bluuebunny/crossref_metadata_2025 huggingface dataset:
A Multi-attention Collaborative Deep Learning Approach for Blood Pressure Prediction
Authors: He Luo , Liu Hongyan , Yang Yinghui , Wang Bei
Published: October 2021

We develop a deep learning model based on Long Short-term Memory (LSTM) to predict blood pressure based on a unique data set collected from physical examination centers capturing comprehensive multi-year physical examination and lab results. In the Multi-attention Collaborative Deep Learning model (MAC-LSTM) we developed for this type of data, we incorporate three types of attention to generate more explainable and accurate results. In addition, we leverage information from similar users to enhance the predictive power of the model due to the challenges with short examination history. Our model significantly reduces predictive errors compared to several state-of-the-art baseline models. Experimental results not only demonstrate our model’s superiority but also provide us with new insights about factors influencing blood pressure. Our data is collected in a natural setting instead of a setting designed specifically to study blood pressure, and the physical examination items used to predict blood pressure are common items included in regular physical examinations for all the users. Therefore, our blood pressure prediction results can be easily used in an alert system for patients and doctors to plan prevention or intervention. The same approach can be used to predict other health-related indexes such as BMI.
Hiring CS Graduates: What We Learned from Employers
Authors: Stepanova Anna , Weaver Alexis , Lahey Joanna , Alexander Gerianne , Hammond Tracy
Published: October 2021

Computer science(CS) majors are in high demand and account for a large part of national computer and information technology job market applicants. Employment in this sector is projected to grow 12% between 2018 and 2028, which is faster than the average of all other occupations. Published data are available on traditional non-computer science-specific hiring processes. However, the hiring process for CS majors may be different. It is critical to have up-to-date information on questions such as “what positions are in high demand for CS majors?,” “what is a typical hiring process?,” and “what do employers say they look for when hiring CS graduates?” This article discusses the analysis of a survey of 218 recruiters hiring CS graduates in the United States. We used Atlas.ti to analyze qualitative survey data and report the results on what positions are in the highest demand, the hiring process, and the resume review process. Our study revealed that a software developer was the most common job the recruiters were looking to fill. We found that the hiring process steps for CS graduates are generally aligned with traditional hiring steps, with an additional emphasis on technical and coding tests. Recruiters reported that their hiring choices were based on reviewing resume’s experience, GPA, and projects sections. The results provide insights into the hiring process, decision making, resume analysis, and some discrepancies between current undergraduate CS program outcomes and employers’ expectations.
Supporting Collaborative Reflection on Personal Values and Health
Authors: Berry Andrew B.L. , Lim Catherine Y. , Liang Calvin A. , Hartzler Andrea L. , Hirsch Tad , Ferguson Dawn M. , Bermet Zoë A. , Ralston James D.
Published: October 2021

People with multiple chronic conditions (MCC) need support to understand and articulate how their personal values relate to their health and health care. We developed three prototypes for supporting reflection on values and health and tested them in a qualitative study involving 12 people with MCC. We identified benefits and limitations to building on how patients prepare for visits with clinicians; revealed varying levels of comfort with deep, exploratory reflection involving a facilitator; and found that reflection oriented toward the future could elicit hopeful attitudes and plans for change, while reflection on the past elicited strong resistance. We translated these findings into design guidelines for supporting collaborative reflection on values and health. We also discussed these findings in relation to previous literature on designing for reflection in three areas: shifting between self-guided and facilitator-guided reflection, balancing between outcome-oriented and exploratory reflection, and exploring temporality in reflection.
Cocode: Providing Social Presence with Co-learner Screen Sharing in Online Programming Classes
Authors: Byun Jeongmin , Park Jungkook , Oh Alice
Published: October 2021

Social presence is known to be important for distance education, and a common approach in online classes is to provide chat boxes and forums to provide the social presence. In such a class, however, learners must explicitly act beyond their normal learning activities, so often there is no social presence in the class even when there are several learners working on the same course material. In this paper, we develop an approach where learners can share the social presence without any explicit action; their normal learning activities would be used to provide visual cues for social presence. We present Cocode, a system designed for an online programming class that shows other learners' code editors and running output in the programming environment with minimum privacy issues. For evaluation, we ran two user studies with groups of participants who took an offline class and an online programming class from the university; results from the studies showed that learners felt less social presence in Cocode than in offline classes, but they felt significantly more social presence in Cocode than in online classes with live video lectures, forums, and chat sessions.
Don't Disturb Me: Challenges of Interacting with Software Bots on Open Source Software Projects
Authors: Wessel Mairieli , Wiese Igor , Steinmacher Igor , Gerosa Marco Aurelio
Published: October 2021

Software bots are used to streamline tasks in Open Source Software (OSS) projects' pull requests, saving development cost, time, and effort. However, their presence can be disruptive to the community. We identified several challenges caused by bots in pull request interactions by interviewing 21 practitioners, including project maintainers, contributors, and bot developers. In particular, our findings indicate noise as a recurrent and central problem. Noise affects both human communication and development workflow by overwhelming and distracting developers. Our main contribution is a theory of how human developers perceive annoying bot behaviors as noise on social coding platforms. This contribution may help practitioners understand the effects of adopting a bot, and researchers and tool designers may leverage our results to better support human-bot interaction on social coding platforms.
PrivacyPrimer: Towards Privacy-Preserving Episodic Memory Support For Older Adults
Authors: Kandappu Thivya , Subbaraju Vigneshwaran , Xu Qianli
Published: October 2021

Built-in pervasive cameras have become an integral part of mobile/wearable devices and enabled a wide range of ubiquitous applications with their ability to be "always-on". In particular, life-logging has been identified as a means to enhance the quality of life of older adults by allowing them to reminisce about their own life experiences. However, the sensitive images captured by the cameras threaten individuals' right to have private social lives and raise concerns about privacy and security in the physical world. This threat gets worse when image recognition technologies can link images to people, scenes, and objects, hence, implicitly and unexpectedly reveal more sensitive information such as social connections. In this paper, we first examine life-log images obtained from 54 older adults to extract (a) the artifacts or visual cues, and (b) the context of the image that influences an older life-logger's ability to recall the life events associated with a life-log image. We call these artifacts and contextual cues "stimuli". Using the set of stimuli extracted, we then propose a set of obfuscation strategies that naturally balances the trade-off between reminiscability and privacy (revealing social ties) while selectively obfuscating parts of the images. More specifically, our platform yields privacy-utility tradeoff by compromising, on average, modest 13.4% reminiscability scores while significantly improving privacy guarantees -- around 40% error in cloud estimation.
"What is the Topic of the Group, Please?" On Migration, Care and the Challenges of Participation in Design
Authors: Krüger Max , Weibert Anne , de Castro Leal Debora , Randall Dave , Wulf Volker
Published: October 2021

An increasing number of people around the world are forced to leave their homes due to the climate catastrophe, regional conflict or poverty. In their new host countries however, refugees and migrants are frequently met by a wide range of challenges, including wider societal participation. The difficulties migrants and refugees face have also increasingly become the topic of HCI and CSCW work. In this paper we report on a three year-long project, involving refugees, migrants and activist supporters in a co-design project to develop tools that aid the process of resettling. Several aspects have challenged equal participation in the project, including divergent motives, unequal power distribution and cultural heterogeneity. Despite these challenges the project outcomes are in use and maintained beyond the project runtime through voluntary actors. We reflect on this discrepancy between process and outcome drawing on the concept of care.
Orienting, Framing, Bridging, Magic, and Counseling: How Data Scientists Navigate the Outer Loop of Client Collaborations in Industry and Academia
Authors: Kross Sean , Guo Philip
Published: October 2021

Data scientists often collaborate with clients to analyze data to meet a client's needs. What does the end-to-end workflow of a data scientist's collaboration with clients look like throughout the lifetime of a project? To investigate this question, we interviewed ten data scientists (5 female, 4 male, 1 non-binary) in diverse roles across industry and academia. We discovered that they work with clients in a six-stage outer-loop workflow, which involves 1) laying groundwork by building trust before a project begins, 2) orienting to the constraints of the client's environment, 3) collaboratively framing the problem, 4) bridging the gap between data science and domain expertise, 5) the inner loop of technical data analysis work, 6) counseling to help clients emotionally cope with analysis results. This novel outer-loop workflow contributes to CSCW by expanding the notion of what collaboration means in data science beyond the widely-known inner-loop technical workflow stages of acquiring, cleaning, analyzing, modeling, and visualizing data. We conclude by discussing the implications of our findings for data science education, parallels to design work, and unmet needs for tool development.
Task Execution Quality Maximization for Mobile Crowdsourcing in Geo-Social Networks
Authors: Wang Liang , Yu Zhiwen , Yang Dingqi , Wang Tian , Wang En , Guo Bin , Zhang Daqing
Published: October 2021

With the rapid development of smart devices and high-quality wireless technologies, mobile crowdsourcing (MCS) has been drawing increasing attention with its great potential in collaboratively completing complicated tasks on a large scale. A key issue toward successful MCS is participant recruitment, where a MCS platform directly recruits suitable crowd participants to execute outsourced tasks by physically traveling to specified locations. Recently, a novel recruitment strategy, namely Word-of-Mouth(WoM)-based MCS, has emerged to effectively improve recruitment effectiveness, by fully exploring users' mobility traces and social relationships on geo-social networks. Against this background, we study in this paper a novel problem, namely Expected Task Execution Quality Maximization (ETEQM) for MCS in geo-social networks, which strives to search a subset of seed users to maximize the expected task execution quality of all recruited participants, under a given incentive budget. To characterize the MCS task propagation process over geo-social networks, we first adopt a propagation tree structure to model the autonomous recruitment between the referrers and the referrals. Based on the model, we then formalize the task execution quality and devise a novel incentive mechanism by harnessing the business strategy of multi-level marketing. We formulate our ETEQM problem as a combinatorial optimization problem, and analyze its NP hardness and high-dimensional characteristics. Based on a cooperative co-evolution framework, we proposed a divide-and-conquer problem-solving approach named ETEQM-CC. We conduct extensive simulation experiments and a case study, verifying the effectiveness of our proposed approach.
Do Platform Migrations Compromise Content Moderation? Evidence from r/The_Donald and r/Incels
Authors: Horta Ribeiro Manoel , Jhaver Shagun , Zannettou Savvas , Blackburn Jeremy , Stringhini Gianluca , De Cristofaro Emiliano , West Robert
Published: October 2021

When toxic online communities on mainstream platforms face moderation measures, such as bans, they may migrate to other platforms with laxer policies or set up their own dedicated websites. Previous work suggests that within mainstream platforms, community-level moderation is effective in mitigating the harm caused by the moderated communities. It is, however, unclear whether these results also hold when considering the broader Web ecosystem. Do toxic communities continue to grow in terms of their user base and activity on the new platforms? Do their members become more toxic and ideologically radicalized? In this paper, we report the results of a large-scale observational study of how problematic online communities progress following community-level moderation measures. We analyze data from r/The_Donald and r/Incels, two communities that were banned from Reddit and subsequently migrated to their own standalone websites. Our results suggest that, in both cases, moderation measures significantly decreased posting activity on the new platform, reducing the number of posts, active users, and newcomers. In spite of that, users in one of the studied communities (r/The_Donald) showed increases in signals associated with toxicity and radicalization, which justifies concerns that the reduction in activity may come at the expense of a more toxic and radical community. Overall, our results paint a nuanced portrait of the consequences of community-level moderation and can inform their design and deployment.
Goldilocks: Consistent Crowdsourced Scalar Annotations with Relative Uncertainty
Authors: Chen Quan Ze , Weld Daniel S. , Zhang Amy X.
Published: October 2021

Human ratings have become a crucial resource for training and evaluating machine learning systems. However, traditional elicitation methods for absolute and comparative rating suffer from issues with consistency and often do not distinguish between uncertainty due to disagreement between annotators and ambiguity inherent to the item being rated. In this work, we present Goldilocks, a novel crowd rating elicitation technique for collecting calibrated scalar annotations that also distinguishes inherent ambiguity from inter-annotator disagreement. We introduce two main ideas: grounding absolute rating scales with examples and using a two-step bounding process to establish a range for an item's placement. We test our designs in three domains: judging toxicity of online comments, estimating satiety of food depicted in images, and estimating age based on portraits. We show that (1) Goldilocks can improve consistency in domains where interpretation of the scale is not universal, and that (2) representing items with ranges lets us simultaneously capture different sources of uncertainty leading to better estimates of pairwise relationship distributions.
Cross-platform Information Operations: Mobilizing Narratives & Building Resilience through both 'Big' & 'Alt' Tech
Authors: Wilson Tom , Starbird Kate
Published: October 2021

Despite increasing awareness and research about online strategic information operations, there remain gaps in our understanding, including how information operations leverage the wider information ecosystem and take shape on and across multiple social media platforms. In this paper we use mixed methods, including digital trace ethnography, to look beyond a single social media platform to the broader information ecosystem. We aim to understand how multiple social media platforms are used, in parallel and complementary ways, to achieve the strategic goals of online information operations. We focus on a specific case study: the contested online conversation surrounding Syria Civil Defense (the White Helmets), a group of first responders that assists civilians affected by the civil war within the country. Our findings reveal a network of social media platforms from which content is produced, stored, and integrated into the Twitter conversation. We highlight specific activities that sustain the strategic narratives and attempt to influence the media agenda. And we note that underpinning these efforts is the work of resilience-building: the use of alternative (non-mainstream) platforms to counter perceived threats of 'censorship' by large, established social media platforms. We end by discussing the implications on social media platform policy.
Charting the Path: Requirements and Constraints for Technology-Supported Walking Meetings
Authors: Haliburton Luke , Wozniak Pawel W. , Schmidt Albrecht , Niess Jasmin
Published: October 2021

Traditional meetings involve extensive sitting, which negatively impacts the health of attendees. Understanding how technology can facilitate integrating physical activity into the workplace, such as in walking meetings, is vital to improving workplace wellbeing. To that end, we applied a mixed-method approach to explore requirements and opportunities for walking meetings. We conducted an online questionnaire and a series of interviews with early adopters of walking meetings and created design fictions based on their feedback. We evaluated the design fictions with a second questionnaire and garnered additional feedback from the original early adopters. Based on our findings, we derived four dimensions associated with walking meetings: practical, environmental, social, and cognitive facets. We define attributes, challenges, and opportunities within these dimensions which are important for designing systems that support walking meetings. Our work identifies key considerations for developing systems that integrate physical activity into communication activities.
Identifying Informatively Easy and Informatively Hard Concepts
Authors: Wiegand R. Paul , Bucci Anthony , Kumar Amruth N. , Albert Jennifer , Gaspar Alessio
Published: October 2021

In this article, we leverage ideas from the theory of coevolutionary computation to analyze interactions of students with problems. We introduce the idea of
informatively
easy or hard concepts. Our approach is different from more traditional analyses of problem difficulty such as item analysis in the sense that we consider Pareto dominance relationships within the multidimensional structure of student–problem performance data rather than average performance measures. This method allows us to uncover not just the problems on which students are struggling but also the variety of difficulties different students face. Our approach is to apply methods from the Dimension Extraction Coevolutionary Algorithm to analyze problem-solving logs of students generated when they use an online software tutoring suite for introductory computer programming called
problets
. The results of our analysis not only have implications for how to scale up and improve adaptive tutoring software but also have the promise of contributing to the identification of common misconceptions held by students and thus, eventually, to the construction of a concept inventory for introductory programming.
Automatically Labeling Low Quality Content on Wikipedia By Leveraging Patterns in Editing Behaviors
Authors: Asthana Sumit , Tobar Thommel Sabrina , Halfaker Aaron Lee , Banovic Nikola
Published: October 2021

Wikipedia articles aim to be definitive sources of encyclopedic content. Yet, only 0.6% of Wikipedia articles have high quality according to its quality scale due to insufficient number of Wikipedia editors and enormous number of articles. Supervised Machine Learning (ML) quality improvement approaches that can automatically identify and fix content issues rely on manual labels of individual Wikipedia sentence quality. However, current labeling approaches are tedious and produce noisy labels. Here, we propose an automated labeling approach that identifies the semantic category (e.g., adding citations, clarifications) of historic Wikipedia edits and uses the modified sentences prior to the edit as examples that require that semantic improvement. Highest-rated article sentences are examples that no longer need semantic improvements. We show that training existing sentence quality classification algorithms on our labels improves their performance compared to training them on existing labels. Our work shows that editing behaviors of Wikipedia editors provide better labels than labels generated by crowdworkers who lack the context to make judgments that the editors would agree with.
Co-Design with Older Adults: Examining and Reflecting on Collaboration with Aging Communities
Authors: Sakaguchi-Tang Dawn K. , Cunningham Jay L. , Roldan Wendy , Yip Jason , Kientz Julie A.
Published: October 2021

Co-design methods have involved older adults in the design process to fill the knowledge gap that younger adult designers might encounter when designing for an aging population. A focus of co-design means establishing equal and equitable relationships between users and designers. To understand the factors that contribute to equal collaborations between older adults and student designers, we conducted 12 co-design sessions with 16 older adults and 11 student designers. We examined their interactions by adapting a framework initially aimed to understand the child-adult design partnership. We also analyzed student designers' reflections to understand their experiences and learnings from designing with older adults. Our findings demonstrate that developing a design partnership is complex. The framework helped surface factors like sharing life experiences and role ownership that influenced balanced or unbalanced interactions. Through the student designers' reflections, we found that student designers identified challenges they encountered and the assumptions they had about the older adult population. We believe that immersing students in a co-design experience with older adults and leveraging reflection activities provides an educational and meaningful experience to the design students.
Exploring Interactions Between Trust, Anthropomorphism, and Relationship Development in Voice Assistants
Authors: Seymour William , Van Kleek Max
Published: October 2021

Modern conversational agents such as Alexa and Google Assistant represent significant progress in speech recognition, natural language processing, and speech synthesis. But as these agents have grown more realistic, concerns have been raised over how their social nature might unconsciously shape our interactions with them. Through a survey of 500 voice assistant users, we explore whether users' relationships with their voice assistants can be quantified using the same metrics as social, interpersonal relationships; as well as if this correlates with how much they trust their devices and the extent to which they anthropomorphise them. Using Knapp's staircase model of human relationships, we find that not only can human-device interactions be modelled in this way, but also that relationship development with voice assistants correlates with increased trust and anthropomorphism.
Trend Alert: A Cross-Platform Organization Manipulated Twitter Trends in the Indian General Election
Authors: Jakesch Maurice , Garimella Kiran , Eckles Dean , Naaman Mor
Published: October 2021

Political organizations worldwide keep innovating their use of social media technologies. In the 2019 Indian general election, organizers used a network of WhatsApp groups to manipulate Twitter trends through coordinated mass postings. We joined 600 WhatsApp groups that support the Bharatiya Janata Party, the right-wing party that won the general election, to investigate these campaigns. We found evidence of 75 hashtag manipulation campaigns in the form of mobilization messages with lists of pre-written tweets. Building on this evidence, we estimate the campaigns' size, describe their organization and determine whether they succeeded in creating controlled social media narratives. Our findings show that the campaigns produced hundreds of nationwide Twitter trends throughout the election. Centrally controlled but voluntary in participation, this hybrid configuration of technologies and organizational strategies shows how profoundly online tools transform campaign politics. Trend alerts complicate the debates over the legitimate use of digital tools for political participation and may have provided a blueprint for participatory media manipulation by a party with popular support.
Digital Privacy Perceptions of Asylum Seekers in Germany: An Empirical Study about Smartphone Usage during the Flight
Authors: Steinbrink Enno , Reichert Lilian , Mende Michelle , Reuter Christian
Published: October 2021

Since 2015, an increased number of asylum seekers is coming to Europe. These migration movements increasingly rely on digital infrastructure, such as mobile internet access and online services, in order to reach their targeted destination countries. Asylum seekers often use smartphones for information and communication purposes. Even though there are many positive aspects in the use of such technologies, researchers have to consider the perceived risks of this specific user group. This work aims at investigating the use of mobile information technologies by asylum seekers during their flight, especially taking privacy into account. Thus, it examines asylum seekers' digital privacy perceptions and identifies privacy protection behaviors by conducting a qualitative interview study with 14 asylum seekers who applied for asylum in Germany. The results show that asylum seekers are often aware of the various risks deriving from the use of smartphones and ICT, such as surveillance and persecution by state or non-state actors as well as extortion by criminals. Based on this, this work furthermore outlines different strategies used to manage these risks. Since the lack of privacy and trust leads to avoidance behavior, the insights of this study provide valuable information for the design of assistance apps and collaboration platforms, which appropriately address the specific needs for digital privacy in the context of flight, or for the conception of privacy-enhancing technologies helping to achieve this.
On the State of Reporting in Crowdsourcing Experiments and a Checklist to Aid Current Practices
Authors: Ramírez Jorge , Sayin Burcu , Baez Marcos , Casati Fabio , Cernuzzi Luca , Benatallah Boualem , Demartini Gianluca
Published: October 2021

Crowdsourcing is being increasingly adopted as a platform to run studies with human subjects. Running a crowdsourcing experiment involves several choices and strategies to successfully port an experimental design into an otherwise uncontrolled research environment, e.g., sampling crowd workers, mapping experimental conditions to micro-tasks, or ensure quality contributions. While several guidelines inform researchers in these choices, guidance of how and what to report from crowdsourcing experiments has been largely overlooked. If under-reported, implementation choices constitute variability sources that can affect the experiment's reproducibility and prevent a fair assessment of research outcomes. In this paper, we examine the current state of reporting of crowdsourcing experiments and offer guidance to address associated reporting issues. We start by identifying sensible implementation choices, relying on existing literature and interviews with experts, to then extensively analyze the reporting of 171 crowdsourcing experiments. Informed by this process, we propose a checklist for reporting crowdsourcing experiments.
Where Do Stories Come From? Examining the Exploration Process in Investigative Data Journalism
Authors: Showkat Dilruba , Baumer Eric P. S.
Published: October 2021

Investigative data journalists work with a variety of data sources to tell a story. Though prior work has indicated that there is a close relationship between journalists' data work practices and that of data scientists. However, these relationships and data work practices are not empirically examined, and understanding them is crucial to inform the design of tools that are used by different groups of people including data scientists and data journalists. Thus, to bridge this gap, we studied investigative reporters' data work practices with one non-profit investigative newsroom. Our study design includes two activities: 1) semi-structured interviews with journalists, and 2) a sketching activity allowing journalists to depict examples of their work practices. By analyzing these data and synthesizing them across related prior work, we propose the major phases in the data-driven investigative journalism story idea generation process. Our study findings show that the journalists employ a collection of multiple, iterative, cyclic processes to identify journalistically "interesting'' story ideas. These processes both significantly resemble and show subtle nuanced differences with data science work practices identified in prior research. We further verified our proposal through a member check with key informants. This work offers three primary contributions. First, it provides a close glimpse into the main phases of investigative journalists' data-driven story idea generation technique. Second, it complements prior work studying formal data science practices by examining data-driven investigative journalists, whose primary expertise lies outside computing. Third, it identifies particular points in the data exploration processes that would benefit from design interventions and suggests future research directions.
How Content Authored by People with Dementia Affects Attitudes towards Dementia
Authors: Lazar Amanda , Brewer Robin N. , Kacorri Hernisa , Hong Jonggi , Punzalan Mary Nicole Dugay , Mahathir Maisarah , Vander Hyde Olivia , Ross III Warren
Published: October 2021

Negative attitudes shape experiences with stigmatized conditions such as dementia, from affecting social relationships to influencing willingness to adopt technology. Consequently, attitudinal change has been identified as one lever to improve life for people with stigmatized conditions. Though recognized as a scaleable approach, social media has not been studied in terms of how it should best be designed or deployed to target attitudes and understanding of dementia. Through a mixed methods design with 123 undergraduate college students, we study the effect of being exposed to dementia-related media, including content produced by people with dementia. We selected undergraduate college students as the target of our intervention, as they represent the next generation that will work and interact with individuals with dementia. Our analysis describes changes over the period of two weeks in attitudes and understanding of the condition. The shifts in understanding of dementia that we found in our qualitative analysis were not captured by the instrument we selected to assess understanding of dementia. While small improvements in positive and overall attitudes were seen across all interventions and the control, we observe a different pattern with negative attitudes, where transcriptions of content produced by people with dementia significantly reduced negative attitudes. The discussion presents implications for supporting people with dementia as content producers, doing so in ways that best affect attitudes and understanding by drawing on research on cues and interactive media, and supporting students in changing their perspectives towards people with dementia.
Social Influence Leads to the Formation of Diverse Local Trends
Authors: Epstein Ziv , Groh Matthew , Dubey Abhimanyu , Pentland Alex
Published: October 2021

How does the visual design of digital platforms impact user behavior and the resulting environment? A body of work suggests that introducing social signals to content can increase both the inequality and unpredictability of its success, but has only been shown in the context of music listening. To further examine the effect of social influence on media popularity, we extend this research to the context of algorithmically-generated images by re-adapting Salganik et al's Music Lab experiment. On a digital platform where participants discover and curate AI-generated hybrid animals, we randomly assign both the knowledge of other participants' behavior and the visual presentation of the information. We successfully replicate the Music Lab's findings in the context of images, whereby social influence leads to an unpredictable winner-take-all market. However, we also find that social influence can lead to the emergence of local cultural trends that diverge from the status quo and are ultimately more diverse. We discuss the implications of these results for platform designers and animal conservation efforts.
After Violation But Before Sanction: Understanding Volunteer Moderators' Profiling Processes Toward Violators in Live Streaming Communities
Authors: Cai Jie , Wohn Donghee Yvette
Published: October 2021

Content moderation is an essential part of online community health and governance. While much of extant research is centered on what happens to the content, moderation also involves the management of violators. This study focuses on how moderators (mods) make decisions about their actions after the violation takes place but before the sanction by examining how they "profile" the violators. Through observations and interviews with volunteer mods on Twitch, we found that mods engage in a complex process of collaborative evidence collection and profile violators into different categories to decide the type and extent of punishment. Mods consider violators' characteristics as well as behavioral history and violation context before taking moderation action. The main purpose of the profiling was to avoid excessive punishment and aim to integrate violators more into the community. We discuss the contributions of profiling to moderation practice and suggest design mechanisms to facilitate mods' profiling processes.
Alternatives to Agrilogistics: Designing for Ecological Thinking
Authors: Biggs Heidi , Joshi Tejaswini , Murphy Ries , Bardzell Jeffrey , Bardzell Shaowen
Published: October 2021

Sustainable HCI (SHCI) researchers have historically looked to small and urban farmers to help situate and extend notions of sustainability within economic, social, and political frameworks. In the face of climate change and the Anthropocene, however, we ask how designing like the alternative farming practices of small and urban farmers might open up new, ecological approaches to agricultural technology. We conducted ethnographic field work with small farmers and their community in Indiana and show how they are challenging "agrilogistics," defined by philosopher Timothy Morton as a strict separation of nature and culture in food production, a separation, he argues, which underlies the substantial agricultural contributions to climate change. Our ethnography led us to suggest new possibilities for design of agricultural technology that support ecological thinking and caring for more-than-human actors through visceral imaginaries, posthuman storytelling, and engaging curiosity, possibilities which may offer ways to disentangle agricultural technology from agrilogistic paradigms.?
Understanding Delivery of Collectively Built Protocols in an Online Health Community for Discontinuation of Psychiatric Drugs
Authors: Papoutsaki Alexandra , So Samuel , Kenderova Georgia , Shapiro Bryan , Epstein Daniel A.
Published: October 2021

People often turn to online health communities (OHCs) for peer support on their specific medical conditions and health-related concerns. Over time, core members in OHCs build a shared understanding of the medical conditions they support. Although prior work has studied how individuals function differently in active sensemaking mode compared to habitual mode, little is known about how OHCs disseminate their advice once their core members operate primarily in habitual mode. We qualitatively observe one such OHC, 'Surviving Antidepressants', to understand how collectively-built protocols are disseminated in the important domain of discontinuing psychiatric drugs. Psychiatric drugs are widely prescribed to treat mental health diagnoses, but, in certain cases, discontinuation might be clinically advisable. Unfortunately, some people experience severe withdrawal symptoms upon discontinuation, even when following medical advice, and thus turn to OHCs for support. We find that collectively-built protocols resemble medical advice and are delivered in a top-down fashion, with staff members being the primary source of informational support. In contrast, all members provide emotional support and exchange advice on navigating the medical system, while many express their distrust of the medical community and pharmaceutical companies. We also discuss the implications of OHCs offering advice outside of the medical system and offer suggestions for how OHCs can collaborate with healthcare providers to advance scientific knowledge and better support people living with medical conditions.
Powerful Privacy Norms in Social Network Discourse
Authors: McDonald Nora , Forte Andrea
Published: October 2021

Social media companies wield power over their users through design, policy, and through their participation in public discourse. We set out to understand how companies leverage public relations to influence expectations of privacy and privacy-related norms. To interrogate the discourse productions of companies in relation to privacy, we examine the blogs associated with three major social media platforms: Facebook, Instagram (both owned by Facebook Inc.), and Snapchat. We analyze privacy-related posts using critical discourse analysis to demonstrate how these powerful entities construct narratives about users and their privacy expectations. We find that each of these platforms often make use of discourse about "vulnerable" identities to invoke relations of power, while at the same time, advancing interpretations and values that favor data capitalism. Finally, we discuss how these public narratives might influence the construction of users' own interpretations of appropriate privacy norms and conceptions of self. We contend that expectations of privacy and social norms are not simply artifacts of users' own needs and desires, but co-constructions that reflect the influence of social media companies themselves.
The Online Authenticity Paradox: What Being "Authentic" on Social Media Means, and Barriers to Achieving It
Authors: Haimson Oliver L. , Liu Tianxiao , Zhang Ben Zefeng , Corvite Shanley
Published: October 2021

People often strive to present themselves authentically on social media, but this may not be possible for everyone. To understand how people view online authenticity, how it relates to social media sharing behaviors, and whether it is achievable, we interviewed 28 social media users who had recently experienced major life transitions. We found that to many participants, online authenticity required presenting a consistent, positive, and "true" self across online and offline contexts. Though most stated that they considered online authenticity achievable, their social media self-disclosure behaviors around life transitions revealed what we call the online authenticity paradox: people strive to achieve online authenticity, yet because doing so requires sharing negative experiences on social media, online authenticity is often unreachable, or is possible only at great personal cost - especially for those with marginalized identities and difficult life experiences.
NudgeCred: Supporting News Credibility Assessment on Social Media Through Nudges
Authors: Bhuiyan Md Momen , Horning Michael , Lee Sang Won , Mitra Tanushree
Published: October 2021

Struggling to curb misinformation, social media platforms are experimenting with design interventions to enhance consumption of credible news on their platforms. Some of these interventions, such as the use of warning messages, are examples of nudges---a choice-preserving technique to steer behavior. Despite their application, we do not know whether nudges could steer people into making conscious news credibility judgments online and if they do, under what constraints. To answer, we combine nudge techniques with heuristic based information processing to design NudgeCred--a browser extension for Twitter. NudgeCred directs users' attention to two design cues: authority of a source and other users' collective opinion on a report by activating three design nudges---Reliable, Questionable, and Unreliable, each denoting particular levels of credibility for news tweets. In a controlled experiment, we found that NudgeCred significantly helped users (n=430) distinguish news tweets' credibility, unrestricted by three behavioral confounds---political ideology, political cynicism, and media skepticism. A five-day field deployment with twelve participants revealed that NudgeCred improved their recognition of news items and attention towards all of our nudges, particularly towards Questionable. Among other considerations, participants proposed that designers should incorporate heuristics that users' would trust. Our work informs nudge-based system design approaches for online media.
Enabling Collaborative Data Science Development with the Ballet Framework
Authors: Smith Micah J. , Cito Jürgen , Lu Kelvin , Veeramachaneni Kalyan
Published: October 2021

While the open-source software development model has led to successful large-scale collaborations in building software systems, data science projects are frequently developed by individuals or small teams. We describe challenges to scaling data science collaborations and present a conceptual framework and ML programming model to address them. We instantiate these ideas in Ballet, the first lightweight framework for collaborative, open-source data science through a focus on feature engineering, and an accompanying cloud-based development environment. Using our framework, collaborators incrementally propose feature definitions to a repository which are each subjected to software and ML performance validation and can be automatically merged into an executable feature engineering pipeline. We leverage Ballet to conduct a case study analysis of an income prediction problem with 27 collaborators, and discuss implications for future designers of collaborative projects.
Parenting, Studying and Working at Home in a Foreign Country: How International Student Mothers in the US Use Screen Media For and With Their Young Children
Authors: Bhatti Neelma , Kotut Lindah , Haqq Derek , Stelter Timothy L. , Saaty Morva , Kelliher Aisling , McCrickard D. Scott
Published: October 2021

The struggle to thrive as a productive student researcher, an attentive parent, and a caring partner can be difficult, particularly for international student parents who are far from home and also possibly burdened with complex cultural expectations, interpersonal dynamics, and institutional biases. Using uses and gratifications theory as a framing mechanism, we describe interviews with twelve international student mothers in the United States who are primary caregivers of children between six months to five years old, focusing on the context of their use of screen media content and devices, the gratifications they seek from their children's use of screen media devices, and the differences in their perceptions about the use of screen media as an educated, non-US parent. Our findings give an initial account of the role of screen based technology in their domestic life with young children, and the limitations of their technological experience. We present four opportunities for designing for this population including technologies for positive distraction, interactive language aids, playful acquaintance tools, and anonymous peer networks for parent support. We conclude by formulating future promising avenues of research in this design space.
Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency
Authors: Yee Kyra , Tantipongpipat Uthaipon , Mishra Shubhanshu
Published: October 2021

Twitter uses machine learning to crop images, where crops are centered around the part predicted to be the most salient. In fall 2020, Twitter users raised concerns that the automated image cropping system on Twitter favored light-skinned over dark-skinned individuals, as well as concerns that the system favored cropping woman's bodies instead of their heads. In order to address these concerns, we conduct an extensive analysis using formalized group fairness metrics. We find systematic disparities in cropping and identify contributing factors, including the fact that the cropping based on the single most salient point can amplify the disparities because of an effect we term argmax bias. However, we demonstrate that formalized fairness metrics and quantitative analysis on their own are insufficient for capturing the risk of representational harm in automatic cropping. We suggest the removal of saliency-based cropping in favor of a solution that better preserves user agency. For developing a new solution that sufficiently address concerns related to representational harm, our critique motivates a combination of quantitative and qualitative methods that include human-centered design.
Exploring the Experiences of Student Volunteer and Student Volunteer Chair Communities at Academic Conferences
Authors: Park Subin , Jung Heejae , Choi Jae Won , Park Jihyeon , Jun Joonyoung , Chun Minki , Jung Hyunggu
Published: October 2021

We report on an interview study with two groups, student volunteers (SV) and student volunteer chairs (SVC), to explore their experiences in contributing to an academic conference. We identified themes regarding their behaviors and perceptions when communicating with each other through an open coding approach. The analysis of the findings reveals that communication issues exist between SV and SVC. While SV faced challenges approaching SVC, SVC had experienced difficulties in finding the right SV to ask for assistance in the tasks. We also found that the information provided by SVC was not sufficient for SV to perform the assigned tasks. Similarly, SVC had a similar issue where they received limited information from former SVC. We present design implications that enable both SV and SVC to share their availability, preferences, and prior knowledge of potential tasks with each other. This is the first study that reports design opportunities to support SV and SVC.