1) preferring simple tools despite the QDA specialized tools' beneficial features and (2) wanting to triangulate their work to convince stakeholders. Finally, we have synthesized these results and suggest design implications for QDA support tools.
(De)Noise: Moderating the Inconsistency Between Human Decision-Makers
Authors: Grgić-Hlača Nina , Ali Junaid , Gummadi Krishna P. , Wortman Vaughan Jennifer
Published: November 2024

Prior research in psychology has found that people's decisions are often inconsistent. An individual's decisions vary across time, and decisions vary even more across people. Inconsistencies have been identified not only in subjective matters, like matters of taste, but also in settings one might expect to be more objective, such as sentencing, job performance evaluations, or real estate appraisals. In our study, we explore whether algorithmic decision aids can be used to moderate the degree of inconsistency in human decision-making in the context of real estate appraisal. In a large-scale human-subject experiment, we study how different forms of algorithmic assistance influence the way that people review and update their estimates of real estate prices. We find that both (i) asking respondents to review their estimates in a series of algorithmically chosen
pairwise comparisons
and (ii) providing respondents with
traditional machine advice
are effective strategies for influencing human responses. Compared to simply reviewing initial estimates one by one, the aforementioned strategies lead to (i) a higher
propensity to update
initial estimates, (ii) a higher
accuracy
of post-review estimates, and (iii) a higher degree of
consistency
between the post-review estimates of different respondents. While these effects are more pronounced with traditional machine advice, the approach of reviewing algorithmically chosen pairs can be implemented in a wider range of settings, since it does not require access to ground truth data.
El costo de la independencia: Latino house-cleaners in Technology-Mediated Labour Markets
Authors: Jaimes Rodriguez Isabella , Petterson Adrian , Doggett Olivia , Chandra Priyank
Published: November 2024

This paper explores the interplay between identity, collective action, and digital marketplaces among Latino house-cleaners in Toronto. Domestic work, such as house-cleaning, has traditionally been devalued, gendered, and delegated to marginalised immigrant populations. The informal nature of house-cleaning work, also introduces precarity, vulnerabilities and potential avenues of exploitation. Drawing on 19 interviews with Latino house-cleaners, the paper underscores how identity shapes experiences and strategies in the labour markets. We find that Latino house-cleaners prefer to use asynchronous digital marketplaces, such as social media commerce groups and online classified advertisement websites, as the platforms provides flexibility and control over their labour outcomes. The paper further delves into how group identities online - such as being Latino and women - foster collective action and solidarity. The paper provides insights on how we can support the needs support the challenges and needs of immigrant domestic workers in technology-mediated labour markets.
Improving Collaborative Management of Multiple Mental and Physical Health Conditions: A Qualitative Inquiry into Designing Technology-Enabled Services for Eliciting Patients' Values
Authors: Liem William Wibowo , Lattie Emily G. , Taple Bayley J. , Stamatis Caitlin A. , Gordon Jacob , Kornfield Rachel , Berry Andrew B.L.
Published: November 2024

People with multiple chronic conditions (MCC) face challenges planning health care collaboratively with primary care clinicians, particularly when their priorities conflict. These challenges intensify with symptoms of anxiety or depression. Elicitation of patients' values is promoted as a means to aligning patient and clinician priorities in primary care, and as a component of psychotherapy for anxiety and depression. But, these approaches remain siloed. We conducted a qualitative interview study to understand patients' preferences for Technology Enabled Services (TESs) to coordinate values elicitation across primary and mental health care settings. Many participants preferred face-to-face elicitation by a mental health clinician; some preferred elicitation via telehealth and some preferred self-directed elicitation. Participants' preferences were influenced by: 1) how they perceived the rationale and benefits of values elicitation; 2) how they perceived the training and credibility of people facilitating elicitation; and 3) how they perceived their own capacity to engage in values elicitation. Participants also shared numerous concerns about values elicitation that warrant critical examination of TESs to support it.
Union Makes Us Strong: Space, Technology, and On-Demand Ridesourcing Digital Labour Platforms
Authors: Thuppilikkat Ashique Ali , Dhar Dipsita , Chandra Priyank
Published: November 2024

The entry of on-demand ridesourcing digital labour platforms (OR-DLPs) in Kolkata, India, restructured the local taxi-cab service industry's economic geography and spatial practices. Notably, they eroded the significance of the spatial fixity of taxi stands operated by traditional trade unions, enmeshed in local society's partisan political dynamics. Therefore, OR-DLPs triggered a reconfiguration of the socio-spatial and political practices around the taxi-cab industry in the city. Globally, traditional trade unions have struggled to organise workers in informal work arrangements and DLPs. However, in Kolkata, the Kolkata Ola-Uber App-Cab Operator and Drivers Union has proved to be successful. They established hybrid and networked unionism through technological affordances, placing worker-organisers rather than external organisers at the centre of their organisational structure. Furthermore, they undertook tech-mediated resistance against the OR-DLPs, local bureaucracy (e.g. the police) and the state. We explore this context to examine the impact of OR-DLPs on labour geography, worker-organising and resistance practices, along with the revitalisation strategies of traditional trade unions in response. From a non-Western context, we expand the frame for CSCW and HCI scholars' ongoing efforts to design worker-centric technologies for resistance.
Transformer-Based Quantification of the Echo Chamber Effect in Online Communities
Authors: Ghafouri Vahid , Alatawi Faisal , Karami Mansooreh , Such Jose , Suarez-Tangil Guillermo
Published: November 2024

An Echo Chamber on social media refers to the environment where like-minded people hear the echo of each others' voices, opinions, or beliefs, which reinforce their own. Echo Chambers can turn social media platforms into collaborative venues that polarize and radicalize users rather than broadening their exposure to diverse information. Having a quantified metric for measuring the Echo Chamber effect can aid moderators and policymakers in tracking and mitigating online polarization and radicalization. Existing methods for Echo Chamber detection are either one-dimensional, only considering the network behavior of users while ignoring their semantic behavior, or require demanding supervised labeling, which is both expensive and less generalizable.

This paper proposes a new metric to quantify the Echo Chamber effect using Transformer models for context-sensitive processing of natural language (NLP). Our metric quantifies (1) the effect of an Echo Chamber through the inverse effect of
user diversity
, and (2) polarization by means of
user separability
between two Echo Chambers in a topic. Leveraging this metric, we further propose an NLP-based embedding that represents the users' activity. Our model is simultaneously effective, computationally cheap, and unsupervised. As our method is unsupervised, it makes existing collaborative moderation efforts to thwart Echo Chamber effects more efficient by addressing the problem of identifying narrow information bases for algorithmic biases and misinformation detection. We run our analysis on three recent highly controversial political topics and a non-controversial topic: Russo-Ukrainian War, Abortion, Gun-Control, and SXSW music festival. Our results offer data-driven findings such as a higher Echo Chamber effect among Republicans over Democrats and diverse explicit support for Ukraine, especially among Democrats. We also observe a direct relationship between the Echo Chamber effect and polarization while observing that the low Echo Chamber effect for the Russo-Ukraine war is accompanied by a low polarization; and vice versa for Gun-Control.
"A Lot of Moving Parts": A Case Study of Open-Source Hardware Design Collaboration in the Thingiverse Community
Authors: Cheng Kathy , Zhou Shurui , Olechowski Alison
Published: November 2024

Open-source is a decentralized and collaborative method of development that encourages open contribution from an extensive and undefined network of individuals. Although commonly associated with software development (OSS), the open-source model extends to hardware development, forming the basis of open-source hardware development (OSH). Compared to OSS, OSH is relatively nascent, lacking adequate tooling support from existing platforms and best practices for efficient collaboration. Taking a necessary step towards improving OSH collaboration, we conduct a detailed case study of DrawBot, a successful OSH project that remarkably fostered a long-term collaboration on Thingiverse - a platform not explicitly intended for complex collaborative design. Through analyzing comment threads and design changes over the course of the project, we found how collaboration occurred, the challenges faced, and how the DrawBot community managed to overcome these obstacles. Beyond offering a detailed account of collaboration practices and challenges, our work contributes best practices, design implications, and practical implications for OSH project maintainers, platform builders, and researchers, respectively. With these insights and our publicly available dataset, we aim to foster more effective and efficient collaborative design in OSH projects.