bluuebunny/crossref_metadata_2025 huggingface dataset:
ConvEx: A Visual Conversation Exploration System for Discord Moderators
Authors: Choi Frederick , Bajpai Tanvi , Pratipati Sowmya , Chandrasekharan Eshwar
Published: October 2023

Moderators are at the core of maintaining healthy online communities. For these moderators, who are often volunteers from the community, filtering through content and responding to misbehavior on time has become increasingly challenging as online communities continue to grow. To address such challenges of scale, recent research has looked into designing better tools for moderators of various platforms (e.g. Reddit, Twitch, Facebook, and Twitter). In this paper, we focus on Discord, a platform where communities are typically involved in large, synchronous group chats, creating an environment with a faster pace and a lack of structure compared to previously studied platforms. To tackle the unique challenges presented by Discord, we developed a new human-AI system called ConvEx for exploring online conversations. ConvEx is an AI-augmented version of the standard Discord interface designed to help moderators be proactive in identifying and preventing potential problems. It provides visual embeddings of conversational metrics, such as activity and toxicity levels, and can be extended to visualize other metrics. Through a user study with eight active moderators of Discord servers, we found that ConvEx supported several high-level strategies in monitoring a server and analyzing conversations. ConvEx allowed moderators to obtain a holistic view of activity across multiple channels on the server while guiding their attention towards problematic conversations and messages in a channel, helping them identify important contextual information to obtain reliable information from the AI analysis while also being able to pick up on contextual nuances which the AI missed. We conclude with design considerations for integrating AI into future interfaces for moderating synchronous, unstructured online conversations.
Critical-Reflective Human-AI Collaboration: Exploring Computational Tools for Art Historical Image Retrieval
Authors: Glinka Katrin , Müller-Birn Claudia
Published: October 2023

Just as other disciplines, the humanities explore how computational research approaches and tools can meaningfully contribute to scholarly knowledge production. Building on related work from the areas of CSCW and HCI, we approach the design of computational tools through the analytical lens of 'human-AI collaboration.' Such work investigates how human competencies and computational capabilities can be effectively and meaningfully combined. However, there is no generalizable concept of what constitutes 'meaningful' human-AI collaboration. In terms of genuinely human competencies, we consider criticality and reflection as guiding principles of scholarly knowledge production and as deeply embedded in the methodologies and practices of the humanities. Although (designing for) reflection is a recurring topic in CSCW and HCI discourses, it has not been centered in work on human-AI collaboration. We posit that integrating both concepts is a viable approach to supporting 'meaningful' human-AI collaboration in the humanities and other qualitative, interpretivist, and hermeneutic research areas. Our research, thus, is guided by the question of how critical reflection can be enabled in human-AI collaboration. We address this question with a use case that centers on computer vision (CV) tools for art historical image retrieval. Specifically, we conducted a qualitative interview study with art historians to explore a) what potentials and affordances art historians ascribe to human-AI collaboration and CV in particular, and b) in what ways art historians conceptualize critical reflection in the context of human-AI collaboration. We extended the interviews with a think-aloud software exploration. We observed and recorded participants' interaction with a ready-to-use CV tool in a possible research scenario. We found that critical reflection, indeed, constitutes a core prerequisite for 'meaningful' human-AI collaboration in humanities research contexts. However, we observed that critical reflection was not fully realized during interaction with the CV tool. We interpret this divergence as supporting our hypothesis that computational tools need to be intentionally designed in such a way that they actively scaffold and support critical reflection during interaction. Based on our findings, we suggest four empirically grounded design implications for 'critical-reflective human-AI collaboration': supporting reflection on the basis of transparency, foregrounding epistemic presumptions, emphasizing the situatedness of data, and strengthening interpretability through contextualized explanations.
DeliData: A Dataset for Deliberation in Multi-party Problem Solving
Authors: Karadzhov Georgi , Stafford Tom , Vlachos Andreas
Published: October 2023

Group deliberation enables people to collaborate and solve problems, however, it is understudied due to a lack of resources. To this end, we introduce the first publicly available dataset containing collaborative conversations on solving a well-established cognitive task, consisting of 500 group dialogues and 14k utterances. In 64% of these conversations, the group members are able to find a better solution than they had identified individually, and in 43.8% of the groups who had a correct answer as their final solution, none of the participants had solved the task correctly by themselves. Furthermore, we propose a novel annotation schema that captures deliberation cues and release all 14k utterances annotated with it. Finally, we use the proposed dataset to develop and evaluate two methods for generating deliberation utterances. The data collection platform, dataset and annotated corpus are publicly available at https://delibot.xyz.
Designing for Common Ground: Visually Representing Conversation Dynamics of Neurodiverse Dyads
Authors: Zolyomi Annuska , Snyder Jaime
Published: October 2023

During interpersonal interactions, conversational moves can help people establishcommon ground ---a shared frame of reference to support communication. Neurodiverse conversation dyads that include autistic and non-autistic members can experience challenges in creating and maintaining such a shared frame of reference due to differing communication and cognitive styles. We conducted a design study to understand conversational patterns among neurodiverse dyads and then used those patterns to co-design concepts for supporting the creation and maintenance of common ground by those conversation pairs. Our study involved two activities with participants: (1) a paired interview with autistic adults and a trusted conversation partner that used a novel swimlane visual elicitation activity, and (2) a remote design study during which the autistic participants designed a game intended to visualize and support neurodiverse conversation dynamics. We found that communication technology can scaffold neurodiverse dyads in locating common ground by supporting crucial individual and joint decision-making; clarification of language and emotions; and embodied sense-making of identity, relationships, and shared information. This project generated insights related to two distinct aspects of designing assistive Information and Communication Technology (ICT) to support autistic individuals: (1) the ability for visual elicitation activities to help autistic individuals recognize interaction patterns, gain a deeper understanding of other's perspectives, and imagine more desirable alternatives, and (2) the importance of recognizing and supporting multi-dimensional aspects of communication practices (i.e., social, emotional, sensory) in establishing and maintaining shared points of reference for neurodiverse conversation dyads.
Diffusion of Community Fact-Checked Misinformation on Twitter
Authors: Drolsbach Chiara Patricia , Pröllochs Nicolas
Published: October 2023

The spread of misinformation on social media is a pressing societal problem that platforms, policymakers, and researchers continue to grapple with. As a countermeasure, recent works have proposed to employ non-expert fact-checkers in the crowd to fact-check social media content. While experimental studies suggest that crowds might be able to accurately assess the veracity of social media content, an understanding of how crowd fact-checked (mis-)information spreads is missing. In this work, we empirically analyze the spread of misleading vs. not misleading community fact-checked posts on social media. For this purpose, we employ a dataset of community-created fact-checks from Twitter's "Birdwatch" pilot and map them to resharing cascades on Twitter. Different from earlier studies analyzing the spread of misinformation listed on third-party fact-checking websites (e.g., snopes.com), we find that community fact-checked misinformation is less viral. Specifically, misleading posts are estimated to receive 36.62% fewer retweets than not misleading posts. A partial explanation may lie in differences in the fact-checking targets: community fact-checkers tend to fact-check posts from influential user accounts with many followers, while expert fact-checks tend to target posts that are shared by less influential users. We further find that there are significant differences in virality across different sub-types of misinformation (e.g., factual errors, missing context, manipulated media). Moreover, we conduct a user study to assess the perceived reliability of (real-world) community-created fact-checks. Here, we find that users, to a large extent, agree with community-created fact-checks. Altogether, our findings offer insights into how misleading vs. not misleading posts spread and highlight the crucial role of sample selection when studying misinformation on social media.
Examining the Impact of Provenance-Enabled Media on Trust and Accuracy Perceptions
Authors: Feng K. J. Kevin , Ritchie Nick , Blumenthal Pia , Parsons Andy , Zhang Amy X.
Published: October 2023

In recent years, industry leaders and researchers have proposed to use technical provenance standards to address visual misinformation spread through digitally altered media. By adding immutable and secure provenance information such as authorship and edit date to media metadata, social media users could potentially better assess the validity of the media they encounter. However, it is unclear how end users would respond to provenance information, or how to best design provenance indicators to be understandable to laypeople. We conducted an online experiment with 595 participants from the US and UK to investigate how provenance information altered users' accuracy perceptions and trust in visual content shared on social media. We found that provenance information often lowered trust and caused users to doubt deceptive media, particularly when it revealed that the media was composited. We additionally tested conditions where the provenance information itself was shown to be incomplete or invalid, and found that these states have a significant impact on participants' accuracy perceptions and trust in media, leading them, in some cases, to disbelieve honest media. Our findings show that provenance, although enlightening, is still not a concept well-understood by users, who confuse media credibility with the orthogonal (albeit related) concept of provenance credibility. We discuss how design choices may contribute to provenance (mis)understanding, and conclude with implications for usable provenance systems, including clearer interfaces and user education.
User Preferences for Interdependent Privacy Preservation Strategies in Social Media
Authors: Necaise Aaron , Tanni Tangila Islam , Williams Aneka , Solihin Yan , Kapadia Apu , Amon Mary Jean
Published: October 2023

Interdependent privacy (IDP) violations occur when users share personal information about others without permission, resulting in potential embarrassment, reputation loss, or harassment. There are several strategies that can be applied to protect IDP, but little is known regarding how social media users perceive IDP threats or how they prefer to respond to them. We utilized a mixed-method approach with a replication study to examine user beliefs about various government-, platform-, and user-level strategies for managing IDP violations. Participants reported that IDP represented a 'serious' online threat, and identified themselves as primarily responsible for responding to violations. IDP strategies that felt more familiar and provided greater perceived control over violations (e.g., flagging, blocking, unfriending) were rated as more effective than platform or government driven interventions. Furthermore, we found users were more willing to share on social media if they perceived their interactions as protected. Findings are discussed in relation to control paradox theory.
Eye into AI: Evaluating the Interpretability of Explainable AI Techniques through a Game with a Purpose
Authors: Morrison Katelyn , Jain Mayank , Hammer Jessica , Perer Adam
Published: October 2023

Recent developments in explainable AI (XAI) aim to improve the transparency of black-box models. However, empirically evaluating the interpretability of these XAI techniques is still an open challenge. The most common evaluation method is algorithmic performance, but such an approach may not accurately represent how interpretable these techniques are to people. A less common but growing evaluation strategy is to leverage crowd-workers to provide feedback on multiple XAI techniques to compare them. However, these tasks often feel like work and may limit participation. We propose a novel, playful, human-centered method for evaluating XAI techniques: a Game With a Purpose (GWAP), Eye into AI, that allows researchers to collect human evaluations of XAI at scale. We provide an empirical study demonstrating how our GWAP supports evaluating and comparing the agreement between three popular XAI techniques (LIME, Grad-CAM, and Feature Visualization) and humans, as well as evaluating and comparing the interpretability of those three XAI techniques applied to a deep learning model for image classification. The data collected from Eye into AI offers convincing evidence that GWAPs can be used to evaluate and compare XAI techniques. Eye into AI is available to the public: https://dig.cmu.edu/eyeintoai/.
How Can We Design Privacy-Friendly Apps for Children? Using a Research through Design Process to Understand Developers' Needs and Challenges
Authors: Ekambaranathan Anirudh , Zhao Jun , Van Kleek Max
Published: October 2023

Mobile apps used by children often make use of harmful techniques, such as data tracking and targeted advertising. Previous research has suggested that developers face several systemic challenges in designing apps that prioritise children's best interests. To understand how developers can be better supported, we used a Research through Design (RtD) method to explore what the future of privacy-friendly app development could look like. We performed an elicitation study with 20 children's app developers to understand their needs and requirements. We found a number of specific technical requirements from the participants about how they would like to be supported, such as having actionable transnational design guidelines and easy-to-use development libraries. However, participants were reluctant to adopt these design ideas in their development practices due to perceived financial risks associated with increased privacy in apps. To overcome this critical gap, participants formulated socio-technical requirements that extend to other stakeholders in the mobile industry, including parents and marketplaces. Our findings provide important immediate and long-term design opportunities for the HCI community, and indicate that support for changing app developers' practices must be designed in the context of their relationship with other stakeholders.
How Time Pressure in Different Phases of Decision-Making Influences Human-AI Collaboration
Authors: Cao Shiye , Gomez Catalina , Huang Chien-Ming
Published: October 2023

Human cognitive and decision-making abilities depreciate under pressure, motivating the emergence of artificial intelligence (AI) systems as decision support tools to assist people in performing tasks under stress. In this work, we study human decision-making behavior and task performance under time pressure---induced from limitedinitial observation time (time to perform the task before providing an initial response without AI input) andfinal decision time (time to weigh an AI's suggestion before reaching a collective human-AI team answer)---for spatial reasoning and count estimation tasks. Our results show that, while the impact of initial observation time on AI-assisted decision-making was dependent on task nature, participants were more likely to follow AI suggestions when they were provided with longer final decision time; moreover, although participants generally tended to adhere to their initial responses, they had more agency when they were more logically engaged in a task. Our results offer a nuanced understanding of human-AI collaboration under time pressure in different phases of the decision-making process.
Investigating AI Teammate Communication Strategies and Their Impact in Human-AI Teams for Effective Teamwork
Authors: Zhang Rui , Duan Wen , Flathmann Christopher , McNeese Nathan , Freeman Guo , Williams Alyssa
Published: October 2023

Recently, AI is integrating into teams to collaborate with humans as a teammate with the goal of achieving unprecedented team outcomes. Much of the coordination between humans and AI teammates relies on human-AI communication, which is challenging due to AI's limitations on natural language communication. Thus, it is essential to identify and develop effective communication strategies for AI teammates in human-AI teams to facilitate the coordination process. Through interviews with 60 participants who collaborated with an AI teammate in a multiplayer online game, in this paper, we explore communication strategies that humans expect AI teammates to apply to support human-AI coordination and collaboration in dyadic teaming environments, and how the AI teammate's communication can impact teaming processes. Our findings highlight four communication strategies AI teammates should apply to support their coordination with humans in dyadic teaming environments. We also find that AI teammates' proactive communication with humans could facilitate the development of human trust and situation awareness, whereas AI lacking such proactive communication is often not perceived as a teammate. Our study extends the current CSCW/HCI research on human-AI communication in teaming environments by shedding light on how communication should be structured in dyadic human-AI teams for effective and smooth collaboration.
Judgment Sieve: Reducing Uncertainty in Group Judgments through Interventions Targeting Ambiguity versus Disagreement
Authors: Chen Quan Ze , Zhang Amy X.
Published: October 2023

When groups of people are tasked with making a judgment, the issue of uncertainty often arises. Existing methods to reduce uncertainty typically focus on iteratively improving specificity in the overall task instruction. However, uncertainty can arise from multiple sources, such as ambiguity of the item being judged due to limited context, or disagreements among the participants due to different perspectives and an under-specified task. A one-size-fits-all intervention may be ineffective if it is not targeted to the right source of uncertainty. In this paper we introduce a new workflow, Judgment Sieve, to reduce uncertainty in tasks involving group judgment in a targeted manner. By utilizing measurements that separate different sources of uncertainty during an initial round of judgment elicitation, we can then select a targeted intervention adding context or deliberation to most effectively reduce uncertainty on each item being judged. We test our approach on two tasks: rating word pair similarity and toxicity of online comments, showing that targeted interventions reduced uncertainty for the most uncertain cases. In the top 10% of cases, we saw an ambiguity reduction of 21.4% and 25.7%, and a disagreement reduction of 22.2% and 11.2% for the two tasks respectively. We also found through a simulation that our targeted approach reduced the average uncertainty scores for both sources of uncertainty as opposed to uniform approaches where reductions in average uncertainty from one source came with an increase for the other.
Knowing Unknown Teammates: Exploring Anonymity and Explanations in a Teammate Information-Sharing Recommender System
Authors: Musick Geoff , Gilman Elizabeth S. , Duan Wen , McNeese Nathan J. , Knijnenburg Bart , O'Neill Thomas
Published: October 2023

A growing organizational trend is to utilize ad-hoc team formation which allows for teams to intentionally form based on the member skills required to accomplish a specific task. Due to the unfamiliar nature of these teams, teammates are often limited by their understanding of one another (e.g., teammate preferences, tendencies, attitudes) which limits the team's functioning and efficiency. This study conceptualizes and investigates the use of a teammate information-sharing recommender system which selectively shares interpersonal recommendations between unfamiliar teammates (e.g., "Your voice may be overshadowed by this teammate when making decisions...") to promote teammate understanding. Through a mixed-methods approach involving 105 participants working on actual unfamiliar teams, this study explores how presentation elements such as anonymity and explanations influence system perceptions and how anonymity influences team outcomes. Results indicate that anonymizing recommendations was associated with worse team measures, particularly team satisfaction and team cohesion. Qualitative results shed light on why team members perceived privacy concerns and team benefits associated with using the system. We contribute to CSCW through a better understanding of how to support unfamiliar teams, the conceptualization and empirical investigation of a novel teammate information-sharing recommender system, and foundational design recommendations associated with such a system.
Learning from the Ex-Believers: Individuals' Journeys In and Out of Conspiracy Theories Online
Authors: Engel Kristen , Phadke Shruti , Mitra Tanushree
Published: October 2023

Conspiracy theories in online spaces, such as anti-vaccination or QAnon, present a unique amalgamation of misinformation, disinformation, and propaganda disguised in entertaining, attention-grabbing content that may appeal to peoples' cultural, moral, or social identities. Studies aiming to understand how people may engage with conspiracy theory content online, or how they may lose belief in conspiracy theories often approach research from a purely theoretical or empirical point of view. In this work, through in-depth interviews with former believers of more than 12 conspiracy theories, with experiences across almost two decades and numerous online platforms, we aim to contribute an understanding of how various online and offline factors synergize to shape a user's engagement, tenure, and disengagement in online conspiracy theorizing. We further investigate how some users recover from conspiracy theorizing with the help of online recovery communities. We find how pre-existing biases and predispositions towards conspiracy theorizing often carry over in online spaces where a user's conspiracy theory worldviews further evolve through content recommendations, interactions in online communities, and socially-primed self-reflections. We also find reasons, such as exposure to inconsistencies in theories or toxicity and anti-social attitudes in online spaces, through which users get disillusioned from conspiracy theories. Our work has implications in bringing forward often unobserved impacts of internet-mediated conspiracy theorizing on the believers---the resulting mental health issues such as depression, distrust and anxiety, and social isolation---which is comparable to the indoctrination trauma. Moreover, our interviews reveal an important role played by online communities in helping users recover from conspiracy theory beliefs by finding empathy and solidarity in fellow former believers. We conclude by providing a path forward for how social computing researchers can contribute online community designs that aid existing issues surrounding safety, inclusivity, and lack of resources in existing online conspiracy theory recovery communities.
Reconsidering Priorities for Digital Maternal and Child Health: Community-centered Perspectives from South Africa
Authors: Coleman Toshka , Till Sarina , Farao Jaydon , Shandu Londiwe , Khuzwayo Nonkululeko , Muthelo Livhuwani , Mbombi Masenyani , Bopape Mamare , van Heerden Alastair , Mothiba Tebogo , Norris Shane , Verdezoto Nervo , Densmore Melissa
Published: October 2023

Especially in developing regions, parents are rarely given a direct voice in the design of digital maternal and child health (MCH) interventions. Instead, MCH needs and requirements are driven by organizations and health workers. In this research, we engage with both rural and urban parents and community leaders to better understand their challenges and priorities for digital MCH and propose a parent-centered agenda for human-computer interaction research. This paper reports on the community-based, digital MCH priorities identified in our research, and describes how we approached community discourse and co-design of digital initiatives for these priorities, through parent-centered workshops with low-resource South African communities. Furthermore, we provide the parent-centered design opportunities and tensions we discovered for digital MCH in South African contexts, such as designing for local contexts and languages, designing for accessibility and connectedness, and highlighting the underdeveloped digital MCH niches. Finally, we highlight the importance of including facilitators for co-design workshops, such as using intermediaries and design cards.
Remove, Reduce, Inform: What Actions do People Want Social Media Platforms to Take on Potentially Misleading Content?
Authors: Atreja Shubham , Hemphill Libby , Resnick Paul
Published: October 2023

To reduce the spread of misinformation, social media platforms may take enforcement actions against offending content, such as adding informational warning labels, reducing distribution, or removing content entirely. However, both their actions and their inactions have been controversial and plagued by allegations of partisan bias. When it comes to specific content items, surprisingly little is known about what ordinary people want the platforms to do. We provide empirical evidence about a politically balanced panel of lay raters' preferences for three potential platform actions on 368 news articles. Our results confirm that on many articles there is a lack of consensus on which actions to take. We find a clear hierarchy of perceived severity of actions with a majority of raters wanting informational labels on the most articles and removal on the fewest. There was no partisan difference in terms of how many articles deserve platform actions but conservatives did prefer somewhat more action on content from liberal sources, and vice versa. We also find that judgments about two holistic properties, misleadingness and harm, could serve as an effective proxy to determine what actions would be approved by a majority of raters.
Seeing is Not Believing: A Nuanced View of Misinformation Warning Efficacy on Video-Sharing Social Media Platforms
Authors: Guo Chen , Zheng Nan , Guo Chengqi (John)
Published: October 2023

Misinformation warnings have become the de facto solution for fighting fake news online. Our study brings attention to the challenge of developing effective misinformation warnings on short video-sharing platforms. We conducted semi-structured interviews with the think-aloud protocol to understand how users interact with and perceive misinformation warnings, specifically the interstitial and contextual warnings adopted by TikTok and Instagram Reels. We recruited 28 regular users of TikTok and Instagram Reels for this study. We contribute to the evolving scholarship on social media misinformation mitigation by casting light on nuanced participant interactions with and perceptions of misinformation warnings and how these interactions and perceptions influence the perceived accuracy of short video content. Our findings are threefold. First, the present study shows that specific contextual warnings do not always elicit behavioral adherence but can alert users to be vigilant about misinformation. Second, users' perceptions of interstitial and contextual warnings are influenced by the warning's explicitness and the risk level of the misinformation. Third, we identify the least and most effective/favored warning designs to help make accuracy judgments according to the participants. To this end, our findings have implications for improving the design of misinformation warnings on short video-sharing platforms.
Similar Others, Social Comparison, and Social Support in Online Support Groups
Authors: Barta Kristen , Wolberg Katelyn , Andalibi Nazanin
Published: October 2023

Social comparison and social support have implications for individuals' wellbeing, offline and on social media. Perceptions of similarity underlie both social comparison and social support processes, though how comparison and support function in tandem in online spaces, and which aspects of identity and experiential similarity are salient to which comparison and support outcomes, merits investigation. Through interviews with people who have joined or considered joining social media-based support groups following pregnancy loss (N=18), we provide an intracommunity view into social comparison within online support groups. We identify a set of identity and experience attributes that inform perceptions of similarity and difference in these support spaces. We characterize tensions arising from these attributes and propose the preliminary Social Comparison and Social Support in Online Support Groups model to describe interactions between social support and comparison processes within online support groups. We further discuss findings' implications for design, including via introducing the tolerance principle of online health support groups. CAUTION: This paper includes quotes about pregnancy loss.
SUMMIT: Scaffolding Open Source Software Issue Discussion Through Summarization
Authors: Gilmer Saskia , Bhat Avinash , Shah Shuvam , Cherry Kevin , Cheng Jinghui , Guo Jin L.C.
Published: October 2023

Issue Tracking Systems (ITS) often support commenting on software issues, which creates a space for discussions centered around bug fixes and improvements to the software. For Open Source Software (OSS) projects, issue discussions serve as a crucial collaboration mechanism for diverse stakeholders. However, these discussions can become lengthy and entangled, making it hard to find relevant information and make further contributions. In this work, we study the use of summarization to aid users in collaboratively making sense of OSS issue discussion threads. Through an empirical investigation, we reveal a complex picture of how summarization is used by issue users in practice as a strategy to help develop and manage their discussions. Grounded on the different objectives served by the summaries and the outcome of our formative study with OSS stakeholders, we identified a set of guidelines to inform the design of collaborative summarization tools for OSS issue discussions. We then developed SUMMIT, a tool that allows issue users to collectively construct summaries of different types of information discussed, as well as a set of comments representing continuous conversations within the thread. To alleviate the manual effort involved, SUMMIT uses techniques that automatically detect information types and summarize texts to facilitate the generation of these summaries. A lab user study indicates that, as the users of SUMMIT, OSS stakeholders adopted different strategies to acquire information on issue threads. Furthermore, different features of SUMMIT effectively lowered the perceived difficulty of locating information from issue threads and enabled the users to prioritize their effort. Overall, our findings demonstrated the potential of SUMMIT, and the corresponding design guidelines, in supporting users to acquire information from lengthy discussions in ITSs. Our work sheds light on key design considerations and features when exploring crowd-based and machine-learning-enabled instruments for asynchronous collaboration on complex tasks such as OSS development.
Supporting Co-Regulation and Motivation in Learning Programming in Online Classrooms
Authors: Goswami Lahari , Senges Alexandre , Estier Thibault , Cherubini Mauro
Published: October 2023

Self-regulation of learning in programming has been extensively investigated, emphasising an individual's metacognitive and motivational regulation components. However, learning often happens in socially situated contexts, and little emphasis has been paid to studying social modes of regulation in programming. We designed Thyone, a collaborative Jupyter Notebook extension to support learners' programming regulation in an online classroom context with the overall aim to foster their intrinsic motivation toward programming. Thyone's salient features - Flowchart, Discuss and Share Cell - incorporate affordances for learners to co-regulate their learning and drive their motivation. In an exploratory quasi-experimental study, we investigated learners' engagement with Thyone's features and assessed its influence on their learning motivation in an introductory programming course. We found that Thyone facilitated the co-regulation of programming learning and that the users' engagement with Thyone appeared to positively influence components of their motivation: interest, autonomy, and relatedness. Our results inform the design of technological interventions to support co-regulation in programming learning.
"Our Job is to be so Temporary": Designing Digital Tools that Meet the Needs of Care Managers and their Patients with Mental Health Concerns
Authors: Kornfield Rachel , Lattie Emily G. , Nicholas Jennifer , Knapp Ashley A. , Mohr David C. , Reddy Madhu
Published: October 2023

Digital tools have potential to support collaborative management of mental health conditions, but we need to better understand how to integrate them in routine healthcare, particularly for patients with both physical and mental health needs. We therefore conducted interviews and design workshops with 1) a group of care managers who support patients with complex health needs, and 2) their patients whose health needs include mental health concerns. We investigate both groups' views of potential applications of digital tools within care management. Findings suggest that care managers felt underprepared to play an ongoing role in addressing mental health issues and had concerns about the burden and ambiguity of providing support through new digital channels. In contrast, patients envisioned benefiting from ongoing mental health support from care managers, including support in using digital tools. Patients' and care managers' needs may diverge such that meeting both through the same tools presents a significant challenge. We discuss how successful design and integration of digital tools into care management would require reconceptualizing these professionals' roles in mental health support.
Conflicts of Control: Continuous Blood Glucose Monitoring and Coordinated Caregiving for Teenagers with Type 1 Diabetes
Authors: Yu Yihan , McDonald David W.
Published: October 2023

Continuous blood glucose monitoring (CGM) is a bio-sensing technology designed to help individuals with diabetes understand and manage their blood sugar levels. Better control and management have the promise of extending and improving the quality of life with diabetes. For teenagers with Type 1 Diabetes (T1D), CGM has the potential to allow for a more collaborative T1D management and control experience by sharing real-time blood glucose data with caregivers. To understand how well CGM designs are living up to this promise and to identify potential challenges, we used the Asynchronous Remote Communities (ARC) with 16 teenagers with T1D and their informal caregivers over a 6 month period to investigate families' lived experiences with CGM and its data sharing function. We found three challenges in caregiver-teenager collaboration; lack of visibility into behaviors behind data, difficulties balancing teenage daily life with T1D control, and technical limitations of current CGM systems. We also found three issues that hinder informal caregiving coordination; unclear task assignment, difficulties in maintaining standards of care, and a lack of informal caregiver rotation. We provide a number of design implications based on these findings to better facilitate home care for teenagers' T1D.
Probing Respiratory Care With Generative Deep Learning
Authors: Scurto Hugo , Similowski Thomas , Bianchini Samuel , Caramiaux Baptiste
Published: October 2023

This paper combines design, machine learning and social computing to explore generative deep learning as both tool and probe for respiratory care. We first present GANspire, a deep learning tool that generates fine-grained breathing waveforms, which we crafted in collaboration with one respiratory physician, attending to joint materialities of human breathing data and deep generative models. We then relate a probe, produced with breathing waveforms generated with GANspire, and led with a group of ten respiratory care experts, responding to its material attributes. Qualitative annotations showed that respiratory care experts interpreted both realistic and ambiguous attributes of breathing waveforms generated with GANspire, according to subjective aspects of physiology, activity and emotion. Semi-structured interviews also revealed experts' broader perceptions, expectations and ethical concerns on AI technology, based on their clinical practice of respiratory care, and reflexive analysis of GANspire. These findings suggest design implications for technological aids in respiratory care, and show how ambiguity of deep generative models can be leveraged as a resource for qualitative inquiry, enabling socio-material research with generative deep learning. Our paper contributes to the CSCW community by broadening how generative deep learning may be approached not only as a tool to design human-computer interactions, but also as a probe to provoke open conversations with communities of practice about their current and speculative uses of AI technology.
Powerful Futures: How a Big Tech Company Envisions Humans and Technologies in the Workplace of the Future
Authors: Cheon EunJeong
Published: October 2023

Big tech companies have had increasing control over how we work with technologies and how technologies define the work we do. In this paper, I identify the sociotechnical futures that Amazon-one of the big tech companies-envisions and the future of work that the company is moving toward. I explore the future of fulfillment centers through an analysis of the patents on fulfillment center technologies which Amazon may turn into reality one day. In my analysis, I focus on humans by asking how they are configured in the future of fulfillment centers and, more specifically, how Amazon envisions the role of human labor within work automation and AI systems. The analysis reveals where and how humans are expected to "step in" to operate the future of fulfillment centers. I discuss my findings within and beyond CSCW, highlighting the importance of studying tech companies' imaginaries. I argue that by understanding tech companies' imaginaries, it becomes possible for us to launch effective sociotechnical interventions to negotiate or even resist their specific imaginaries and/or design ways for a more democratic uptake of companies' future technologies. Finally, I articulate practical ways in which patents can be utilized in CSCW research.
The Relevance of KES-Oriented Processes for the Implementation of ERP Systems: Findings From an Empirical Study in German SMEs
Authors: Rutz Philipp , Kotthaus Christoph , Pinatti de Carvalho Aparecido Fabiano , Randall Dave , Pipek Volkmar
Published: October 2023

Past and current research has shown that Knowledge and Expertise Sharing (KES) is central to the appropriation of enterprise software such as Enterprise Resource Planning (ERP) systems. ERP implementation projects in Small and Medium-sized Enterprises (SMEs) are often driven by research and practice. However, they tend to focus on the 'go live' moment rather than on the subsequent, much longer post-implementation phase. This, we argue, results in decreased utilisation over time and an increased need for workarounds. In this paper, we draw on an empirical study within four organisations which exposes the limitations of KES in ERP implementation projects in SMEs, especially in regard to the preparation of key users. Our findings suggest that, despite key users' essential role in these projects, they are often chosen haphazardly and are ill-prepared. As a result, they cannot fulfil their role of facilitating KES with end users, who end up appropriating ERP systems mostly through 'learning by doing'. This stems directly from complex and largely unrecognised processes involving consultants, hotlines, management, key users and end users. In this paper, we introduce and discuss specific socio-technical, KES-oriented measures which can potentially lead to sustainable KES throughout the ERP life-cycle for longer-term success.
"A feeling of déjà vu": The Effects of Avatar Appearance-Similarity on Persuasiveness in Social Virtual Reality
Authors: Shih Meng Ting , Lee Yi-Chieh , Huang Chih-Mao , Chan Liwei
Published: October 2023

The similarity effect refers to the tendency for people to be more easily influenced by others who resemble them in appearance. This phenomenon has been found to have positive impacts, including on the building of trust, that enrich the quality of communication (e.g., fluency or collaboration performance). While research has shown that the similarity effect occurs in screen-based communication platforms, it remains unclear how this phenomenon impacts user perceptions, especially of others' persuasiveness, in immersive environments such as virtual reality (VR). In this study, we adopted a mixed-methods approach to exploring how interaction with avatars of similar appearance to one's own self-representation influences conversations. Such similarity was operationalized as having three levels: identicality, moderate similarity, and dissimilarity. The study found that avatars of moderate similarity have the greatest persuasiveness; however, in both identicality and moderate similarity conditions, participants felt it was easier to communicate with and lower eeriness rating to avatars than in the dissimilarity condition. Multiple linear regression further revealed that users who had relatively low self-esteem and/or were relatively conscientious were more susceptible to the positive effect of appearance similarity on persuasiveness. We conclude that the similarity effect, especially when the similarity in question is moderate, could be leveraged to support persuasiveness in VR-based communication.
Benefits of Community Voice: A Framework for Understanding Inclusion of Community Voice in HCI4D
Authors: Saha Manika , Lindsay Stephen , Varghese Delvin , Bartindale Tom , Olivier Patrick
Published: October 2023

Community voice is widely used in computer-supported cooperative work (CSCW) and human-computer interaction (HCI) work with underserved communities. However, the term is unresolved, denoting disparate activities, methods, and phenomena that are at their most useful when combined. We argue for a rethink by setting out a more nuanced understanding of "community" and "voice". Building on our own experiences of HCI for development (HCI4D) work and the existing literature, we propose a framework for the benefits it can bring to those who actively engage with communities as part of their work. This framework can be understood in terms of its four constituent benefits for CSCW and HCI4D practitioners: (i) understanding context, (ii) creating empathy, (iii) leveraging local skills and knowledge, and (iv) building trust and buy-in. We reflect on how an improved understanding of these benefits applies to three prior projects with women living in Bangladesh and discuss the issues and need for more work on community voice. Finally, we discuss how this more detailed perspective on community voice helps us understand power dynamics and polyvocal communities in development contexts.
Beyond the Session: Centering Teleoperators in Socially Assistive Robot-Child Interactions Reveals the Bigger Picture
Authors: Elbeleidy Saad , Mott Terran , Liu Dan , Do Ellen , Reddy Elizabeth , Williams Tom
Published: October 2023

Socially assistive robots play an effective role in children's therapy and education. Robots engage children and provide interaction that is free of the potential judgment of human peers and adults. Research in socially assistive robots for children generally focuses on therapeutic and educational outcomes for those children, informed by a vision of autonomous robots. This perspective ignores therapists and educators, who operate these robots in practice. Through nine interviews with individuals who have used robots to deliver socially assistive services to neurodivergent children, we (1) define a dual-cycle model of therapy that helps capture the domain expert view of therapy, (2) identify six core themes of teleoperator needs and patterns across these themes, (3) provide high-level guidelines and detailed recommendations for designing teleoperated socially assistive robot systems, and (4) outline a vision of robot-assisted therapy informed by these guidelines and recommendations that centers teleoperators of socially assistive robots in practice.
Care Workers' Wellbeing in Data-Driven Healthcare Workplace: Identity, Agency, and Social Justice
Authors: Sun Yuling , Ma Xiaojuan , Lindtner Silvia , He Liang
Published: October 2023

This paper zooms in on a particularly precarious and largely invisibilized group of care workers: middle-aged and less-educated female migrant workers from rural China. Drawing from a mixed-methods study, we specifically examine how the extensive use of data-driven technologies impacts care workers' wellbeing in the workplace. Our findings suggest the extensive use of data-driven healthcare technologies are eroding care workers' workplace wellbeing, especially their sense of identity, agency, and perceived justice. Specifically, in the data-driven workplace, care workers are treated as a servant to data, instead of a human with agency and knowledge. They are no merely care workers who provide various care services for care receivers, but also data workers, whose practices and agency are greatly limited by data. This aggravates preexisting hardship of care workers, and reproduces new social injustice. We suggest CSCW researchers and practitioners take into account how pre-existing social structures shaped the designs of socio-technological systems, and reconceptualize the paradigm of "data-drivenness" for more just and ethical data-driven healthcare technologies.