bluuebunny/crossref_metadata_2025 huggingface dataset:
Social Media Breaks: An Opportunity for Recovery and Procrastination
Authors: Hossain Elahi , Wadley Greg , Berthouze Nadia , Cox Anna L.
Published: April 2024

Social media (SM) breaks from studying can either support students' wellbeing and performance by acting as a recovery behaviour or subvert it by acting as a procrastination behaviour. It is currently unclear which influences lead an SM break to be a positive recovery vs. negative procrastination behaviour. A behavioural and emotion regulation (ER) perspective may help to elucidate these influences. In this paper, we report a semi-structured interview study with 20 undergraduates to explore their experiences of SM breaks when studying. Our analysis describes how motivational and environmental factors can influence a break's propensity for recovery or procrastination during the break initiation and execution phases. We apply an ER perspective to these reports and demonstrate how it helps to explain further in which circumstances SM breaks are likely to support recovery or procrastination. Based on this analysis, we present recommendations for designing interventions to support healthy breaks and reduce unhealthy ones.
Socio-digital Rural Resilience: An Exploration of Information Infrastructures Within and Across Rural Villages During Covid-19
Authors: Johnson Ian G. , Vlachokyriakos Vasilis
Published: April 2024

Access to the internet and digital technology are reported determinants and enablers of social capital and community resilience. However, despite rural areas facing ecological, social, and economic uncertainties across the world, communication technologies and the design of civic technologies in HCI are not designed with the rural in mind. In this paper, we explore information infrastructures within and across rural villages in the UK focusing on the interplay between human, social, and technical infrastructures. We report on the existing communication practices, underlining the impacts of transitioning to digital communication technologies during the COVID-19 pandemic. Our findings point to the fragility of information infrastructure, the "civic duty" of bridging information and skills gaps, and the role of technology as a threat to social norms and disrupter of the 'politics of the rural'. The work presented here has implications for Rural HCI researchers and designers interested in design from the rural.
Swapping 5G for 3G: Motivations, Experiences, and Implications of Contemporary Dumbphone Adoption
Authors: Rothschild Annabel , Lindqvist Janne
Published: April 2024

In highly developed countries where smartphones are both accessible and expected, why are some individuals still choosing to use dumbphones? Dumbphones, as an anachronistic (or, outdated) technology are an unusual choice when many government systems, business services, and interpersonal relationships make use of the diverse communication methods presented by smartphones. However, dumbphones are increasingly (re)adopted by individuals seeking, among other motivations, a low-distraction digital handset. We investigate the phenomenon of designer dumbphones, or newly developed dumbphones redesigned to meet the needs of dumbphone users, despite dumbphone-unfriendly current technical infrastructural. We report on the results of interviews with eight traditional dumbphone users, five designer dumbphone users, and two designer dumbphone developers. Our findings highlight both the impact of the digital disconnection movement and dumbphones as tools for mental and physical health, practicing religious devotion, and enacting political disaffiliation. Our analysis takes into account experiences of isolation during the COVID-19 pandemic, and the kinds of privilege needed to choose digital disconnection, along with the interpersonal complications that result from doing so. This work contributes to conversations around volitional technical (non)use and disputes the notion that increased communication leads to richer interpersonal interaction. As dumbphone (re)adoption begins to trend in popular media, our goal is to uncover potential sites of digital disconnection and understand how different groups of individuals might experience those sites.
Targets of Weaponized Islamophobia: The Impact of Misinformation on the Online Practices of Muslims in the United States
Authors: Khan Sadia O. , Ghafourian Tania , Patil Sameer
Published: April 2024

Online misinformation about Islam and Muslims has increasingly become a weapon in the arsenal of Islamophobia. Such content typically aims to marginalize and disempower Muslims and sow animosity between them and non-Muslims. However, there has not yet been an investigation of the impact of such targeted misinformation on the online practices of Muslims. Through semi-structured interviews with 19 Muslim participants from diverse backgrounds, we sought to understand how Muslims in the United States navigate and use the very online spaces that are leveraged to spread misinformation targeted at them. Our findings provide a nuanced understanding of how targeted misinformation subjects Muslims to misperceptions from non-Muslims and has the harmful effect of leaving Muslims exhausted and disempowered by the futility of their attempts to correct the misperceptions. In addition, we identify forces that drive Muslims to act or retreat in their online practices in response to being targeted by misinformation. These findings can be useful to industry, civil society organizations, and policymakers in order to curb injustices related to Islamic misinformation.
Ten Seconds Can Last Longer: Prevalence, Impact, and User Perceptions of Food Cues on Snapchat
Authors: Kurdi Maram , Albadi Nuha , Mishra Shivakant
Published: April 2024

Obesity is a global health crisis projected to affect one billion people worldwide by 2030. Previous research has emphasized the role of food cues in print media and television as contributing factors to the obesity epidemic. However, the influence of food cues shared by general users on social media platforms, particularly Snapchat, remains largely unexplored. To this end, this paper presents a comprehensive, large-scale study employing a multi-method approach to assess, measure, and mitigate the influence of food cues shared on Snapchat in three different countries with diverse cultural backgrounds: Saudi Arabia, the United States, and France. Our analysis of over 350K collected snaps reveals that food cues are prevalent among Snapchat users, with food content comprising approximately 20% of all collected snaps. Subsequently, we assessed the impact of exposure to Snapchat food content on appetite and examined whether it might inadvertently exacerbate cravings. Our experimental study, involving 37 participants, yields a significant finding: exposure to food snaps leads to a substantial increase in caloric intake. In light of these findings and as an effort to mitigate this impact, this paper sheds light on Snapchat users' perceptions of a proposed intervention design idea, which would allow them to customize their feed and hide food content to potentially reduce their exposure to it. According to our survey of 813 Snapchat users, the majority (57%) indicated they would be willing to hide food-related content from their feeds. In particular, being male and having a high body mass index (BMI) were both associated with a higher willingness to block food content from their Snapchat feed. While food snaps are intended to disappear after 24 hours, our results suggest that their impact can have a long-lasting effect on health and wellness.
"The Headline Was So Wild That I Had To Check": An Exploration of Women's Encounters With Health Misinformation on Social Media
Authors: Malki Lisa Mekioussa , Patel Dilisha , Singh Aneesha
Published: April 2024

Misinformation has emerged as a significant threat to public health in recent years and has been observed across numerous health issues, the most prolific being COVID-19. Though increasing attention has been paid to women's health within the social scientific and HCI communities, very little research has holistically explored the unique challenges women face when navigating health misinformation. To address this gap, we conducted a qualitative diary and interview study aimed at investigating women's perceptions and lived experiences of health misinformation on social media, and how they respond emotionally and behaviourally to health misinformation encountered in their day-to-day lives. We found that participants perceived health misinformation as ubiquitous and poorly-managed by social media platforms, resulting in a lack of trust in current moderation and fact-checking interventions. We also observed that encounters with misinformation triggered negative emotional responses, which participants attempted to navigate through ad-hoc strategies such as drawing on personal experience and reading social media comment sections, which facilitated collective sensemaking. We discuss our findings in relation to the design of targeted interventions which empower women to engage constructively with health information on social media. In particular, we underscore the importance of trust, accountability, and intersectionality in future design and research practice, and encourage a holistic view of how women are impacted by misinformation.
The Online Identity Help Center: Designing and Developing a Content Moderation Policy Resource for Marginalized Social Media Users
Authors: Mayworm Samuel , Li Shannon , Thach Hibby , Delmonaco Daniel , Paneda Christian , Wegner Andrea , Haimson Oliver L.
Published: April 2024

Marginalized social media users struggle to navigate inequitable content moderation they experience online. We developed the Online Identity Help Center (OIHC) to confront this challenge by providing information on social media users' rights, summarizing platforms' policies, and providing instructions to appeal moderation decisions. We discuss our findings from interviews (n = 24) and surveys (n = 75) which informed the OIHC's design, along with interviews about and usability tests of the site (n = 12). We found that the OIHC's resources made it easier for participants to understand platforms' policies and access appeal resources. Participants expressed increased willingness to read platforms' policies after reading the OIHC's summarized versions, but expressed mistrust of platforms after reading them. We discuss the study's implications, such as the benefits of providing summarized policies to encourage digital literacy, and how doing so may enable users to express skepticism of platforms' policies after reading them.
"This Chatbot Would Never...": Perceived Moral Agency of Mental Health Chatbots
Authors: Wester Joel , Pohl Henning , Hosio Simo , van Berkel Niels
Published: April 2024

Despite repeated reports of socially inappropriate and dangerous chatbot behaviour, chatbots are increasingly used as mental health services in providing support for young people. In sensitive settings as such, the notion of perceived moral agency (PMA) is crucial, given its critical role in human-human interactions. In this paper, we investigate the role of PMA in human-chatbot interactions. Specifically, we seek to understand how PMA influence the perception of trust, likeability, and perceived safety of chatbots for mental health across two distinct age groups. We conduct an online experiment(N = 279)to evaluate chatbots with low and high PMA as targeted towards teenagers and adults. Our results indicate increased trust, likeability, and perceived safety in mental health chatbots displaying high PMA. A qualitative analysis revealed four themes, assessing participants' expectations of mental health chatbots in general, as well as targeted towards teenagers: Anthropomorphism, Warmth, Sensitivity, and Appearance manifestation. We show that PMA plays a crucial role in influencing the perceptions of chatbots and provide recommendations for designing socially appropriate mental health chatbots.
Toward Trauma-Informed Research Practices with Youth in HCI: Caring for Participants and Research Assistants When Studying Sensitive Topics
Authors: Razi Afsaneh , Seberger John S. , Alsoubai Ashwaq , Naher Nurun , De Choudhury Munmun , Wisniewski Pamela J.
Published: April 2024

Research involving sensitive data often leads to valuable human-centered insights. Yet, the effects of participating in and conducting research about sensitive data with youth are poorly understood. We conducted meta-level research to improve our understanding of these effects. We did the following: (i) asked youth (aged 13-21) to share their private Instagram Direct Messages (DMs) and flag their unsafe DMs; (ii) interviewed 30 participants about the experience of reflecting on this sensitive data; (iii) interviewed research assistants (RAs, n=12) about their experience analyzing youth's data. We found that reflecting about DMs brought discomfort for participants and RAs, although both benefited from increasing their awareness about online risks, their behavior, and privacy and social media practices. Participants had high expectations for safeguarding their private data while their concerns were mitigated by the potential to improve online safety. We provide implications for ethical research practices and the development of reflective practices among participants and RAs through applying trauma-informed principles to HCI research.
Understanding The Needs of Mentoring in Surgery to Guide the Design of Surgical Telementoring Systems
Authors: Lambert Solène , Voros Sandrine , Troccaz Jocelyne , Canlorbe Geoffroy , Avellino Ignacio
Published: April 2024

All along their careers, surgeons learn through mentoring, operating on patients side-by-side with expert surgeons. However, access to experts is more and more challenging as they are often located far from learners. Telementoring can increase the reach of mentors by supporting mentoring at a distance, but despite their potential, current systems remain rarely institutionalized. We investigate current surgical mentoring practices, to inform future design of telementoring systems in providing support for learning. In a field study, we observe 11 surgeries and conduct follow-up self-confrontation interviews with mentor-mentee pairs. Through Thematic Analysis, we find that the domain of surgery results in mentee's needs for reassurance regarding safety, being spared from workload and risk, both essential to support the acquisition of skills. Second, that mentors understand the situation primarily through the surgical site view but also through inspecting the mentee's posture and the patient's body. Thirdly, that mentors take physical distance as their presence limits mentees' autonomy development. We discuss the limits of current surgical telementoring systems and rethink their role as companions of onsite mentoring.
Unfulfilled Promises of Child Safety and Privacy: Portrayals and Use of Children in Smart Home Marketing
Authors: Sun Kaiwen , Li Jingjie , Zou Yixin , Radesky Jenny , Brooks Christopher , Schaub Florian
Published: April 2024

Smart home technologies are making their way into families. Parents' and children's shared use of smart home technologies has received growing attention in CSCW and related research communities. Families and children are also frequently featured as target audiences in smart home product marketing. However, there is limited knowledge of how exactly children and family interactions are portrayed in smart home product marketing, and to what extent those portrayals align with the actual consideration of children and families in product features and resources for child safety and privacy. We conducted a content analysis of product websites and online resources of 102 smart home products, as these materials constitute a main marketing channel and information source about products for consumers. We found that despite featuring children in smart home marketing, most analyzed product websites did not mention child safety features and lacked sufficient information on how children's data is collected and used. Specifically, our findings highlight misalignments in three aspects: (1) children are depicted as users of smart home products but there are insufficient child-friendly product features; (2) harmonious child-product co-presence is portrayed but potential child safety issues are neglected; and (3) children are shown as the subject of monitoring and datafication but there is limited information on child data collection and use. We discuss how parent-child relationships and parenting may be negatively impacted by such marketing depictions, and we provide design and policy recommendations for better incorporating child safety and privacy considerations into smart home products.
Using Co-Design with Streamers and Viewers to Identify Values and Resolve Tensions in the Design of Interpersonal Wearable Telepresence Systems
Authors: Pfeil Kevin P. , Badillo-Urquiola Karla , Belga Jacob , Sera-Josef Jose-Valentin T. , Laviola Joseph J. , Wisniewski Pamela J.
Published: April 2024

We conducted a co-design study with 26 users in dyadic groups who were assigned to the opposing roles of "Streamers'' and "Viewers'' to design interactive, wearable telepresence prototypes for interpersonal use. The goal was to elicit values, identify value tensions, and resolve these tensions in the design of a system that allows Streamers to share their live experiences with a remote Viewer. Leveraging the lens of value-sensitive design (VSD), we found that these different stakeholders prioritized different values in design, but possibly due to our prompt to design for someone they cared about/knew, often accounted for one another's needs in their solutions to arrive at designs that were affordable, unobtrusive, and socially acceptable for the Streamer, while giving the Viewer a sense of autonomy. Our work highlights the strengths of co-design when eliciting important human values in the design of sociotechnical systems for wearable telepresence, reconciling value tensions, and conceptualizing novel hardware- and software-based solutions for enhancing the interpersonal telepresence experience of both viewers and streamers. A key insight from our study is that no single system will meet all users' needs; therefore, we should move towards building customizable toolkits to account for differing values and needs.
Using Sensor-Captured Patient-Generated Data to Support Clinical Decision-making in PTSD Therapy
Authors: Evans Hayley I. , Ryu Myeonghan , Hsieh Theresa , Zhou Jiawei , Xu Kefan , Akers Kenneth W. , Sherrill Andrew M. , Arriaga Rosa I.
Published: April 2024

Today, clinicians have limited visibility into the quality of homework exercises that occur outside of the clinical context; however, understanding patient performance in these exercises is essential for guiding patient-centered care. To address this, we present the Clinician Homework Review (CHR), a unique measure and interface that displays similarity ratings calculated using sensor-captured patient-generated data (sPGD; i.e. heart rate, phone usage, ambient noise, and physical activity) for therapeutic exercises outside of the clinical setting within the post-traumatic stress disorder (PTSD) treatment context. Through concept testing sessions with 10 clinicians, we examine how sPGD can be leveraged to measure and investigate what contributes to patient performance in a therapeutic exercise. We also share in-depth information regarding clinician interpretation and planned use of data displayed by CHR in clinical sessions with patients. We frame our results in the context of situated objectivity and propose the notion of "perceived reference weight," which describes the significance attributed to contextualized data. In doing so, we support clinical decision-making in PTSD therapy.
Wage Theft and Technology in the Home Care Context
Authors: Ming Joy , Gong Dana , Ngai Chit Sum Eunice , Sterling Madeline , Vashistha Aditya , Dell Nicola
Published: April 2024

Home care workers (HCWs) are professionals who provide care to older adults and people with disabilities at home. However, HCWs are vulnerable and especially susceptible to wage theft, or not being paid their legally-entitled wages in full by their employers. Prior work has examined other low-wage work settings to show how technology is designed and deployed has the potential to both cause and address wage theft. We extend this work by examining the relationship between technology and wage theft in the home care context. We collaborated closely with a local grassroots organization to conduct interviews with workers and labor, legal, and payroll experts. We uncovered how the complex, volatile, and diverse nature of home care complicates the errors in time-tracking systems. Through design provocations and focus groups with workers and experts, we also investigated the potential of technology as a part of broader efforts to curb wage theft through educating and empowering isolated HCWs. While we found that approachable design could reduce errors in existing systems, make employer processes more transparent, and help workers exchange knowledge to build collective power, we also discuss concerns around burden, privacy, and accountability when designing technologies for HCWs and other low-wage workers.
Collective Privacy Sensemaking on Social Media about Period and Fertility Tracking post Roe v. Wade
Authors: Song Qiurong , Ma Renkai , Kou Yubo , Gui Xinning
Published: April 2024

On June 24, 2022, the U.S. Supreme Court overturned Roe v. Wade, which has led to full bans on most abortions in 14 states within one year. Many people in the U.S. use period and fertility tracking apps for reproductive healthcare and concerns have arisen about the privacy risks these apps might pose in the wake of Roe reversal. Existing literature on privacy risks of period and fertility tracking apps has primarily examined the privacy policies and practices of these apps. However, how users make sense of the privacy risks of these apps, especially in the post-Roe time, remains understudied. This study explores collective privacy sensemaking on social media, a practice in which people collectively make sense of a privacy situation. Our findings reveal how people contextualize privacy issues, speculate about the associated risks, as well as explore risk mitigation strategies. We conclude with privacy design implications for privacy design in period and fertility tracking apps and contribute insights that could inform policymaking and legal perspectives.
Enhancing Epilepsy Awareness and Cooperative Care in Elementary and Middle Schools
Authors: Min Aehong , Trueblood Miller Wendy R. , Connelly Kay , Nippert-Eng Christena , Lin Hsien-Chang , Shih Patrick C.
Published: April 2024

Epilepsy, a prevalent chronic neurological disorder, presents numerous challenges for people with epilepsy (PWEs) and their caregivers. They experience difficulties in receiving proper care and support due to stigma and misconceptions. Promoting public awareness in early education would be critical to reduce the stigma and to properly support them. We conducted 145 surveys and 21 interviews with teachers, school nurses, and parents of elementary and middle school students. While the participants exhibited slightly positive attitudes towards PWEs, we identified obstacles that hinder learning about epilepsy and seizure first aid: inadequate education and limited information sharing among school stakeholders. Moreover, there is a pressing need for age-appropriate education that considers the students' ages and perceptual levels. Considering the current limitations and needs, we propose potential implications for future information and communication technologies (ICTs) designs, including knowledge-sharing systems and an educational game aimed at enhancing epilepsy awareness and fostering collaborative care in elementary and middle school environments.
Misinformation as a Harm: Structured Approaches for Fact-Checking Prioritization
Authors: Sehat Connie Moon , Li Ryan , Nie Peipei , Prabhakar Tarunima , Zhang Amy X.
Published: April 2024

In this work, we examine how fact-checkers prioritize which claims to fact-check and what tools may assist them in their efforts. Through a series of interviews with 23 professional fact-checkers from around the world, we validate that harm assessment is a central component of how fact-checkers triage their work. We also clarify the processes behind fact-checking prioritization, finding that they are typically ad hoc, and gather suggestions for tools that could help with these processes.

To address the needs articulated by fact-checkers, we present a structured framework of questions to help fact-checkers negotiate the priority of claims through assessing potential harms. Our FABLE Framework of Misinformation Harms incorporates five dimensions of magnitude---(social) Fragmentation, Actionability, Believability, Likelihood of spread, and Exploitativeness---that can help determine the potential urgency of a specific message or claim when considering misinformation as harm. The result is a practical and conceptual tool to support fact-checkers and others as they make strategic decisions to prioritize their efforts. We conclude with a discussion of computational approaches to support structured prioritization, as well as applications beyond fact-checking to content moderation and curation.
Neurodiversity and the Accessible University: Exploring Organizational Barriers, Access Labor and Opportunities for Change
Authors: Borsotti Valeria , Begel Andrew , Bjørn Pernille
Published: April 2024

The access needs of neurodivergent individuals in organizational settings are many and varied - and so are their everyday contributions to the creation of collective access. In this study, we contribute to the growing body of CSCW research on accessibility and investigate the invisible access labor of neurodivergent students in three computer science institutions. We use an exploratory, multi-stakeholder approach, combining semi-structured interviews (n=26) and document analysis. We adopted a broad definition of neurodiversity: our study included individuals with autism, dyslexia, ADHD, cyclothymia and individuals with neurological conditions that developed as a result of illness, trauma or injury. Our findings show that neurodivergent students face a number of structural and attitudinal barriers to access in the educational environment and within the disability support system. We identified barriers in three main areas: (i) assistive technology access barriers, (ii) cognitive and physical access barriers, and (iii) social access barriers. We examined how stigma, individualized understandings of disability and intersectional disadvantage shape organizational practices and explored how students are creatively improving collective access through micro-interventions, although these efforts are largely invisible. We then draw on our findings to identify opportunities for change. We propose access grafting as a bottom-up approach to rethinking and reorienting organizational strategies to improve equitable access.
Sustaining Community-Based Research in Computing: Lessons from Two Tech Capacity Building Initiatives for Local Businesses
Authors: Kotturi Yasmine , Hui Julie , Johnson TJ , Sanifu Lutalo , Dillahunt Tawanna R.
Published: April 2024

The field of human-computer interaction (HCI) has traditionally focused on the design of novel technology artifacts. However, ensuring considerations for artifact maintenance and repair is crucial to sustainably supporting the populations they aim to serve over the long term. Drawing on two multi-year programs for tech capacity building in post-industrial U.S. cities, this article presents a comparative analysis to investigate the challenges and strategies for sustained community-based research in computing. In particular, our work detailed three considerations for academic-community partnerships. First, long-term partnerships prioritized transferring trust across academic and community personnel and continually set expectations that responded to evolving community initiatives (i.e., relational sustainability). Second, partnerships used academic support as a way to kickstart community initiatives, and flexibly reframed interventions to stay aligned with evolving community goals (i.e., economic sustainability). Third, partnerships trained personnel to provide technical support alongside interventions and prioritized advice that resisted short-term trends (i.e., technical sustainability). We provide concrete examples of how our two academic-community partnerships carried out such suggestions-such details go unreported in scholarly articles yet are essential for sustainability considerations. We discuss ongoing challenges, such as rethinking when longevity should and should not be the end goal.
The Contemporary Art of Image Search: Iterative User Intent Expansion via Vision-Language Model
Authors: Ye Yilin , Zhu Qian , Xiao Shishi , Zhang Kang , Zeng Wei
Published: April 2024

Image search is an essential and user-friendly method to explore vast galleries of digital images. However, existing image search methods heavily rely on proximity measurements like tag matching or image similarity, requiring precise user inputs for satisfactory results. To meet the growing demand for a contemporary image search engine that enables accurate comprehension of users' search intentions, we introduce an innovative user intent expansion framework. Our framework leverages visual-language models to parse and compose multi-modal user inputs to provide more accurate and satisfying results. It comprises two-stage processes: 1) a parsing stage that incorporates a language parsing module with large language models to enhance the comprehension of textual inputs, along with a visual parsing module that integrates an interactive segmentation module to swiftly identify detailed visual elements within images; and 2) a logic composition stage that combines multiple user search intents into a unified logic expression for more sophisticated operations in complex searching scenarios. Moreover, the intent expansion framework enables users to perform flexible contextualized interactions with the search results to further specify or adjust their detailed search intents iteratively. We implemented the framework into an image search system for NFT (non-fungible token) search and conducted a user study to evaluate its usability and novel properties. The results indicate that the proposed framework significantly improves users' image search experience. Particularly the parsing and contextualized interactions prove useful in allowing users to express their search intents more accurately and engage in a more enjoyable iterative search experience.
The Impact of Imperfect XAI on Human-AI Decision-Making
Authors: Morrison Katelyn , Spitzer Philipp , Turri Violet , Feng Michelle , Kühl Niklas , Perer Adam
Published: April 2024

Explainability techniques are rapidly being developed to improve human-AI decision-making across various cooperative work settings. Consequently, previous research has evaluated how decision-makers collaborate with imperfect AI by investigating appropriate reliance and task performance with the aim of designing more human-centered computer-supported collaborative tools. Several human-centered explainable AI (XAI) techniques have been proposed in hopes of improving decision-makers' collaboration with AI; however, these techniques are grounded in findings from previous studies that primarily focus on the impact of incorrect AI advice. Few studies acknowledge the possibility of the explanations being incorrect even if the AI advice is correct. Thus, it is crucial to understand how imperfect XAI affects human-AI decision-making. In this work, we contribute a robust, mixed-methods user study with 136 participants to evaluate how incorrect explanations influence humans' decision-making behavior in a bird species identification task, taking into account their level of expertise and an explanation's level of assertiveness. Our findings reveal the influence of imperfect XAI and humans' level of expertise on their reliance on AI and human-AI team performance. We also discuss how explanations can deceive decision-makers during human-AI collaboration. Hence, we shed light on the impacts of imperfect XAI in the field of computer-supported cooperative work and provide guidelines for designers of human-AI collaboration systems.
The Virality of Hate Speech on Social Media
Authors: Maarouf Abdurahman , Pröllochs Nicolas , Feuerriegel Stefan
Published: April 2024

Online hate speech is responsible for violent attacks such as, e.g., the Pittsburgh synagogue shooting in 2018, thereby posing a significant threat to vulnerable groups and society in general. However, little is known about what makes hate speech on social media go viral. In this paper, we collect N = 25,219 cascades with 65,946 retweets from X (formerly known as Twitter) and classify them as hateful vs. normal. Using a generalized linear regression, we then estimate differences in the spread of hateful vs. normal content based on author and content variables. We thereby identify important determinants that explain differences in the spreading of hateful vs. normal content. For example, hateful content authored by verified users is disproportionally more likely to go viral than hateful content from non-verified ones: hateful content from a verified user (as opposed to normal content) has a 3.5 times larger cascade size, a 3.2 times longer cascade lifetime, and a 1.2 times larger structural virality. Altogether, we offer novel insights into the virality of hate speech on social media.
Profiling Programming Language Learning
Authors: Crichton Will , Krishnamurthi Shriram
Published: April 2024

This paper documents a year-long experiment to “profile” the process of learning a programming language: gathering data to understand what makes a language hard to learn, and using that data to improve the learning process. We added interactive quizzes to The Rust Programming Language, the official textbook for learning Rust. Over 13 months, 62,526 readers answered questions 1,140,202 times. First, we analyze the trajectories of readers. We find that many readers drop-out of the book early when faced with difficult language concepts like Rust’s ownership types. Second, we use classical test theory and item response theory to analyze the characteristics of quiz questions. We find that better questions are more conceptual in nature, such as asking why a program does not compile vs. whether a program compiles. Third, we performed 12 interventions into the book to help readers with difficult questions. We find that on average, interventions improved quiz scores on the targeted questions by +20
A Learning-Based Approach to Static Program Slicing
Authors: Yadavally Aashish , Li Yi , Wang Shaohua , Nguyen Tien N.
Published: April 2024

Traditional program slicing techniques are crucial for early bug detection and manual/automated debugging of online code snippets. Nevertheless, their inability to handle incomplete code hinders their real-world applicability in such scenarios. To overcome these challenges, we present NS-Slicer, a novel learning-based approach that predicts static program slices for both complete and partial code Our tool leverages a pre-trained language model to exploit its understanding of fine-grained variable-statement dependencies within source code. With this knowledge, given a variable at a specific location and a statement in a code snippet, NS-Slicer determines whether the statement belongs to the backward slice or forward slice, respectively. We conducted a series of experiments to evaluate NS-Slicer's performance. On complete code, it predicts the backward and forward slices with an F1-score of 97.41% and 95.82%, respectively, while achieving an overall F1-score of 96.77%. Notably, in 85.20% of the cases, the static program slices predicted by NS-Slicer exactly match entire slices from the oracle. For partial programs, it achieved an F1-score of 96.77%–97.49% for backward slicing, 92.14%–95.40% for forward slicing, and an overall F1-score of 94.66%–96.62%. Furthermore, we demonstrate NS-Slicer's utility in vulnerability detection (VD), integrating its predicted slices into an automated VD tool. In this setup, the tool detected vulnerabilities in Java code with a high F1-score of 73.38%. We also include the analyses studying NS-Slicer’s promising performance and limitations, providing insights into its understanding of intrinsic code properties such as variable aliasing, leading to better slicing.
UniSparse: An Intermediate Language for General Sparse Format Customization
Authors: Liu Jie , Zhao Zhongyuan , Ding Zijian , Brock Benjamin , Rong Hongbo , Zhang Zhiru
Published: April 2024

The ongoing trend of hardware specialization has led to a growing use of custom data formats when processing sparse workloads, which are typically memory-bound. These formats facilitate optimized software/hardware implementations by utilizing sparsity pattern- or target-aware data structures and layouts to enhance memory access latency and bandwidth utilization. However, existing sparse tensor programming models and compilers offer little or no support for productively customizing the sparse formats. Additionally, because these frameworks represent formats using a limited set of per-dimension attributes, they lack the flexibility to accommodate numerous new variations of custom sparse data structures and layouts. To overcome this deficiency, we propose UniSparse, an intermediate language that provides a unified abstraction for representing and customizing sparse formats. Unlike the existing attribute-based frameworks, UniSparse decouples the logical representation of the sparse tensor (i.e., the data structure) from its low-level memory layout, enabling the customization of both. As a result, a rich set of format customizations can be succinctly expressed in a small set of well-defined query, mutation, and layout primitives. We also develop a compiler leveraging the MLIR infrastructure, which supports adaptive customization of formats, and automatic code generation of format conversion and compute operations for heterogeneous architectures. We demonstrate the efficacy of our approach through experiments running commonly-used sparse linear algebra operations with specialized formats on multiple different hardware targets, including an Intel CPU, an NVIDIA GPU, an AMD Xilinx FPGA, and a simulated processing-in-memory (PIM) device.
HOL4P4: Mechanized Small-Step Semantics for P4
Authors: Alshnakat Anoud , Lundberg Didrik , Guanciale Roberto , Dam Mads
Published: April 2024

We present the first semantics of the network data plane programming language P4 able to adequately capture all key features of P4
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, the most recent version of P4, including external functions (externs) and concurrency. These features are intimately related since, in P4, extern invocations are the only points at which one execution thread can affect another. Reflecting P4’s lack of a general-purpose memory and the presence of multithreading the semantics is given in small-step style and eschews the use of a heap. In addition to the P4 language itself, we provide an architectural level semantics, which allows the composition of P4-programmed blocks, models end-to-end packet processing, and can take into account features such as arbitration and packet recirculation. A corresponding type system is provided with attendant progress, preservation, and type-soundness theorems. Semantics, type system, and meta-theory are formalized in the HOL4 theorem prover. From this formalization, we derive a HOL4 executable semantics that supports verified execution of programs with partially symbolic packets able to validate simple end-to-end program properties.
Quantitative Bounds on Resource Usage of Probabilistic Programs
Authors: Chatterjee Krishnendu , Goharshady Amir Kafshdar , Meggendorfer Tobias , Žikelić Đorđe
Published: April 2024

Cost analysis, also known as resource usage analysis, is the task of finding bounds on the total cost of a program and is a well-studied problem in static analysis. In this work, we consider two classical quantitative problems in cost analysis for probabilistic programs. The first problem is to find a bound on the expected total cost of the program. This is a natural measure for the resource usage of the program and can also be directly applied to average-case runtime analysis. The second problem asks for a tail bound, i.e. ‍given a threshold
t
the goal is to find a probability bound
p
such that ℙ[total cost ≥
t
] ≤
p
. Intuitively, given a threshold
t
on the resource, the problem is to find the likelihood that the total cost exceeds this threshold.

First, for expectation bounds, a major obstacle in previous works on cost analysis is that they can handle only non-negative costs or bounded variable updates. In contrast, we provide a new variant of the standard notion of cost martingales, that allows us to find expectation bounds for a class of programs with general positive or negative costs and no restriction on the variable updates. More specifically, our approach is applicable as long as there is a lower bound on the total cost incurred along every path.

Second, for tail bounds, all previous methods are limited to programs in which the expected total cost is finite. In contrast, we present a novel approach, based on a combination of our martingale-based method for expectation bounds with a quantitative safety analysis, to obtain a solution to the tail bound problem that is applicable even to programs with infinite expected cost. Specifically, this allows us to obtain runtime tail bounds for programs that do not terminate almost-surely.

In summary, we provide a novel combination of martingale-based cost analysis and quantitative safety analysis that is able to find expectation and tail cost bounds for probabilistic programs, without the restrictions of non-negative costs, bounded updates, or finiteness of the expected total cost. Finally, we provide experimental results showcasing that our approach can solve instances that were beyond the reach of previous methods.
Quarl: A Learning-Based Quantum Circuit Optimizer
Authors: Li Zikun , Peng Jinjun , Mei Yixuan , Lin Sina , Wu Yi , Padon Oded , Jia Zhihao
Published: April 2024

Optimizing quantum circuits is challenging due to the very large search space of functionally equivalent circuits and the necessity of applying transformations that temporarily decrease performance to achieve a final performance improvement. This paper presents Quarl, a learning-based quantum circuit optimizer. Applying reinforcement learning (RL) to quantum circuit optimization raises two main challenges: the large and varying action space and the non-uniform state representation. Quarl addresses these issues with a novel neural architecture and RL-training procedure. Our neural architecture decomposes the action space into two parts and leverages graph neural networks in its state representation, both of which are guided by the intuition that optimization decisions can be mostly guided by local reasoning while allowing global circuit-wide reasoning. Our evaluation shows that Quarl significantly outperforms existing circuit optimizers on almost all benchmark circuits. Surprisingly, Quarl can learn to perform rotation merging—a complex, non-local circuit optimization implemented as a separate pass in existing optimizers.
Design and Implementation of an Aspect-Oriented C Programming Language
Authors: Chen Zhe , Zhu Yunlong , Wang Zhemin
Published: April 2024

Aspect-Oriented Programming (AOP) is a programming paradigm that implements crosscutting concerns in a modular way. People have witnessed the prosperity of AOP languages for Java and C++, such as AspectJ and AspectC++, which has propelled AOP to become an important programming paradigm with many interesting application scenarios, e.g., runtime verification. In contrast, the AOP languages for C are still poor and lack compiler support. In this paper, we design a new general-purpose and expressive aspect-oriented C programming language, namely Aclang, and implement a compiler for it, which brings fully-fledged AOP support into the C domain. We have evaluated the effectiveness and performance of our compiler against two state-of-the-art tools, ACC and AspectC++. In terms of effectiveness, Aclang outperforms ACC and AspectC++. In terms of performance, Aclang outperforms ACC in execution time and outperforms AspectC++ in both execution time and memory consumption.
Multiverse Notebook: Shifting Data Scientists to Time Travelers
Authors: Sato Shigeyuki , Nakamaru Tomoki
Published: April 2024

Computational notebook environments are popular and de facto standard
tools for programming in data science, whereas computational notebooks are notorious in
software engineering. The criticism there stems from the characteristic
of facilitating unrestricted dynamic patching of running programs, which
makes exploratory coding quick but the resultant code messy and
inconsistent. In this work, we first reveal that dynamic patching is a
natural demand rather than a mere bad practice in data science
programming on Kaggle. We then develop Multiverse Notebook, a
computational notebook engine for time-traveling exploration. It
enables users to time-travel to any past state and restart with new code
from there under state isolation. We present an approach to efficiently
implementing time-traveling exploration. We empirically evaluate
Multiverse Notebook on ten real-world tasks from Kaggle. Our experiments
show that time-traveling exploration on Multiverse Notebook is
reasonably efficient.
Hopping Proofs of Expectation-Based Properties: Applications to Skiplists and Security Proofs
Authors: Avanzini Martin , Barthe Gilles , Grégoire Benjamin , Moser Georg , Vanoni Gabriele
Published: April 2024

We propose, implement, and evaluate a hopping proof approach for
proving expectation-based properties of probabilistic programs. Our
approach combines EHL, a syntax-directed proof system for reducing
proof goals of a program to proof goals of simpler programs, with a
"hopping" proof rule for reducing proof goals of an
original program to proof goal of a different program which is
suitably related (by means of pRHL, a relational program logic for
probabilistic program) to the original program. We prove that EHL
is sound for a core language with procedure calls and adversarial
computations, and complete for the adversary-free fragment of the
language. We also provide an implementation of EHL into
EasyCrypt, a proof assistant tailored for reasoning about
relational properties of probabilistic programs. We provide a tight
integration of EHL with other program logics supported by
EasyCrypt, and in particular probabilistic Relational Hoare Logic
(pRHL). Using this tight integration, we give mechanized proofs of
expected complexity of in-place implementations of randomized
quickselect and skip lists. We also sketch applications of our
approach to cryptographic proofs and discuss the broader impact of
EHL in the EasyCrypt proof assistant.