bluuebunny/crossref_metadata_2025 huggingface dataset:
Understanding Chinese Internet Users' Perceptions of, and Online Platforms' Compliance with, the Personal Information Protection Law (PIPL)
Authors: Zhou Morgana Mo , Qu Zhiyan , Wan Jinhan , Wen Bo , Yao Yaxing , Lu Zhicong
Published: April 2024

The Personal Information Protection Law (PIPL) was implemented in November 2021 to safeguard the personal information rights and interests of Internet users in China. However, the impact and existing shortcomings of the PIPL remain unclear, carrying significant implications for policymakers. This study examined privacy policies on 13 online platforms before and after the PIPL. Concurrently, it conducted semi-structured interviews with 30 Chinese Internet users to assess their perceptions of the PIPL. Users were also given tasks to identify non-compliance within the platforms, assessing their ability to address related privacy concerns effectively. The research revealed various instances of non-compliance in post-PIPL privacy policies, especially concerning inadequate risk assessments for sensitive data. Although users identified some non-compliant activities like app eavesdropping, issues related to individual consent proved challenging. Surprisingly, over half of the interviewees believed that the government could access their personal data without explicit consent. Our findings and implications can be valuable for lawmakers, online platforms, users, and future researchers seeking to enhance personal privacy practices both in China and globally.
Understanding Farmers' Data Collection Practices on Small-to-Medium Farms for the Design of Future Farm Management Information Systems
Authors: Friedman Natalie , Tan Zm , Haskins Micah N. , Ju Wendy , Bailey Diane , Longchamps Louis
Published: April 2024

Farm Management Information Systems (FMIS) integrate data from a variety of sources, including sensors, for the purpose of enabling farmers to interpret past activity and predict future performance. FMIS is traditionally designed for and used by large farms, given their capital and need for automation and scale-up. This paper examines the current data collection practices on small and medium farms so that FMIS systems can be better designed to their needs. Our empirical research comprises interviews conducted during 10 farm visits. Our semi-structured interviews incorporated questions about daily activities, points of decision-making, data sharing, and incentives for data collection. We analyzed the interviews by focusing on possible obstacles to adopting expanding digital data collection practices and how expanded data collection might help fulfill farmers' goals and motivations. We found that farmers use their own bespoke data collection techniques instead of or in parallel to more formalized methods and often hold key observations and hypotheses in their heads rather than committing them to any data collection system at all. Key barriers to FMIS adoption include technology skepticism, technical hurdles, lack of support, and self-doubt in technical skills. Based on this empirical work and analysis, we recommend that FMIS systems can best address the needs of small and medium farms by 1) accounting for the farmers' different approaches to memorizing vs. storing data, 2) integrating rather than trying to replace existing practices, and 3) considering the economic and political motivations driving farm decision-making and practices.
Understanding Phantom Tactile Sensation on Commercially Available Social Virtual Reality Platforms
Authors: Chen Qijia , Spapé Michiel M. , Jacucci Giulio
Published: April 2024

Phantom tactile sensation (PTS) is usually experienced by participants in laboratory settings with the assistance and supervision of professionals. Extensive reports from users demonstrate they experience PTS on commercially available virtual reality (VR) platforms. We gathered and analyzed 2885 posts by 1408 users to understand how users obtain PTS and how they evaluate their PTS experience. We observed that users experience PTS in three ways: 1) starting to feel it naturally, 2) intentionally developing the ability to experience PTS, and 3) feeling it under substance use. Users perceive the sensation differently. Many people perceive PTS as positive, enhancing their immersion and bringing people closer. While other users consider it a negative experience as it exacerbates harassment issues, or the sensation itself is negative, even painful. We discuss the perceived causes of PTS and how social VR conditions users' evaluation of their PTS experience. We further reflect on PTS from the perspective of the risk of VR use in real-life scenarios. Finally, we provide design implications on employing PTS to enhance users' VR experience and reduce the negatives PTS brings.
Understanding Practices around Computational News Discovery Tools in the Domain of Science Journalism
Authors: Nishal Sachita , Sinchai Jasmine , Diakopoulos Nicholas
Published: April 2024

Science and technology journalists today face challenges in finding newsworthy leads due to increased workloads, reduced resources, and expanding scientific publishing ecosystems. Given this context, we explore computational methods to aid these journalists' news discovery in terms of their agency and time-efficiency. We prototyped three computational information subsidies into an interactive tool that we used as a probe to better understand how such a tool may offer utility or more broadly shape the practices of professional science journalists. Our findings highlight central considerations around science journalists' user agency, contexts of use, and professional responsibility that such tools can influence and could account for in design. Based on this, we suggest design opportunities for enhancing and extending user agency over the longer-term; incorporating contextual, personal and collaborative notions of newsworthiness; and leveraging flexible interfaces and generative models. Overall, our findings contribute a richer view of the sociotechnical system around computational news discovery tools, and suggest ways to improve such tools to better support the practices of science journalists.
Understanding the Technology-Mediated Home Phlebotomy Ecosystem in India
Authors: Gupta Meghna , Karnatak Nimisha , Jain Mohit
Published: April 2024

The increasing platformization of healthcare services in India, in the wake of COVID-19, has resulted in huge demand for home phlebotomy. However, there is a limited understanding of the impact of digitization on home phlebotomists' workflows. To address this gap, we conducted 26 semi-structured interviews with home phlebotomists, riders, and patients, supplemented by observations of the entire workday of 3 phlebotomists. We found that home phlebotomists' technology-mediated workflows are organized in ways that enable them to build strong support networks of human infrastructure, helping them negotiate and optimize their daily workflows. Moreover, while the digitization of their workflows resulted in continued surveillance, it empowered them to justify their decisions and present evidence of work when needed. Based on our findings, we discuss implications for equitable platform work and the future of platformized health and conclude with design recommendations for telehealth platforms offering home phlebotomy services.
W4-Groups: Modeling the Who, What, When and Where of Group Behavior via Mobility Sensing
Authors: Atrey Akanksha , Zakaria Camellia , Balan Rajesh , Shenoy Prashant
Published: April 2024

Human social interactions occur in group settings of varying sizes and locations, depending on the type of social activity. The ability to distinguish group formations based on their purposes transforms how group detection mechanisms function. Not only should such tools support the effective detection of serendipitous encounters, but they can derive categories of relation types among users. Determining who is involved, what activity is performed, and when and where the activity occurs are critical to understanding group processes in greater depth, including supporting goal-oriented applications (e.g., performance, productivity, and mental health) that require sensing social factors. In this work, we propose W4-Groups that captures the functional perspective of variability and repeatability when automatically constructing short-term and long-term groups via multiple data sources (e.g., WiFi and location check-in data). We design and implement W4-Groups to detect and extract all four group features who-what-when-where from the user's daily mobility patterns. We empirically evaluate the framework using two real-world WiFi datasets and a location check-in dataset, yielding an average of 92% overall accuracy, 96% precision, and 94% recall. Further, we supplement two case studies to demonstrate the application of W4-Groups for next-group activity prediction and analyzing changes in group behavior at a longitudinal scale, exemplifying short-term and long-term occurrences.
"We're Not in That Circle of Misinformation": Understanding Community-Based Trusted Messengers Through Cultural Code-Switching
Authors: Chen Amy Z. , Park Chaeeun , Darkwa Asantewaa , Holliday Rhonda C. , Best Michael L.
Published: April 2024

While social computing technologies are increasingly being used to counter misinformation, more work is needed to understand how they can support the crucial work of community-based trusted messengers, especially in marginalized communities where distrust in health authorities is rooted in historical inequities. We describe an early exploration of these opportunities in our collaboration with Black and Latinx young adult "Peer Champions" addressing COVID-19 vaccine hesitancy the U.S. state of Georgia. We conducted interviews engaging them with a social media monitoring and outreach dashboard we designed, to probe their understanding of their roles and current and potential use of digital platforms. With the concept of cultural code-switching as a framing, we found that the Peer Champions leveraged their particular combination of cultural, health, and digital literacy skills to understand their communities' concerns surrounding misinformation and to communicate health information in a culturally appropriate manner. While being positioned between their communities and public health research and practice motivated and enabled their work, it also introduced challenges in finding (mis)information online and navigating tensions around authenticity and respect when engaging those close to them. Our research contributes towards characterizing the valuable and difficult work trusted messengers do, and (re)imagining collaboratively designed interpretive digital tools to support them.
"We are no Luddites!" - CSCW, Co-Determination and Digital Transformation in Germany
Authors: Gerbracht Marc , Krüger Max , De Castro Leal Débora , Tolmie Peter , Wulf Volker
Published: April 2024

HCI and CSCW have a long-standing interest in the consequences of digital transformation for work practices. A key part of this is its impact on labour relations. While prior HCI studies have examined labour relations in domains such as platform work, few have looked at industrial settings. This paper presents an ethnographic study of works councils in Small and Medium-sized Enterprises (SMEs) in Germany. Works councils exemplify the principle of Co-Determination, which is the right of employees to participate in the design and management of their workplace and play a vital role in vetting new technologies whenever they imply changes in work practices. A number of issues confront works councils when they attempt to accomplish this, namely a lack of time, knowledge, and external support. We discuss these issues and consider their implications for both design and the constitution of workplace democracy in future industrial settings.
"What are you doing, TikTok?" : How Marginalized Social Media Users Perceive, Theorize, and "Prove" Shadowbanning
Authors: Delmonaco Daniel , Mayworm Samuel , Thach Hibby , Guberman Josh , Augusta Aurelia , Haimson Oliver L.
Published: April 2024

Shadowbanning is a unique content moderation strategy receiving recent media attention for the ways it impacts marginalized social media users and communities. Social media companies often deny this content moderation practice despite user experiences online. In this paper, we use qualitative surveys and interviews to understand how marginalized social media users make sense of shadowbanning, develop folk theories about shadowbanning, and attempt to prove its occurrence. We find that marginalized social media users collaboratively develop and test algorithmic folk theories to make sense of their unclear experiences with shadowbanning. Participants reported direct consequences of shadowbanning, including frustration, decreased engagement, the inability to post specific content, and potential financial implications. They reported holding negative perceptions of platforms where they experienced shadowbanning, sometimes attributing their shadowbans to platforms' deliberate suppression of marginalized users' content. Some marginalized social media users acted on their theories by adapting their social media behavior to avoid potential shadowbans. We contributecollaborative algorithm investigation : a new concept describing social media users' strategies of collaboratively developing and testing algorithmic folk theories. Finally, we present design and policy recommendations for addressing shadowbanning and its potential harms.
Understanding Perceived Utility and Comfort of In-Home General-Purpose Sensing through Progressive Exposure
Authors: Jain Pranut , Xu Andrew , Downes Thomas , Kim Injung , Khan Talha , Biehl Jacob T. , Lee Adam J.
Published: April 2024

A fundamental paradigm shift for in-home sensing is apparent. Special-purpose sensing, where there is a one-to-one relationship between sensors and applications, is evolving into general-purpose sensing, where there is a many-to-many relationship between sensors and applications. This new shift may impact how individuals think about in-home sensing, where utility and comfort are often linked to applications rather than sensed data. We explore the evolution of individuals' perceptions as they become increasingly and contextually aware of sensor capabilities and data characteristics. Through a multi-phase study where 12 participants were progressively led through six exposure conditions across laboratory and home environments, we find that exposure changes represent inflection points for perceptions of utility and comfort with data collection. These changes define opportunities for increasing trust in sensing infrastructures via data- and context-aware interventions, managing over-reliance on awareness notifications, and providing data-enabled "what if" analyses to balance comfort and utility within an individual's unique context and environment.
When Workers Want to Say No: A View into Critical Consciousness and Workplace Democracy in Data Work
Authors: DiSalvo Carl , Rothschild Annabel , Schenck Lara L. , Shapiro Ben Rydal , DiSalvo Betsy
Published: April 2024

In this paper, we describe and reflect upon the development of critical consciousness and workplace democracy within an experimental workplace called DataWorks. Through DataWorks, we hire adults from communities historically minoritized in computing education and data careers, and train them in entry-level data skills developed through work on client projects. In this process, workers gain a range of skills. Some of these skills are technical, such as programming for data analysis; some are managerial, such as scoping and bidding projects; others are social, perhaps even political, such as the ability to say "No" to projects. In what follows, we describe a workshop series developed to build the workers' critical literacy and consciousness about their data work, specifically regarding the use of data in machine learning systems. After that, we describe a data project the workers questioned and resisted because they determined the work to be harmful. In that process, they demonstrated and enacted a critical consciousness towards data and machine learning. Reflecting on this enactment of data-focused critical consciousness, we identify themes that characterize a democratic workplace, describe the work of designing for organizational action and institutional relations, and discuss how worker and researcher positionality affects this work. In doing so, we argue for enabling workers to resist and refuse harmful data work and challenge the standard power structures of academic research and data work.
Characterising CSCW Research on Human-Robot Collaboration
Authors: Johansen Stine S. , Brophy Claire , Rittenbruch Markus , Donovan Jared W.
Published: April 2024

Human-Robot Collaboration (HRC) is an increasingly prominent topic in CSCW. From telepresence systems through field robots for extreme missions to social robots in homes, HRC is becoming a recurring theme across a broad range of CSCW research. Based on the growing interest, we explore the coupling of the field of CSCW and research on robotics at this critical time. This paper presents a primary and secondary literature review of CSCW proceedings and venues for robotics research. We identified 29 CSCW papers that contribute to the body of HRC research and analyse the foundations on which these papers rely. Then, we identified 138 papers published in robotics outlets that either (1) cite one or more CSCW papers, or (2) use the term CSCW. We discuss how CSCW is currently contributing to HRC research through five research topics: Sociomateriality, teamwork, awareness, embodiment, and communication. The paper contributes by offering three key future pathways for expanding this area of research, outlining questions and gaps that have yet to be explored for (1) expanding the application domains and diversifying robot types for HRC, (2) methodological implications for HRC, and (3) learning from human collaboration.
Embedding Democratic Values into Social Media AIs via Societal Objective Functions
Authors: Jia Chenyan , Lam Michelle S. , Mai Minh Chau , Hancock Jeffrey T. , Bernstein Michael S.
Published: April 2024

Mounting evidence indicates that the artificial intelligence (AI) systems that rank our social media feeds bear nontrivial responsibility for amplifying partisan animosity: negative thoughts, feelings, and behaviors toward political out-groups. Can we design these AIs to consider democratic values such as mitigating partisan animosity as part of their objective functions? We introduce a method for translating established, vetted social scientific constructs into AI objective functions, which we term societal objective functions, and demonstrate the method with application to the political science construct of anti-democratic attitudes. Traditionally, we have lacked observable outcomes to use to train such models-however, the social sciences have developed survey instruments and qualitative codebooks for these constructs, and their precision facilitates translation into detailed prompts for large language models. We apply this method to create a democratic attitude model that estimates the extent to which a social media post promotes anti-democratic attitudes, and test this democratic attitude model across three studies. In Study 1, we first test the attitudinal and behavioral effectiveness of the intervention among US partisans (N=1,380) by manually annotating (alpha=.895) social media posts with anti-democratic attitude scores and testing several feed ranking conditions based on these scores. Removal (d=.20) and downranking feeds (d=.25) reduced participants' partisan animosity without compromising their experience and engagement. In Study 2, we scale up the manual labels by creating the democratic attitude model, finding strong agreement with manual labels (rho=.75). Finally, in Study 3, we replicate Study 1 using the democratic attitude model instead of manual labels to test its attitudinal and behavioral impact (N=558), and again find that the feed downranking using the societal objective function reduced partisan animosity (d=.25). This method presents a novel strategy to draw on social science theory and methods to mitigate societal harms in social media AIs.
Evaluating the Use of Hypothetical 'Would You Rather' Scenarios to Discuss Privacy and Security Concepts with Children
Authors: Blinder Elana B. , Chetty Marshini , Vitak Jessica , Torok Zoe , Fessehazion Salina , Yip Jason , Fails Jerry Alan , Bonsignore Elizabeth , Clegg Tamara
Published: April 2024

Children are exposed to technology at home and school at very young ages, often using family mobile devices and educational apps. It is therefore critical that they begin learning about privacy and security concepts during their elementary school years, rather than waiting until they are older. Such skills will help children navigate an increasingly connected world and develop agency over their personal data, online interactions, and online security. In this paper, we explore how a simple technique---a ''Would Your Rather'' (WYR) game involving hypothetical privacy and security scenarios---can support children in working through the nuances of these types of situations and how educators can leverage this approach to support children's privacy and security learning. We conducted three focus groups with 21 children aged 7-12 using the WYR activity and interviewed 13 elementary school teachers about the use of WYR for facilitating privacy and security learning. We found that WYR provided a meaningful opportunity for children to assess privacy and security risks, consider some of the social and emotional aspects of privacy and security dilemmas, and assert their agency in a manner typically unavailable to children in an adult-centric society. Teachers highlighted connections between privacy and security dilemmas and children's social and emotional learning and offered additional insights about using this WYR technique in and beyond their classrooms. Based on these findings, we highlight four opportunities for using WYR to support children in engaging with privacy and security concepts from an early age.
Fighting for Their Voice: Understanding Indian Muslim Women's Responses to Networked Harassment
Authors: Bhimdiwala Ayesha , Adavi Krishna Akhil Kumar , Arif Ahmer
Published: April 2024

Social computing researchers have described how online harassment is taking on more sophisticated forms where communities rather than lone individuals carry out attacks. At the same time, those who are targeted are coming together with members of the online crowd to confront this networked harassment. However, we currently lack empirical studies of how such self-organizing efforts take place, especially in non-western contexts like India. To address this gap, this paper examines two case studies of gendered Islamophobia, Sulli Deals and Bulli Bai where a group of Indian Muslim women were targeted via fake auctions hosted on GitHub, that were subsequently amplified over social media. We conducted 12 in-depth interviews with the women targeted in these incidents to understand how these women collectively made sense of these incidents, and the social and spiritual resources they drew upon as they constructed a response together. Our findings describe how our participants understood these incidents (e.g. with families and each other); the work they did to combat the harassment (e.g. coordinating a unified media response); and how religious beliefs and spiritual practices played into this (e.g. values around courage, patience, flexibility, and humility). We conclude by discussing how these findings can refresh our understanding of designing community-driven responses to networked harassment.
Friendship Formation in an Enforced Online Regime: Findings from a U.S. University Under COVID
Authors: Lee Soyoung , Toyama Kentaro
Published: April 2024

Friendships are a key element of mental health, yet modern life increasingly involves "enforced online regimes," which can inhibit friendship formation. One example is provided by residential university students under COVID-19. Through interviews with 17 graduate students at a U.S. university, we investigate how new friendships were made and maintained under the pandemic. While some of our individual findings echo previous work with online social interaction, our analysis reveals a novel 7-phase friendship formation process that extends Levinger & Snoek's classic pair-relatedness theory. The model enables pinpoint diagnoses. For our participants, three specific phases were blocked -- Physical Awareness (apprehension of another's physical characteristics); Personal Contact (exchange of personal information); and Ongoing Mutuality (repeat interactions to build friendship). The model also explains divergent results under similar but different situations (e.g., residential students under COVID eventually made friends, but students of purely online courses do not), and enables targeted recommendations.
Is the Same Performance Really the Same?: Understanding How Listeners Perceive ASR Results Differently According to the Speaker's Accent
Authors: Kim Seoyoung , Park Yeon Su , Ahn Dakyeom , Kwak Jin Myung , Kim Juho
Published: April 2024

Research suggests that automatic speech recognition (ASR) systems, which automatically convert speech to text, show different performances according to various input classes (e.g., accent, age), requiring attention to building fairer AI systems that would perform similarly across various input classes. However, would an AI system with the same performance regardless of input classes really be perceived as fair enough? To this end, we investigate how listeners perceive the ASR system of the same result differently according to whether the speaker is a native speaker (NS) or a non-native speaker (NNS), which may lead to unfair situations. We conducted a study (n = 420), where participants were given one of the ten speech recordings with various accents of the same script along with the same captions. We found that even with the same ASR output, listeners perceive the ASR results differently. They found captions to be more useful for NNS's speech and blamed NNS more for the errors than NS. Based on the findings, we present design implications suggesting that we should take a step further than just achieving the same performance across various input classes to build a fair ASR system.
Mindful Explanations: Prevalence and Impact of Mind Attribution in XAI Research
Authors: Hindennach Susanne , Shi Lei , Miletić Filip , Bulling Andreas
Published: April 2024

When users perceive AI systems as mindful, independent agents, they hold them responsible instead of the AI experts who created and designed these systems. So far, it has not been studied whether explanations support this shift in responsibility through the use of mind-attributing verbs like "to think". To better understand the prevalence of mind-attributing explanations we analyse AI explanations in 3,533 explainable AI (XAI) research articles from the Semantic Scholar Open Research Corpus (S2ORC). Using methods from semantic shift detection, we identify three dominant types of mind attribution: (1) metaphorical (e.g. "to learn" or "to predict"), (2) awareness (e.g. "to consider"), and (3) agency (e.g. "to make decisions"). We then analyse the impact of mind-attributing explanations on awareness and responsibility in a vignette-based experiment with 199 participants. We find that participants who were given a mind-attributing explanation were more likely to rate the AI system as aware of the harm it caused. Moreover, the mind-attributing explanation had a responsibility-concealing effect: Considering the AI experts' involvement lead to reduced ratings of AI responsibility for participants who were given a non-mind-attributing or no explanation. In contrast, participants who read the mind-attributing explanation still held the AI system responsible despite considering the AI experts' involvement. Taken together, our work underlines the need to carefully phrase explanations about AI systems in scientific writing to reduce mind attribution and clearly communicate human responsibility.
'Not Just a Computer Screen:' Distributed Attunement in Virtual School Environments
Authors: Jonas Anne
Published: April 2024

Many digital platforms pit consumers or customers and service workers against one another or render human work invisible, concealing human labor for profit and the charismatic allure of automation. When schooling is digitized, what transformations occur to the relationships between teachers, parents, and students, and with what consequences? In pre-pandemic virtual and hybrid schooling, I find that teachers developed alternative strategies to practice caring responsiveness in online environments of low visibility through a process I call "distributed attunement.'' These strategies entailed building holistic relationships, collaborating with student families and fellow teachers, and interpreting student experiences using a combination of platform data and social information. Rather than giving in to digitization as a vehicle for removing or obscuring care work, this study illustrates that teachers centered relationships as core to the virtual schooling experience. The distributed attunement teachers in virtual schools created indicates the impossibility of separating technical and affective labor in a pedagogical context and provide promising sites to develop supports for high-quality online education. Further, teachers' navigation of limited visibility suggests tactics for CSCW researchers concerned with documenting invisible labor in the development of digital systems. Nevertheless, these strategies can be appropriated or undermined by employers in ways that threaten to diminish their efficacy, requiring ongoing advocacy to push for flourishing online schooling options.
"Not my Priority:" Ethics and the Boundaries of Computer Science Identities in Undergraduate CS Education
Authors: Darling-Wolf Hana , Patitsas Elizabeth
Published: April 2024

Researchers in the CSCW community have long problematized the separation of social and ethical considerations from design work. Despite increasing attention to tech ethics and ethics education, however, computer scientists' sense of ethical responsibility remains of concern. This paper offers insights on how this boundary between tech and ethics is maintained and reinforced for students as they develop their identities as computer scientists. Drawing on interviews with eight undergraduate computer science (CS) students at McGill University, we explore the role that ethics play in the legitimate peripheral participation of students inside and outside their formal education. We found that while individual opinions on the importance of ethics varied, students agreed that ethics are not valued or rewarded in their education, extracurriculars, or future work prospects. We describe how placing ethics outside the boundary of computing acts as a form of occupational closure, excluding both important multidisciplinary work and marginalized bodies. We argue that in order to promote ethical practice in the design of CSCW systems, we must make it in the interest of future designers to learn socially grounded ethics. This requires that designers, researchers, and future employers actively reshape the boundaries of computing by asserting social and ethical considerations as values of computing and design.
Stoking the Flames: Understanding Escalation in an Online Harassment Community
Authors: Take Kejsi , Zhong Victoria , Geeng Chris , Bevensee Emmi , McCoy Damon , Greenstadt Rachel
Published: April 2024

Online harassment remains a prevalent problem for internet users. Its impact is made orders of magnitude worse when multiple harassers coordinate to conduct networked attacks. This paper presents an analysis of 231 threads in Kiwi Farms, a notorious online harassment community. We find that networked online harassment campaigns consists of three phases: target introduction, network decision, and network response. The first stage consists of the initial narrative elements, that are approved or not in stage two and expanded in stage three. Narrative building is a common element of all three stages. The network plays a key role in narrative building, adding elements to the narrative in at least 80 % of the threads, resulting in sustained harassment. This finding is central to our model of Continuous Narrative Escalation (CNE), that has two parts: (1) narrative continuation, the action of repeatedly adding new information to the existing narrative and (2) escalation, the aggravation of harassment that occurs as a consequence.

In addition, we present insights from our analysis of 100 takedown requests threads, discussing received abuse reports. We find that these takedown requests are misused by the community and are used as elements to further fuel the narrative. We use our findings and framework to come up with a set of recommendations, that can inform harassment interventions and make online spaces safer.
The Effect of Individual Traits on Emerging Roles in Synchronous Computer-Mediated Groups
Authors: Vatnani Vishal B. , Barlow Jordan B.
Published: April 2024

This study examines group dynamics in a virtual decision-making context through the lens of role theory and speech act theory, furthering previous research on the roles that emerge in such settings. Specifically, this research uses communication transcripts and text analysis to complete two main research objectives. First, we extend prior research by exploring how individual traits of group members (specifically, personality, cognitive ability, and social sensitivity) are related to emerging roles in computer-mediated communication. Personal characteristics such as agreeableness, social sensitivity, and motivation were significant predictors of role emergence in online decision-making groups. Agreeableness predicted the emergence of the listener role, social sensitivity predicted the emergence of the manager role, and motivation predicted the emergence of the sharer role. Second, we answer the call for more replication research and further validate the results from previous research by measuring roles assumed by individuals working synchronously online; we highlight the findings that are confirmed across previous research and our current study.
The State of Pilot Study Reporting in Crowdsourcing: A Reflection on Best Practices and Guidelines
Authors: Oppenlaender Jonas , Abbas Tahir , Gadiraju Ujwal
Published: April 2024

Pilot studies are an essential cornerstone of the design of crowdsourcing campaigns, yet they are often only mentioned in passing in the scholarly literature. A lack of details surrounding pilot studies in crowdsourcing research hinders the replication of studies and the reproduction of findings, stalling potential scientific advances. We conducted a systematic literature review on the current state of pilot study reporting at the intersection of crowdsourcing and HCI research. Our review of ten years of literature included 171 articles published in the proceedings of the Conference on Human Computation and Crowdsourcing (AAAI HCOMP) and the ACM Digital Library. We found that pilot studies in crowdsourcing research (i.e., crowd pilot studies) are often under-reported in the literature. Important details, such as the number of workers and rewards to workers, are often not reported. On the basis of our findings, we reflect on the current state of practice and formulate a set of best practice guidelines for reporting crowd pilot studies in crowdsourcing research. We also provide implications for the design of crowdsourcing platforms and make practical suggestions for supporting crowd pilot study reporting.
The Subversive AI Acceptance Scale (SAIA-8): A Scale to Measure User Acceptance of AI-Generated, Privacy-Enhancing Image Modifications
Authors: Logas Jacob , Garg Poojita , Arriaga Rosa I. , Das Sauvik
Published: April 2024

To resist government and corporate use of facial recognition to surveil users through their personal images, researchers have created privacy-enhancing image filters that use adversarial machine learning. These "subversive AI'' (SAI) image filters aim to defend users from facial recognition by distorting personal images in ways that are barely noticeable to humans but confusing to computer vision algorithms. SAI filters are limited, however, by the lack of rigorous user evaluation that assess their acceptability. We addressed this limitation by creating and validating a scale to measure user acceptance --- the SAIA-8. In a three-step process, we apply a mixed-methods approach that closely adhered to best practices for scale creation and validation in measurement theory. Initially, to understand the factors that influence user acceptance of SAI filter outputs, we interviewed 15 participants. Interviewees disliked extant SAI filter outputs because of a perceived lack of usefulness and conflicts with their desired self-presentation. Using insights and statements from the interviews, we generated 106 potential items for the scale. Employing an iterative refinement and validation process with 245 participants from Prolific, we arrived at the SAIA-8 scale: a set of eight items that capture user acceptability of privacy-enhancing perturbations to personal images, and that can aid in benchmarking and prioritizing user acceptability when developing and evaluating new SAI filters.
Understanding is a Two-Way Street: User-Initiated Repair on Agent Responses and Hearing in Conversational Interfaces
Authors: Moore Robert J. , An Sungeun , Marrese Olivia H.
Published: April 2024

Although methods for repairing prior turns in natural conversation are critical for enabling mutual understanding, or successful communication, these methods are seldom built into conversational user interfaces systematically. Chatbots and voice assistants tend to ask users to paraphrase what they said if it was not understood, but users cannot do the same if they encounter trouble in understanding what the agent said. Understanding is a one-way street in most (intent-based) conversation-like interfaces. An exception to this is Moore and Arar (2019), who demonstrate nine types of user-initiated repair on agent responses that are common in natural conversation and who have shown that users will employ these repair features correctly in text-based interfaces if taught. In this small-scale study, we test these user-initiated repairs (in second position) in a voice-based interface. With understanding-oriented repairs, we found that participants employed them much the same way in text and voice. In addition, we examine some hearing- and speaking-oriented repairs that emerged from the use of our novel multi-modal interface. We found that participants used them to manage troubles specific to the voice modality. Analysis of user logs and transcripts suggests that user-initiated repair features are valuable components of conversational interfaces.
Fulfilling OCaml Modules with Transparency
Authors: Clement Blaudeau , Rémy Didier , Radanne Gabriel
Published: April 2024

ML modules come as an additional layer on top of a core language to offer
large-scale notions of composition and abstraction. They largely
contributed to the success of OCaml and SML. While modules are easy to write
for common cases, their advanced use may become tricky. Additionally,
despite a long line of works, their meta-theory remains difficult to
comprehend, with involved soundness proofs. In fact, the module layer of
OCaml does not currently have a formal specification and its implementation
has some surprising behaviors.

Building on previous translations from ML modules to Fω, we propose a type
system, called Mω, that covers a large subset of OCaml modules, including
both applicative and generative functors, and extended with transparent
ascription. This system produces signatures in an OCaml-like syntax extended
with Fω quantifiers. We provide a reverse translation from Mω signatures to
path-based source signatures along with a characterization of signature
avoidance cases, making Mω signatures well suited to serve as a new internal
representation for a typechecker.

The soundness of the type system is shown by elaboration in Fω. We improve
over previous encodings of sealing within applicative functors, by the
introduction of transparent existential types, a weaker form of existential
types that can be lifted out of universal and arrow types. This shines a new
light on the form of abstraction provided by applicative functors and brings
their treatment much closer to those of generative functors.
Compiling Recurrences over Dense and Sparse Arrays
Authors: Sundram Shiv , Tariq Muhammad Usman , Kjolstad Fredrik
Published: April 2024

We present a framework for compiling recurrence equations into native code. In our framework, users specify a system of recurrences, the types of data structures that store inputs and outputs, and scheduling commands for optimization. Our compiler then lowers these specifications into native code that respects the dependencies in the recurrence equations. Our compiler can generate code over both sparse and dense data structures, and determines if the recurrence system is solvable with the provided scheduling primitives. We evaluate the performance and correctness of the generated code on several recurrences, from domains as diverse as dense and sparse matrix solvers, dynamic programming, graph problems, and sparse tensor algebra. We demonstrate that the generated code has competitive performance to hand-optimized implementations in libraries. However, these handwritten libraries target specific recurrences, specific data structures, and specific optimizations. Our system, on the other hand, automatically generates implementations from recurrences, data formats, and schedules, giving our system more generality than library approaches.
Newtonian Program Analysis of Probabilistic Programs
Authors: Wang Di , Reps Thomas
Published: April 2024

Due to their quantitative nature, probabilistic programs pose non-trivial challenges for designing compositional and efficient program analyses. Many analyses for probabilistic programs rely on iterative approximation. This article presents an interprocedural dataflow-analysis framework, called NPA-PMA, for designing and implementing (partially) non-iterative program analyses of probabilistic programs with unstructured control-flow, nondeterminism, and general recursion. NPA-PMA is based on Newtonian Program Analysis (NPA), a generalization of Newton's method to solve equation systems over semirings. The key challenge for developing NPA-PMA is to handle multiple kinds of confluences in both the algebraic structures that specify analyses and the equation systems that encode control flow: semirings support a single confluence operation, whereas NPA-PMA involves three confluence operations (conditional, probabilistic, and nondeterministic).

Our work introduces ω-continuous pre-Markov algebras (ωPMAs) to factor out common parts of different analyses; adopts regular infinite-tree expressions to encode probabilistic programs with unstructured control-flow; and presents a linearization method that makes Newton's method applicable to the setting of regular-infinite-tree equations over ωPMAs. NPA-PMA allows analyses to supply a non-iterative strategy to solve linearized equations. Our experimental evaluation demonstrates that (i) NPA-PMA holds considerable promise for outperforming Kleene iteration, and (ii) provides great generality for designing program analyses.
Identifying and Correcting Programming Language Behavior Misconceptions
Authors: Lu Kuang-Chen , Krishnamurthi Shriram
Published: April 2024

Misconceptions about core linguistic concepts like mutable
variables, mutable compound data, and their interaction with scope
and higher-order functions seem to be widespread.
But
how do we detect
them, given that experts have blind spots and may not realize the
myriad ways in which students can misunderstand programs?
Furthermore, once identified, what can we do to correct them?

In this paper, we present a curated list of misconceptions, and an
instrument to detect them. These are distilled from student work
over several years and match and extend prior research. We also
present an automated, self-guided tutoring system. The tutor builds
on strategies in the education literature and is explicitly designed
around identifying and correcting misconceptions.

We have tested the tutor in multiple settings.
Our data consistently show that (a) the misconceptions we tackle are
widespread, and (b) the tutor appears to improve understanding.
CYCLE: Learning to Self-Refine the Code Generation
Authors: Ding Yangruibo , Min Marcus J. , Kaiser Gail , Ray Baishakhi
Published: April 2024

Pre-trained code language models have achieved promising performance in code generation and improved the programming efficiency of human developers. However, their self-refinement capability is typically overlooked by the existing evaluations of code LMs, which focus only on the accuracy of the one-time prediction. For the cases when code LMs fail to implement the correct program, developers actually find it hard to debug and fix the faulty prediction since it is not written by the developers themselves. Unfortunately, our study reveals that code LMs cannot efficiently self-refine their faulty generations as well.

In this paper, we propose CYCLE framework, learning to self-refine the faulty generation according to the available feedback, such as the execution results reported by the test suites. We evaluate CYCLE on three popular code generation benchmarks, HumanEval, MBPP, and APPS. The results reveal that CYCLE successfully maintains, sometimes improves, the quality of one-time code generation, while significantly improving the self-refinement capability of code LMs. We implement four variants of CYCLE with varied numbers of parameters across 350M, 1B, 2B, and 3B, and the experiments show that CYCLE consistently boosts the code generation performance, by up to 63.5
Evaluating the Effectiveness of Deep Learning Models for Foundational Program Analysis Tasks
Authors: Chen Qian , Yu Chenyang , Liu Ruyan , Zhang Chi , Wang Yu , Wang Ke , Su Ting , Wang Linzhang
Published: April 2024

While deep neural networks provide state-of-the-art solutions to a wide range of programming language tasks, their effectiveness in dealing with foundational program analysis tasks remains under explored. In this paper, we present an empirical study that evaluates four prominent models of code (i.e., CuBERT, CodeBERT, GGNN, and Graph Sandwiches) in two such foundational tasks: (1) alias prediction, in which models predict whether two pointers must alias, may alias or must not alias; and (2) equivalence prediction, in which models predict whether or not two programs are semantically equivalent. At the core of this study is CodeSem, a dataset built upon the source code of real-world flagship software (e.g., Linux Kernel, GCC, MySQL) and manually validated for the two prediction tasks.
Results show that all models are accurate in both prediction tasks, especially CuBERT with an accuracy of 89% and 84% in alias prediction and equivalence prediction, respectively. We also conduct a comprehensive, in-depth analysis of the results of all models in both tasks, concluding that deep learning models are generally capable of performing foundational tasks in program analysis even though in specific cases their weaknesses are also evident.

Our code and evaluation data are publicly available at https://github.com/CodeSemDataset/CodeSem.