bluuebunny/crossref_metadata_2025 huggingface dataset:
A Systematic Review of Interaction Design Strategies for Group Recommendation Systems
Authors: Alvarado Oscar , Htun Nyi Nyi , Jin Yucheng , Verbert Katrien
Published: November 2022

Systems involving artificial intelligence (AI) are protagonists in many everyday activities. Moreover, designers are increasingly implementing these systems for groups of users in various social and cooperative domains. Unfortunately, research on personalized recommendation systems often reports negative experiences due to a lack of diversity, control, or transparency. Providing a meta-analysis of the interaction design strategies for group recommendation systems (GRS) offers designers and practitioners a departure to address these issues and imagine new interaction possibilities for this context. Therefore, we systematically reviewed the ACM, IEEE, and Scopus digital libraries to identify GRS interface designs, resulting in a final corpus of 142 academic papers. After a systematic coding process, we used descriptive statistics and thematic analysis to uncover the current state of the art regarding interaction design strategies for GRS in six areas: (1) application domains; (2) devices chosen to implement the systems; (3) prototype fidelity; (4) strategies for profile transparency, justification, control, and diversity; (5) strategies for group formation and final group consensus; and, (6) evaluation methods applied in user studies during the design process. Based on our findings, we present an exhaustive typology of interaction design strategies for GRS and a set of research opportunities to foster human-centered interfaces for personalized recommendations in cooperative and social computing contexts.
An Approximation of Freedom: On-demand Therapy and the Feminization of Labor
Authors: Huber Linda , Pierce Casey , Lindtner Silvia
Published: November 2022

Platform labor and gig work have become key sites for understanding a nascent "future of work" hallmarked by informalization and digitization. A growing body of research emphasizes how experiences of platform work are mediated not only by algorithms and user interfaces, but also by gender, race, local cultures as well as labor hierarchies. Drawing from ongoing ethnographic research on the digital transformation of healthcare, we show how therapists' experiences of platform labor are centrally shaped by the historical and ongoing feminization of mental health work. Platforms reinscribe feminized labor conditions that are pervasive in the healthcare industry, and yet platform labor appears as 'useful' to some therapists as they navigate a set of precarious career choices fundamentally structured by feminization. We use the analytic of the stopgap to describe platforms' two-fold reproduction of the status quo: first by offering an approximation of freedom to individual workers, helping to forestall a crisis of unsustainable work conditions; and second by reinscribing the same logics of exploitation in order to make labor scalable. This stopgap analytic reorients the focus away from the impact of the platforms technologies as such, towards the conditions that make stopgap solutions necessary for survival. It also points towards the importance of intervening in the conditions of exclusion and exploitation that help to create a market for platform stopgaps.
Being there to Learn: Narrative Style and Cross-platform Comparison for 360-degree Educational Videos
Authors: Hwang Angel Hsing-Chi , Kim Jaryung , Lobo Shane Neil , Shu Yingyi , Won Andrea Stevenson
Published: November 2022

360-degree videos hold great potential as learning tools that can provide a sense of presence with instructors. However, much remains to be explored about how these videos should be designed. Across three within-subjects studies, we compared the effects of two different narrative styles, monologue and dialogue, on learning experiences in 360-degree videos filmed in an apple orchard. Study 1 presented the 360-degree videos using virtual reality (VR) with head-mounted displays. Study 2 presented the same content in web-based VR using computer screens. Replicating Study 2, Study 3 further examined users' interaction with the video content using on-screen mouse tracking. Across all three studies, participants preferred a monologue format, and also reported higher physical and social presence in this format. Furthermore, greater physical presence correlated with improved recall of informative content. This suggests that many of the benefits of 360-degree videos can be enjoyed even by students and teachers without access to VR headsets, providing more inclusive and accessible learning opportunities.
Building User-oriented Personalized Machine Translator based on User-Generated Textual Content
Authors: Zhang Peng , Guan Zhengqing , Liu Baoxi , Ding Xianghua (Sharon) , Lu Tun , Gu Hansu , Gu Ning
Published: November 2022

Machine Translation (MT) has been a very useful tool to assist multilingual communication and collaboration. In recent years, by taking advantage of the exciting developments of neural networks and deep learning, the accuracy and speed of machine translation have been continuously improved. However, most machine translation methods and systems are data-driven. They tend to select a consensus response represented in training data, while a user's preferred linguistic style, which is important for translation comprehension and user experience, is ignored. For this problem, we aim to build a user-oriented personalized machine translation model in this paper. The model aims to learn each user's linguistic style from the textual content that is generated by her/him (User-Generated Textual Content, UGTC) in social media context and generate personalized translation results utilizing several state-of-the-art deep learning techniques like Transformer and pre-training. We also implemented a user-oriented personalized machine translator using Weibo as a case of the source of UGTC to provide a systematical implementation scheme of a user-oriented personalized machine translation system based on our model. The translator was evaluated by automatic evaluation in combination with human evaluation. The results suggest that our model can generate more personalized, natural and lively translation results and enhance the comprehensibility of translation results, which makes its generations more preferred by users versus general translation results.
Collaboration, Invisible Work, and The Costs of Macrotask Freelancing
Authors: Hulikal Muralidhar Srihari , Rintel Sean , Suri Siddharth
Published: November 2022

Online labour platforms promise efficient, low-friction matching of workers with clients at scale and on-demand. Prior studies on Upwork have shown that the combination of specialized knowledge and expertise, high autonomy, and extent of client-worker engagement makes macrotasks a unique category of 'on-demand' work. However, there is a need to unpack the nature of macrotask work from the freelancers' perspective. Based on a qualitative study of 21 freelancers on Upwork, this paper fills this important gap by delineating how freelancers reason about accomplishing macrotasks, their interaction with clients, the key challenges that they face in various stages of the work process, and the strategies they devise to mitigate the costs and overhead. This paper shows that freelancers perceive accomplishing macrotasks as a collaborative achievement with the client. It also demonstrates that the skill-intensive nature of tasks implies that matching freelancer with the task/client is of enormous importance. It describes three programmatic solutions that the platform offers to facilitate the matching process along with their benefits and limitations. It, then, shows how freelancers seek to minimize the costs and work associated with matching and collaboration through repeat hiring along the benefits they result in. Lastly, the paper highlights how the same set of core issues is mirrored for both the freelancer and client sides of the market, which need to be addressed in order to enhance the ease and effectiveness of client-freelancer collaboration.
Community-in-the-loop: Creating Artificial Process Intelligence for Co-production of City Service
Authors: Wang Ye , Nagireddy Srichakradhar Reddy , Thota Charan Tej , Ho Duy H. , Lee Yugyung
Published: November 2022

Communities have first-hand knowledge about community issues. This study aims to improve the efficiency of social-technical problem-solving by proposing the concept of "artificial process intelligence," based on the theories of socio-technical decision-making. The technical challenges addressed were channeling the communication between the internal-facing and external-facing 311 categorizations. Accordingly, deep learning models were trained on data from Kansas City's 311 system: (1) Bidirectional Encoder Representations from Transformers (BERT) based classification models that can predict the internal-facing 311 service categories and the city departments that handle the issue; (2) the Balanced Latent Dirichlet Allocation (LDA) and BERT clustering (BLBC) model that inductively summarizes residents' complaints and maps the main themes to the internal-facing 311 service categories; (3) a regression time series model that can predict response and completion time. Our case study demonstrated that these models could provide the information needed for reciprocal communication, city service planning, and community envisioning. Future studies should explore interface design like a chatbot and conduct more research on the acceptance and diffusion of AI-assisted 311 systems.
Fathers, Young Children and Technology: Changes in Device Use and Family Dynamics during the COVID-19 UK Lockdown
Authors: Matthews Joe , Burgess Romana , Ellis Bridget , Jenkinson Morgan , Ayobi Amid , Dermott Esther , O'Kane Aisling Ann
Published: November 2022

The COVID-19 2020 lockdown measures altered how families spent time together, with many fathers adopting new household roles and spending more time with their children. This paper contributes an empirical account of technology use and fatherhood during the COVID-19 pandemic, and draws implications for the design of technologies to support fathers. We outline the findings from semi-structured interviews carried out with fathers during lockdown in the UK. Initial interviews (n=19) highlighted challenges in screen viewing, family dynamics, idea generation and self-care. This informed the design of four prototype apps to enrich follow-up interviews (n=12), using these prototypes as prompts to explore the emergent challenges in more depth. The interviews identified significant changes and concerns related to technology use within the context of COVID-19, with new roles for fathers conflicting with traditional ones combined with situational stressors to amplify issues with guilt, shame and self-care related to technology use.
Governing Online Goods: Maturity and Formalization in Minecraft, Reddit, and World of Warcraft Communities
Authors: Frey Seth , Zhong Qiankun , Bulat Beril , Weisman William D. , Liu Caitlyn , Fujimoto Stephen , Wang Hannah , Schweik Charles M.
Published: November 2022

Building a successful community means governing active populations and limited resources. This challenge often requires communities to design formal governance systems from scratch. But the characteristics of successful institutional designs are unclear. Communities that are more mature and established may have more elaborate formal policy systems (as cause or effect of their success). Alternatively, they may require less formalization precisely because of their maturity: because they have more latitude and capacity to select and acculturate new members, or because their reputations encourage greater self-selection. Indeed, scholars often downplay the role that formal rules relative to unwritten rules, norms, and values. But in a community with formal rules, decisions are more consistent, transparent, and legitimate. To understand the relationship of formal institutions to community maturity and governance style, we conduct a large-scale quantitative analysis applying institutional analysis frameworks of self-governance scholar Elinor Ostrom to 80,000 communities across 3 platforms: the sandbox game Minecraft, the MMO game World of Warcraft, and Reddit. We classify communities' written rules according to several institutional taxonomies in order to test predictors of institutional formalization. From this analysis we extract two major findings. First, institutional formalization, the size and complexity of an online community's governance system, is generally positively associated with maturity, as measured by age, population size, or degree of user engagement. Second, we find that online communities employ similar governance styles across platforms, strongly favoring "weak" norms to "strong" requirements. These findings suggest that designers and founders of online communities converge, to some extent independently, on styles of governance practice that are correlated with successful self-governance. With deeper insights into the patterns of successful self-governance, we can help more communities overcome the challenges of self-governance and create for their members powerful experiences of shared meaning and collective empowerment.?
Amazon Echo Show as a Multimodal Human-to-Human Care Support Tool within Self-Isolating Older UK Households
Authors: Soubutts Ewan , Ayobi Amid , Eardley Rachel , McNaney Roisin , Cater Kirsten , O'Kane Aisling Ann
Published: November 2022

Although the use of voice assistants to support older adults at home shows promise, little is known about how the next generation of these devices, which also include displays, can support health and care needs. This question has become more important during COVID-19, when issues of social isolation for older adults have been exacerbated. During social distancing measures in the UK, eleven older adult households (16 participants) were interviewed before receiving an Amazon Echo Show, shortly after receiving it, and after three months of use. We identify ways in which this multi-modal device is used by people in social isolation to support social, care, and information needs in the home. We frame these findings within the growing research area of smart homes for health and care, and provide implications for the adoption, use and acceptance of these devices to support aging in place together.
Engagement or Knowledge Retention: Exploring Trade-offs in Promoting Discussion at News Websites
Authors: McInnis Brian James , Ajmani Leah , Dow Steven P.
Published: November 2022

How does presenting comments in a news article affect the ways that readers engage with and retain information about news? This paper presents results from a controlled experiment investigating effects related to different strategies for promoting discussion at news websites (N=336 participants). The strategies include highlighting specific comments about a data visualization, providing prompts with the comments, and annotating prompts on the visualization. By comparison to a simple list of comments (baseline), our analysis found that annotations contributed to higher levels of participant engagement in the discussion, yet lower levels of knowledge retention related to the article. These findings raise new considerations about whether and how to integrate discussion content into news and points toward future content moderation systems that assist in representing and eliciting discussion at news websites.
Interrogating Data Work as a Community of Practice
Authors: Rothschild Annabel , Meng Amanda , DiSalvo Carl , Johnson Britney , Shapiro Ben Rydal , DiSalvo Betsy
Published: November 2022

We apply Lave & Wenger's construct of a community of practice to identify and position members of the data work community of practice, focusing on members on the periphery who have received less attention - as compared to full practitioners (e.g., data scientists). Reporting on results of interviews with 19 civic workers who perform data work as their main task, we identify an atypical relationship between subject-domain experts (such as our interviewees) and full members of the data work community. Our interviewees may have less computational skill in data work, but they have extensive and varied practices to engage in data contextualization that data scientists and other full community members could learn from. In identifying the attributes of data workers on the periphery, we also hope to call attention to the challenges they face in performing data work in low resources institutions (e.g., governmental, non-profit). Our findings contribute to the larger conversations in human-centered data science about who performs data work and how they go about it, in order to addresses questions of power, fairness, and bias in data-intensive systems.
Intrance: Designing an Interactive Enhancement System for the Development of QA Chatbots
Authors: Feine Jasper , Morana Stefan , Maedche Alexander
Published: November 2022

User input is essential for the successful development of question-and-answer (QA) chatbots. Therefore, interactive development systems are emerging that allow developers to involve test-users in the QA chatbot development process. Although the feasibility and effectiveness of this approach have been demonstrated, there is a lack of knowledge on how to design interactive QA chatbot development systems to increase test-user engagement as well as data quality in this time-consuming and tedious task. To address this research gap, we propose an interactive system design based on the interactivity effects model. We instantiate the proposed design and introduce Intrance, an interactive enhancement system for QA chatbots. Subsequently, we show in two online experiments that the proposed design significantly increases subjective and objective engagement of test-users and has a positive effect on data quality, conceptualized as data completeness and data accuracy. We discuss design implications for an implementation of the proposed design in commercial chatbot development systems.
Learning to Become a Volunteer Counselor: Lessons from a Peer-to-Peer Mental Health Community
Authors: Yao Zheng , Zhu Haiyi , Kraut Robert E.
Published: November 2022

Online peer-to-peer therapy sessions can be effective in improving people's mental well-being. However, online volunteer counselors may lack the expertise and necessary training to provide high-quality sessions, and these low-quality sessions may negatively impact volunteers' motivations as well as clients' well-being. This paper uses interviews with 20 senior online volunteer counselors to examine how they addressed challenges and acquired skills when volunteering in a large, mental-health support community - 7Cups.com. Although volunteers in this community received some training based on principles of active listening and motivational interviewing, results indicate that the training was insufficient and that volunteer counselors had to independently develop strategies to deal with specific challenges that they encountered in their volunteer work. Their strategies, however, might deviate from standard practice since they generally lacked systematic feedback from mentors or clients and, instead, relied on their personal experiences. Additionally, volunteer counselors reported having difficulty maintaining their professional boundaries with the clients. Even though training and support resources were available, they were underutilized. The results of this study have uncovered new design spaces for HCI practitioners and researchers, including social computing and artificial intelligence approaches that may provide better support to volunteer counselors in online mental health communities.
Not Another School Resource Map: Meeting Underserved Families' Information Needs Requires Trusting Relationships and Personalized Care
Authors: Robertson Samantha , Nguyen Tonya , Salehi Niloufar
Published: November 2022

Public school districts across the United States have implemented school choice systems that have the potential to improve underserved students' access to educational opportunities. However, research has shown that learning about and applying for schools can be extremely time-consuming and expensive, making it difficult for these systems to create more equitable access to resources in practice. A common factor surfaced in prior work is unequal access to information about the schools and enrollment process. In response, governments and non-profits have invested in providing more information about schools to parents, for instance, through detailed online dashboards. However, we know little about what information is actually useful for historically marginalized and underserved families. We conducted interviews with 10 low-income families and families of color to learn about the challenges they faced navigating an online school choice and enrollment system. We complement this data with four interviews with people who have supported families through the enrollment process in a wide range of roles, from school principal to non-profit staff ("parent advocates''). Our findings highlight the value of personalized support and trusting relationships to delivering relevant and helpful information. We contrast this against online information resources and dashboards, which tend to be impersonal, target a broad audience, and make strong assumptions about what parents should look for in a school without sensitivity to families' varying circumstances. We advocate for an assets-based design approach to information support in public school enrollment, which would ask how we can support the local, one-on-one support that community members already provide.
Paying Attention to the Algorithm Behind the Curtain: Bringing Transparency to YouTube's Demonetization Algorithms
Authors: Dunna Arun , Keith Katherine A. , Zuckerman Ethan , Vallina-Rodriguez Narseo , O'Connor Brendan , Nithyanand Rishab
Published: November 2022

YouTube has long been a top-choice destination for independent video content creators to share their work. A large part of YouTube's appeal is owed to its practice of sharing advertising revenue with qualifying content creators through the YouTube Partner Program (YPP). In recent years, changes to the monetization policies and the introduction of algorithmic systems for making monetization decisions have been a source of controversy and tension between content creators and the platform. There have been numerous accusations suggesting that the underlying monetization algorithms engage in preferential treatment of larger channels and effectively censor minority voices by demonetizing their content.

In this paper, we conduct a measurement of the YouTube monetization algorithms. We begin by measuring the incidence rates of different monetization decisions and the time taken to reach them. Next, we analyze the relationships between video content, channel popularity and these decisions. Finally, we explore the relationship between demonetization and a channel's view growth rate. Taken all together, our work suggests that demonetization after a video is publicly listed is not a common occurrence, the characteristics of the process are associated with channel size and (in unexplainable ways) video topic, and demonetization appears to have a harsh influence on the growth rate of smaller channels. We also highlight the challenges associated with conducting large-scale algorithm audits such as ours and make an argument for more transparency in algorithmic decision-making.
Persua: A Visual Interactive System to Enhance the Persuasiveness of Arguments in Online Discussion
Authors: Xia Meng , Zhu Qian , Wang Xingbo , Nie Fei , Qu Huamin , Ma Xiaojuan
Published: November 2022

Persuading people to change their opinions is a common practice in online discussion forums on topics ranging from political campaigns to relationship consultation. Enhancing people's ability to write persuasive arguments could not only practice their critical thinking and reasoning but also contribute to the effectiveness and civility in online communication. It is, however, not an easy task in online discussion settings where written words are the primary communication channel. In this paper, we derived four design goals for a tool that helps users improve the persuasiveness of arguments in online discussions through a survey with 123 online forum users and interviews with five debating experts. To satisfy these design goals, we analyzed and built a labeled dataset of fine-grained persuasive strategies (i.e., logos, pathos, ethos, and evidence) in 164 arguments with high ratings on persuasiveness from ChangeMyView, a popular online discussion forum. We then designed an interactive visual system, Persua, which provides example-based guidance on persuasive strategies to enhance the persuasiveness of arguments. In particular, the system constructs portfolios of arguments based on different persuasive strategies applied to a given discussion topic. It then presents concrete examples based on the difference between the portfolios of user input and high-quality arguments in the dataset. A between-subjects study shows suggestive evidence that Persua encourages users to submit more times for feedback and helps users improve more on the persuasiveness of their arguments than a baseline system. Finally, a set of design considerations was summarized to guide future intelligent systems that improve the persuasiveness in text.
Burnout and the Quantified Workplace: Tensions around Personal Sensing Interventions for Stress in Resident Physicians
Authors: Adler Daniel A. , Tseng Emily , Moon Khatiya C. , Young John Q. , Kane John M. , Moss Emanuel , Mohr David C. , Choudhury Tanzeem
Published: November 2022

Recent research has explored computational tools to manage workplace stress via personal sensing, a measurement paradigm in which behavioral data streams are collected from technologies including smartphones, wearables, and personal computers. As these tools develop, they invite inquiry into how they can be appropriately implemented towards improving workers' well-being. In this study, we explored this proposition through formative interviews followed by a design provocation centered around measuring burnout in a U.S. resident physician program. Residents and their supervising attending physicians were presented with medium-fidelity mockups of a dashboard providing behavioral data on residents' sleep, activity and time working; self-reported data on residents' levels of burnout; and a free text box where residents could further contextualize their well-being. Our findings uncover tensions around how best to measure workplace well-being, who within a workplace is accountable for worker stress, and how the introduction of such tools remakes the boundaries of appropriate information flows between worker and workplace. We conclude by charting future work confronting these tensions, to ensure personal sensing is leveraged to truly improve worker well-being.
Consensus Building in On-Line Citizen Science
Authors: Sharma Nirwan , Colucci-Gray Laura , van der Wal René , Siddharthan Advaith
Published: November 2022

A number of initiatives invite members of the public to perform online classification tasks such as identifying objects in images. These tasks are crucial to numerous large-scale Citizen Science projects in different disciplines, with volunteers using their knowledge and online support tools to, for example, identify species of wildlife or classify galaxies by their shapes. However, for complex classification tasks, such as this case study on identifying species of bumblebee, reaching an agreement between volunteers - or even between experts~-~may require consensus-building processes. Collaboration and teamwork approaches to problem solving and decision-making have been widely documented to improve both task performance and user learning in the real world. Most of these processes and projects are mediated online through feedback delivered in an asynchronous manner, and this article thus addresses a central research question: How do participants involved in species identification tasks respond to different forms of feedback provided in online collaboration, designed to support peer-learning and improve task performance? We tested four different approaches to feedback within a collaboration task, where participants reviewed their previously annotated data based on information curated from their peers on a long running online citizen science initiative. The selected interfaces have a strong foundation in social science and psychology literature and can be applied to citizen science practices as well as other online communities. Results showed that while all four approaches increased accuracy, there were differences based on the types of consensus that existed before collaboration. Such differences highlight the usefulness of different forms of feedback during collaboration for increasing data accuracy of identification and furthering users' expertise on identification tasks. We found that anonymised and goal-directed free text comments posted on social learning interfaces were most effective in improving data accuracy as well as creating opportunities for peer-learning, particularly where the species identification task was more difficult. This study has significant implications for extending the practice of citizen science across formal and informal learning environments and reaching out to a variety of users.
Decaying Photos for Enhanced Privacy: User Perceptions Towards Temporal Redactions and 'Trusted' Platforms
Authors: Pias Sabid Bin Habib , Ahmad Imtiaz , Akter Taslima , Kapadia Apu , Lee Adam J.
Published: November 2022

With the rising popularity of photo sharing in online social media, interpersonal privacy violations, where one person violates the privacy of another, have become an increasing concern. Although applying image obfuscations can be a useful tool for improving privacy when sharing photos, prior studies have found these obfuscation techniques adversely affect viewers' satisfaction. On the other hand, ephemeral photos, popularized by apps such as Snapchat, allow viewers to see the entire photo, which then disappears shortly thereafter to protect privacy. However, people often use workarounds to save these photos before deletion. In this work, we study people's sharing preferences with two proposed 'temporal redactions', which combines ephemerality with redactions to allow viewers to see the entire image, yet make these images safe for longer storage through a gradual or delayed application of redaction on the sensitive portions of the photo. We conducted an online experiment (N=385) to study people's sharing behaviors in different contexts and under different levels of assurance provided by the viewer's platform (e.g., guaranteeing temporal redactions are applied through the use of 'trusted hardware'). Our findings suggest that the proposed temporal redaction mechanisms are often preferred over existing methods. On the other hand, more efforts are needed to convey the benefits of trusted hardware to users, as no significant differences were observed in attitudes towards 'trusted hardware' on viewers' devices.
Hostile Ecologies
Authors: Tandon Udayan , Khovanskaya Vera , Arcilla Enrique , Hussein Mikaiil Haji , Zschiesche Peter , Irani Lilly
Published: November 2022

This paper describes how the contemporary technology innovation ecology is hostile to community-driven design. These hostilities are important to understand if we want to intervene in the policy landscape of technology innovation to support viable alternatives to big tech consolidation and more democratic ways of developing and maintaining technology. We contribute a thick description of the hostile ecologies faced by transportation workers, community organizers, and allied technology researchers as they work toward building a cooperatively-owned taxi business with a digital dispatching technology. Our findings show that the hostile innovation ecology manifests as constrained access to resources, an inequitable regulatory framework, diminished agency in the software design process, and limits to the will of our community partners. We discuss the paths toward innovation for United Taxi Workers San Diego as compared with transportation network companies (e.g. Lyft, Uber) in terms of access to funding, regulation, labor, expertise, and market. We argue that a critical examination of institutions and policies in the innovation ecology is a necessary step toward charting fair, equitable, and community-strengthening pathways for technology innovation in the future.
LAGH: Towards Asymmetrical Collaborative Bodily Play between 1st and 2nd Person Perspectives
Authors: Chen Bo-Han , Wong Sai-Keung , Chang Wei-Che , Fan Roy Ping-Hao
Published: November 2022

This paper investigated to what extent social interactions and empathy of users could be induced when different control mechanisms were used in an asymmetric collaboration. We conducted a user study to explore the user experience under one decentralized and two centralized control conditions via using the proposed two-player asymmetric collaborative bodily play, LAGH, which supports perspective-taking through the integration with the first- and second-perspectives and shared objects. The two players have complementary views and controls to each other in an immersive environment. The results indicate that participant pairs were encouraged by the asymmetric collaboration interface to share their emotional and physiological perspectives with each other. When their control abilities were balanced, they were more motivated to perform information sharing and interact with each other, thereby enhancing closeness and stimulating empathy. Furthermore, users could improve the collaboration efficiency.
Measuring the Prevalence of Anti-Social Behavior in Online Communities
Authors: Park Joon Sung , Seering Joseph , Bernstein Michael S.
Published: November 2022

With increasing attention to online anti-social behaviors such as personal attacks and bigotry, it is critical to have an accurate accounting of how widespread anti-social behaviors are. In this paper, we empirically measure the prevalence of anti-social behavior in one of the world's most popular online community platforms. We operationalize this goal as measuring the proportion of unmoderated comments in the 97 most popular communities on Reddit that violate eight widely accepted platform norms. To achieve this goal, we contribute a human-AI pipeline for identifying these violations and a bootstrap sampling method to quantify measurement uncertainty. We find that 6.25% (95% Confidence Interval [5.36%, 7.13%]) of all comments in 2016, and 4.28% (95% CI [2.50%, 6.26%]) in 2020, are violations of these norms. Most anti-social behaviors remain unmoderated: moderators only removed one in twenty violating comments in 2016, and one in ten violating comments in 2020. Personal attacks were the most prevalent category of norm violation; pornography and bigotry were the most likely to be moderated, while politically inflammatory comments and misogyny/vulgarity were the least likely to be moderated. This paper offers a method and set of empirical results for tracking these phenomena as both the social practices (e.g., moderation) and technical practices (e.g., design) evolve.
PlanHelper
Authors: Liu Chengzhong , Huang Zeyu , Liu Dingdong , Zhou Shixu , Peng Zhenhui , Ma Xiaojuan
Published: November 2022

Community-based Question Answering (CQA) platforms can provide rich experience and suggestions for people who seek to construct Activity Plans (AP), such as bodybuilding or sightseeing. However, answer posts in CQA platforms could be too unstructured and overwhelming to be easily applied to AP construction, as validated by our formative study for understanding relevant user challenges. We therefore proposed an answer-post processing pipeline, based on which we built PlanHelper, a tool assisting users in processing the CQA information and constructing AP interactively. We conducted a within-subject study (N=24) with a Quora-like interface as the baseline. Results suggested that when creating AP with PlanHelper, users were significantly more satisfied with the informational support and more engaged during the interaction. Moreover, we performed an in-depth analysis on the user behaviors with PlanHelper and summarized the design considerations for such supporting tools.
Privacy Research with Marginalized Groups
Authors: Sannon Shruti , Forte Andrea
Published: November 2022

People who are marginalized experience disproportionate harms when their privacy is violated. Meeting their needs is vital for developing equitable and privacy-protective technologies. In response, research at the intersection of privacy and marginalization has acquired newfound urgency in the HCI and social computing community. In this literature review, we set out to understand how researchers have investigated this area of study. What topics have been examined, and how? What are the key findings and recommendations? And, crucially, where do we go from here? Based on a review of papers on privacy and marginalization published between 2010-2020 across HCI, Communication, and Privacy-focused venues, we make three main contributions: (1) we identify key themes in existing work and introduce the Privacy Responses and Costs framework to describe the tensions around protecting privacy in marginalized contexts, (2) we identify understudied research topics (e.g., race) and other avenues for future work, and (3) we characterize trends in research practices, including the under-reporting of important methodological choices, and provide suggestions to establish shared best practices for this growing research area.
The Data-Production Dispositif
Authors: Miceli Milagros , Posada Julian
Published: November 2022

Machine learning (ML) depends on data to train and verify models. Very often, organizations outsource processes related to data work (i.e., generating and annotating data and evaluating outputs) through business process outsourcing (BPO) companies and crowdsourcing platforms. This paper investigates outsourced ML data work in Latin America by studying three platforms in Venezuela and a BPO in Argentina. We lean on the Foucauldian notion of dispositif to define the data-production dispositif as an ensemble of discourses, actions, and objects strategically disposed to (re)produce power/knowledge relations in data and labor. Our dispositif analysis comprises the examination of 210 data work instruction documents, 55 interviews with data workers, managers, and requesters, and participant observation. Our findings show that discourses encoded in instructions reproduce and normalize the worldviews of requesters. Precarious working conditions and economic dependency alienate workers, making them obedient to instructions. Furthermore, discourses and social contexts materialize in artifacts, such as interfaces and performance metrics, limiting workers' agency and normalizing specific ways of interpreting data. We conclude by stressing the importance of counteracting the data-production dispositif by fighting alienation and precarization, and empowering data workers to become assets in the quest for high-quality data.
Youths' Perceptions of Data Collection in Online Advertising and Social Media
Authors: Goray Cami , Schoenebeck Sarita
Published: November 2022

This project illuminates what data youth believe online advertisers and social media companies collect about them. We situate these findings within the context of current advertising regulations and compare youth beliefs with what data social media companies report collecting based on their privacy policies. Through interviews with 21 youth ages 10-17 in the United States, we learn that participants are largely aware of how their interactions on the website or app are used to inform personalized content. However, certain types of information like geolocation or how long data is retained is less clear to them. We also learn about what school and family factors influence youth to adopt apps and websites. This work has implications for design and policy related to companies' personal data collection and targeted advertising, especially for youth.
"A Reasonable Life"
Authors: Steup Rosemary , White Paige , Dombrowski Lynn , Su Norman Makoto
Published: November 2022

Past CSCW work has examined the role of temporal rhythms in cooperative work and has identified alignment work--the work required to bring dissonant rhythms into alignment--as an important aspect of large-scale collaboration. We ask instead how individual workers interact with temporal rhythms to sustain the conditions that make their work possible--not aligning rhythms, but attuning them. This paper draws on interviews with farmer-knowledge workers, people who engage with both farm work (the work of growing food or raising animals for food, on a commercial or non-commercial basis) and computer-based knowledge work. We identify three ways that farmer-knowledge workers interact with natural and structural rhythms to construct sustainable work-lives: anchoring (tying oneself to a particular rhythm to create accountability and structure), decoupling (loosening or cutting ties with a rhythm to create flexibility), and gap-filling (interweaving complementary rhythms to create balance). Together, these practices constitute attunement work.
Conversational Agents for Information Retrieval in the Education Domain
Authors: Schmitt Anuschka , Wambsganss Thiemo , Leimeister Jan Marco
Published: November 2022

Text-based conversational agents (CAs) are widely deployed across a number of daily tasks, including information retrieval. However, most existing agents follow a default design that disregards user needs and preferences, ultimately leading to a lack of usage and an unsatisfying user experience. To better understand how CAs can be designed in order to lead to effective system use, we deduced relevant design requirements from both literature and 13 user interviews. We built and tested a question-answering, text-based CA for an information retrieval task in an education scenario. Results from our experimental test with 41 students indicate that following a user-centered design has a significant positive effect on enjoyment and trust in a CA as opposed to deploying a default CA. If not designed with the user in mind, CAs are not necessarily more beneficial than traditional question-answering systems. Beyond practical implications for effective CA design, this paper points towards key challenges and potential research avenues when deploying social cues for CAs.
Adherence to Misinformation on Social Media Through Socio-Cognitive and Group-Based Processes
Authors: Efstratiou Alexandros , De Cristofaro Emiliano
Published: November 2022

Previous work suggests that people's preference for different kinds of information depends on more than just accuracy. This could happen because the messages contained within different pieces of information may either be well-liked or repulsive. Whereas factual information must often convey uncomfortable truths, misinformation can have little regard for veracity and leverage psychological processes which increase its attractiveness and proliferation on social media. In this review, we argue that when misinformation proliferates, this happens because the social media environment enables adherence to misinformation by reducing, rather than increasing, the psychological cost of doing so. We cover how attention may often be shifted away from accuracy and towards other goals, how social and individual cognition is affected by misinformation and the cases under which debunking it is most effective, and how the formation of online groups affects information consumption patterns, often leading to more polarization and radicalization. Throughout, we make the case that polarization and misinformation adherence are closely tied. We identify ways in which the psychological cost of adhering to misinformation can be increased when designing anti-misinformation interventions or resilient affordances, and we outline open research questions that the CSCW community can take up in further understanding this cost.
Aligning Eyes between Humans and Deep Neural Network through Interactive Attention Alignment
Authors: Gao Yuyang , Sun Tong Steven , Zhao Liang , Hong Sungsoo Ray
Published: November 2022

While Deep Neural Networks (DNNs) are deriving the major innovations through their powerful automation, we are also witnessing the peril behind automation as a form of bias, such as automated racism, gender bias, and adversarial bias. As the societal impact of DNNs grows, finding an effective way to steer DNNs to align their behavior with the human mental model has become indispensable in realizing fair and accountable models. While establishing the way to adjust DNNs to "think like humans'' is in pressing need, there have been few approaches aiming to capture how "humans would think'' when DNNs introduce biased reasoning in seeing a new instance. We propose Interactive Attention Alignment (IAA), a framework that uses the methods for visualizing model attention, such as saliency maps, as an interactive medium that humans can leverage to unveil the cases of DNN's biased reasoning and directly adjust the attention. To realize more effective human-steerable DNNs than state-of-the-art, IAA introduces two novel devices. First, IAA uses Reasonability Matrix to systematically identify and adjust the cases of biased attention. Second, IAA applies GRADIA, a computational pipeline designed for effectively applying the adjusted attention to jointly maximize attention quality and prediction accuracy. We evaluated Reasonability Matrix in Study 1 and GRADIA in Study 2 in the gender classification problem. In Study 1, we found applying Reasonability Matrix in bias detection can significantly improve the perceived quality of model attention from human eyes than not applying Reasonability Matrix. In Study 2, we found using GRADIA significantly improves (1) the human-assessed perceived quality of model attention and (2) model performance in scenarios where the training samples are limited. Based on our observation in the two studies, we present implications for future design in the problem space of social computing and interactive data annotation toward achieving a human-centered steerable AI.
Authenticity for Rent? Airbnb Hosts and the Commodification of Urban Displacement
Authors: Stewart Remy
Published: November 2022

Airbnb as an online short-term rental marketplace has had significant impact on the housing dynamics of major metropolitan regions. This work examines Airbnb's relationship with urban residential displacement through the dual phenomena of neighborhood gentrification as the loss of low-income households along with neighborhood exclusion as the preservation of elite enclaves without affordable housing options available to low-income households. I feature approximately 15 thousand unique San Francisco Airbnb listings spanning from 2016 to 2021 to investigate how Airbnb hosts employ cultural rhetoric associated with urban displacement to advertise their units' neighborhoods. I employ a computational mixed-methods design by combining natural language processing techniques with qualitative content analysis to examine how hosts differ in their cultural representations of their listing's neighborhoods by its relationship with residential displacement. My findings highlight the pervasiveness of rhetoric focusing on the authenticity and diversity of gentrifying neighborhoods contrasted to the safety and family-friendliness of exclusionary neighborhoods as a key strategy Airbnb hosts use to commercialize their listings. These findings underscore the reinforcing dynamics between regional Airbnb markets and urban displacement, with subsequent implications towards the ongoing marginalization of longstanding residential communities within contemporary cities.