Urban cycling is often a solitary pursuit, as many cities do not provide infrastructure to facilitate social cycling, such as protected bike lanes. Negotiating congested streets can be stressful, even under the best conditions. The challenges of obstacle avoidance are amplified at night, when reduced visibility increases the risk of collision. In order to promote social cycling at night, we introduce [Bike] Swarm. Inspired by the natural synchrony emerging in swarms of nocturnal insects such as crickets and fireflies, [Bike] Swarm automatically inducts cyclists into a collective of nearby riders. A removable sheath of white LED illuminators is attached to the bicycle frame, providing a baseline level of illumination. When cyclists enter a 5-meter radius of other [Bike] Swarm riders, their bicycle lights begin pulsating in unison, creating a visually unified presence and extending membership in a self-organizing community that is increasing safety cooperatively.
We demonstrate MIDST, a system we developed to support stigmergic coordination in data-science teams, that is, coordination supported by a shared work product. To improve coordination, the system supports modularization of an analysis as a workflow, distributed code development and sharing and tracking of task status through a web application.
This demo presents ModelLens, an interactive system designed to support data science teams in their model improvement practices. A central component of improving models is analyzing model errors, often incorporating feedback on the model's precision and recall performance (e.g., differences between the model's predicted label and its actual label). Today, error analysis is typically an ad hoc, team-specific process, largely accomplished through spreadsheets. ModelLens offers a unified view of feedback from multiple sources, the ability for data scientists to explore the context of an individual feedback instance, as well as a customizable ontology to enable collaborative and systematic annotations of model errors.
Understanding natural human behaviours in-the-wild are highly valued in HCI and CSCW communities. However, it is challenging to collect and analyze the field data due to public privacy. We present Skeletonographer, the tool that supports anonymous digital ethnography studies using skeletonized representations of people. Our system utilizes computer vision techniques to generate and visualize skeletons from video. Users can play back the skeleton data similar to a video player, and apply custom labels for analysis. We applied this tool to our project as a case study, and proved its feasibility to act as an ethnographic research tool.
An increasing number of American adults are consuming news on social media platforms. However, these digital platforms employ personalized recommendation algorithms to increase user engagement. This selective exposure to information leads to the formation of filter bubbles where the users are constantly fed information in line with their ideological views. In this paper, we aim at bursting these filter bubbles through diverse exposure. We discuss the design of a Google Chrome extension, Pop, which augments user's Twitter feeds with news tweets from agencies of different ideological standings.
Assistive technologies have been developed to make informal caregiving more feasible, but are typically privacy invasive, unaffordable, or inconvenient. To address this gap, we present Carebit, a cross-platform app for iOS and Android users that allows caregivers to be notified based on preset thresholds about their loved one (caregivee). We have designed Carebit to protect the privacy of the caregivee while allowing the caregiver to conveniently check up on them through their phone. Carebit also provides an app for the caregivee to be able to communicate with their caregiver.
In the midst of the current boom in ethical principles, frameworks and guidelines for emerging applications of artificial intelligence (AI), it is difficult to assess how these translate into the context of real-world applications. Through interviews and ethnography, my research explores AI specialists' accounts of navigating the ethical and social impact of their work, examining and providing insight into the various interactions impacting ethical decision-making in AI system development. Having investigated behavior of AI specialists as proactive moral agents, the work then aims to explore how we can support meaningful applications of ethics in system design and development.
This research examines the challenges of constructing and using digital representations (data doubles) that stand-in for individual clients in the human services fields of homelessness and HIV/AIDS. By focusing on the multi-stakeholder context of often-stigmatized human services, this research develops a better understanding of the challenges and opportunities in using data doubles for collaborative decision-making. In an era of extensive data collection and analysis in many areas of society, this research contributes more broadly to the design and use of information systems to support equitable decisions.
My dissertation focuses on the role of collaboration in depression self-management. Mental health self-management is often viewed as an individual activity, yet individual-focused solutions (e.g., self-tracking) often miss important support from social networks. My preliminary study investigated self-management of individuals with depression. Participants used a variety of technology channels to connect with others to express their moods and create solutions to challenges. However, these connections can also be a source of conflict. Next, I will investigate collaborations between individuals managing depression and their close ties to understand how these activities play out in-person and through technology.
Smart home devices have gained widespread adoption and are expected to grow rapidly in usage. However, they introduce new challenges to the privacy of the user and the security of the smart society. This paper provides an overview of ongoing dissertation research towards understanding the privacy concerns of smart home device (SHD) users and non-users, a comprehensive analysis of vulnerabilities of SHDs, and the development of smart home usable privacy (SHUP) framework.
In this work I aim to unpack how algorithmic tools measure and quantify human behavior. I explore algorithms' appropriate contexts of use and their connections to algorithmic bias. This thesis ultimately explores a method of representing subjects of analysis in the design and evaluation of an algorithmic tool. Specifically, I examine ways in which members of an underrepresented group-- older adults-- can be directly incorporated in the creation of an algorithmic tool.
Contributors to online crowdsourcing systems generally work independently on pieces of a product. Task interdependencies may require collaboration, mostly implicitly, to develop a final product. Even individuals engaged in a group conversation may not stay with the same group for long, i.e., the group is an "occasional group". Further, occasional group interactions are often not well supported by the platforms. These factors particularly impact the process of group learning. I plan to develop a model of group learning in occasional groups and to test the model by using it to design a knowledge-based chatbot to improve learning in occasional groups.
Immigrants use technologies for socialization in new countries, but HCI scholars have not explored this research space. In my dissertation, I am adopting the social exchange theory to explore this research space. My first study suggested that trust is the key to addressing immigrants' concerns about social exchange through technologies. Following this finding, my ongoing work will be focused on designing interventions to address these trust-related concerns. My dissertation should shed light on inclusive design supporting immigrants' socialization, and inform research on other newcomers (e.g., domestic migrants and refugees) and specific forms of resource exchange (e.g., ridesharing and language exchange).
Using critical ethnographic methods, this dissertation study analyzes stakeholder experiences of virtual k-12 schools in the U.S. and their relation to patterns of marginalization in education and labor systems. Combining a focus on distributed labor in the platform economy with an understanding of schools as powerful conduits to well-being, anchors of community cohesion and growth, and sites of prolonged institutional violence, I posit that school digitization may provide an escape route for some from structural social harms, yet depending on its design and implementation, may also threaten to re-inscribe and extend existing hierarchies and create new challenges for social justice.
Interactions with robots in organizational and community settings are presented in research mostly as an aggregate of individual interactions, rather than as the product of the social dynamics of the community contexts. Through my Ph.D. research, I have identified the need for a community perspective and conducted field studies to understand the conceptual and methodological relevance of community-centric approaches for Human-Robot Interactions (HRI). I aim to derive a value framework for systematic study of the use and design of robots for communities. A combination of socio-technical and community-centric approaches inform my early conceptualization of 'Community Robots'(CR).
The increasing volume of text data is challenging the cognitive capabilities of experts. Machine learning and crowdsourcing present opportunities for large-scale distributed sensemaking, but we must overcome the challenge of modeling the holistic process so that many distributed agents can contribute to suitable components asynchronously and meaningfully. My dissertation work is devoted to addressing this challenge from a crowdsourcing perspective. Specifically, I study 1) how novice crowds can build theories from raw datasets without expert intervention; 2) what bottlenecks exist for crowds in the theory-building process; and 3) how previous crowd analyses can be refined to enable iterative crowdsourced sensemaking.
Human-nature interaction, joining the agendas of sustainable interaction design and nonanthropocentric HCI, takes up the challenges presented by climate change and environmental crisis. My dissertation focuses on studying sustainable farming practices through posthuman concepts to experiment ways of supporting multispecies collaborative works. Specifically, my research aims to discover and develop alternative design paradigms and practices for sustaining both human-nature collaboration, cohabitation, and co-creation.
My doctoral research aims to understand if civic data infrastructures can be designed and operationalized to serve the data equity and advocacy needs of minoritized communities. I do this using a combination of participatory and ethnographic research methods to investigate the infrastructural elements of the Communities Who Know Data Dashboard. This research will be useful to both designers and users of civic data infrastructures as it will account for the socio, material, political, organizational and technological elements that are necessary for a community to learn, use and build civic data infrastructures that best serve their needs.
Whether through television, newspapers, or more increasingly through Social Networking Sites (SNS), journalistic coverage of current events have long played a significant role in mediating knowledge and information to the public. The platforms and channels through which news is produced and consumed shape how the public talk about current issues, exemplifying the critical link between democratic discourse and the press. However, with the advent of social media, the display of online news content has increasingly changed over the years. This implies that the conditions and avenues through which audiences make sense of mediated politics through news have possibly changed as well. This is the premise that motivates my work. In my dissertation, I examine how social media news consumption impacts the viability of online political deliberation around news content. Specifically, I investigate how civil discourse is shaped in relation to political hashtags in the headlines and texts of social media news posts. I use both qualitative and computational (natural language processing) methods on publicly available social media news comments and survey data collected through large-scale experiments.
New forms of digitally-mediated labor pose several privacy concerns for workers. However, due to asymmetries in power and information, workers may be limited in the degree to which they can protect their privacy. Through my doctoral research, I examine how issues of privacy play out in the gig economy, and how they impact workers' experiences as well as how and what work gets completed.
In recent years, disruptive behaviors ranging from targeted harassment to increased prevalence of hate groups online have become a priority for researchers and practitioners to address. In my work I have analyzed moderation practices in online communities both qualitatively and quantitatively, studied the relationships between users and automated moderation agents, and developed tools to strengthen communities' identities and empower them to self-moderate more effectively. In my future work I will focus in more depth on the role of platforms and automated agents in shaping communities and their moderation practices with the goal of creating more positive, supportive spaces online.
Research on health practices, particularly around patient records in CSCW and HCI largely has underlying assumptions like individualism, emotional minimalism, recording clinical evidence, etc. We break away from this over simplification, that forgoes cultural categories like beliefs and assumptions about illness, technology and, gender roles, etc. This paper provides overview of ongoing dissertation research seeking to examine socio cultural aspects evoked within obstetrics and gynaecology (OB/GYN) outpatient consultation and how this leads to variations in EMR use. We have identified case study as suitable research strategy and principles of grounded approach will be applied while analyzing data. We hope to bring more nuanced understanding of cultural mechanisms in creating variations in use of health IT.
The increasing popularity of smart home devices has raised significant privacy issues. This project is motivated by two gaps in the literature. First, research has been focusing on smart home users' privacy perceptions rather than other stakeholders in a smart home considering various social factors. These social factors may include social relationships (e.g., guests and homeowners) and various social contexts (e.g., temporary residency). Second, most existing privacy mechanisms for smart homes are either proposed or implemented by researchers without users' input or designed solely for smart home users. This project takes the first attempt to fill these gaps.
Many decisions about social, economic, and personal life are heavily data-driven. At the same time, data has become increasingly quantified, and available to people and institutions in positions of power, often with little introspection or reflection on its positive uses or harmful misuses. This panel will inspect CSCW's role in identifying constructive and appropriate uses of data and its responsibility for protecting against harms and inequalities perpetuated by misuse. The panel will present a series of debates about quantification of data, data surveillance, organizational data use, and policy making. An overarching theme throughout the set of debates is interrogating CSCW's role in extending critical scholarship on power and justice towards academic, policy, and industry impact.
As people increasingly innovate outside of formal R&D departments, individuals take on the responsibility of attracting, managing, and protecting social, financial, human, and information capital. With internet technology playing a central role in how individuals work together to produce something that they could not produce alone, it is necessary to understand how technologies are shaping the innovation process from start to finish. We bring together human-computer interaction researchers and industry leaders who have worked with people and platforms designed to support collective innovation across diverse domains. We will discuss the current and future research on the role of platforms in collective innovation, including topics in social computing, crowdsourcing, peer production, online communities, gig economy, & online marketplaces.
For over 20 years, the academic human-computer interaction (HCI) and computer supported collaborative work (CSCW) communities have conducted research on user-facing hardware, software, and social media platforms dominated by a few key firms. Even as the contributions made by these scholars have shaped the development of these technologies, so too does industry funding play a key role in convening and supporting our community. This panel brings together HCI researchers for a reflective conversation on industry funding support for HCI.
Research in the fields of Computer-Supported Cooperative Work (CSCW) and Human-Computer Interaction (HCI) is increasingly examining topics surrounding women's health. Many of these works convey that women's health cannot be isolated from various social and affective aspects of health, strongly suggesting a more holistic engagement with women's wellbeing instead. Extending beyond wellbeing, we find that much prior work is committed to promoting women's empowerment along various dimensions, such as being more in control of their bodies, having the freedom to express themselves online, being able to secure access to computing education, among other goals. In this panel, we bring together the voices of members of the CSCW community who actively care about women's health, wellbeing, and empowerment in the worlds of research, design, and practice.
Conversational agents (e.g., chatbots, virtual agents) are becoming part of our everyday lives, from personal assistants, social companions to team support. In recent years, there has been a wave of research on this topic in both the HCI and AI communities, though the two communities approach the research and development of conversational agents with different foci and distinct methodologies. In this panel, we bring scholars in the HCI and AI communities together to share their perspectives and identify research topics that the two communities can collaborate on. We invite the CSCW community to reflect on the phenomena, challenges and research opportunities in human-agent communication, as well as the roles of agents in improving our personal, social and organizational lives.
Screen size has potential to affect the results of crowdsourced image classification and impact data quality, even for simple tasks like determining whether an animal is present in an image or not. We evaluated the rates at which volunteers judged images to be blank for six Snapshot Safari citizen science projects on the Zooniverse platform, and found that in all but one project, people using mobile devices were more likely to say an image was blank than those using a desktop computer. Qualitative evaluation further demonstrated that screen brightness can also play a role in reliability for volunteers using mobile devices. These findings can be taken into consideration in the design and testing of apps and platforms for crowdsourced image classification to support reliability and data quality.
Teamwork skills are crucial to college students, both at university and afterwards. However, few tools exist to monitor student teamwork and to help students develop teamwork skills. We present a tool which collects the interactions of students who are using online platforms to complete a sustained task as a team; conducts a range of analyses of these data; and then presents information about team and team member behaviors in real time on a digital dashboard. This dashboard provides instructors with a user-friendly picture of team and team-member dynamics, which can also be made available, as appropriate, to both teams and team members. While some behaviors have been shown to be (or are self-evidently) beneficial or harmful to team performance, these data and analyses also make possible exploration of whether less obvious behaviors affect team outcomes and performance.
Location-Based Social Networks (LBSNs) have been used in many applications including crowd estimation and event detection. Snapchat is an LBSN that offers real-time activity monitoring around the world through its Snap Map using aggregation of user-submitted snaps. In this paper, we used Snap Map to study the crowd behavior at the Grand Holy Mosque (GHM) in Mecca during the holy month of Ramadan. We tracked activities in specific locations around the GHM for a month. We then used this temporal/spatial data to study crowd density and the behavior of GHM's visitors and compared them with the reported figures published by GHM's officials. Initial results show that Snap Map can be useful in understanding crowd behavior and detecting certain events, and may potentially be used in crowd size prediction.
Recent press articles have reported that Internet-connected consumer "IoT" devices can impact interpersonal relationships among home residents and visitors. However, academic research into the variety and extent of these effects remains limited. In this study, we conduct semi-structured interviews with individuals from multi-occupant households in order to understand how home IoT devices have affected their relationships. Our results will illustrate the interpersonal costs and benefits of home IoT devices and inform recommendations for the design of future IoT technologies.
Social matching systems, while popular for dating and platonic connections, can also play a role in the workplace, particularly for initiating collaborations between professionals from traditionally disparate fields of practice. In this paper we present a profile page design to support university faculty in self-presenting to nonacademic partners in their local community for collaborative research opportunities, as informed by a focus group study. We then discuss preliminary insight from a survey to faculty prompting them to create their profile page and provide feedback on its design. The created profiles indicate that faculty embrace profile pictures as tools for conveying their expertise and resources rather than their physical appearance. Furthermore, functionality to link the profiles of users who are currently working together should be considered as a way to convey combined collaboration potential.
In recent years, there has been an unprecedented growth in content that is shared and presented on social media platforms. Along with this growth, however, there is an increasing concern over the lack of control social media users have on the content they are shown by invisible algorithms. In this paper, we introduce Gobo, an open-source social media browser system that enables users to manage and filter content from multiple platforms on their own. Gobo aims to help users control what's hidden from their feeds, add perspectives from outside their network to help them break filter bubbles, and explore why they see certain content on their feed. Through an iterative design process, we've built and deployed Gobo in the wild and conducted a pilot study in the form of a survey to understand how the users respond to the shift of control from invisible algorithms to themselves. Our initial findings suggest that Gobo has potential to provide an alternate design space to enhance control, transparency, and explainability in social media.
The English language Wikipedia is notable for its large number of articles. However, 288 other active language editions of Wikipedia have also developed through the intricate interactions of contributing editors. While the editor interactions in the English Wikipedia have been researched extensively, these other language editions remain understudied. To understand how editors currently come to consensus in article building in the Spanish language, a team of researchers has leveraged an existing English framework that depicts how power and policies play a role in mass collaboration. Using this English language framework, we are utilizing qualitative coding methods to build a unique model of the editor interactions on the Spanish language Wikipedia (ES). The results of this study will help contribute to a deeper understanding of how a framework in a different language edition of Wikipedia varies from the English. Our preliminary findings show that the power plays utilized in the Spanish Wikipedia talk pages differ in type and amount from power plays identified in the talk pages in the English language Wikipedia (EN), suggesting different forms of editor interactions and facilitation strategies across this multilingual platform.
Conversational agents (CAs) with voice interfaces are becoming ubiquitous and are routinely used by a wide range of individuals. Users with vision and mobility issues can leverage the voice interfaces of CAs to complete tasks with increasing complexity. However, CAs with voice interfaces pose unique challenges for those with different accessibility needs - specifically, older adults with hearing loss.There has been little work to understand how deaf older adults leverage CAs and the challenges they face while using voice interfaces. To address this gap, we conduct in-depth qualitative interviews with 4 deaf older adults to understand how and why they use CAs. We explore their expectations for their devices and identify common challenges (e.g., default voices used by commercial CAs). We provide suggestions for designing CAs that can better accommodate a range of hearing abilities and provide the first step forward toward a more inclusive CA.
Harassment is an issue in online communities with the live streaming platform Twitch being no exception. In this study, we surveyed 375 Twitch users in person at TwitchCon, asking them about who should be responsible for deciding what should be allowed and what strategies they perceived to be effective in handling harassment. We found that users thought that streamers should be most responsible for enforcing rules and that either blocking bad actors, ignoring them, or trying to educate them were the most effective strategies.
As customers are increasingly participating in online product and service reviews, companies can leverage the content of those consumer reviews to improve or retain customer satisfaction. By using a panel data set collected from Yelp, this study empirically tests the effects of voting and sentiment of customer reviews on future customer satisfaction. The results show that cool votes on customer reviews have a positive impact on customer satisfaction in the next month. While average positive sentiment score has a positive effect on customer satisfaction of a restaurant, average negative score has a negative influence. In addition, the diversity of the sentiment scores moderates the effects of positive and negative sentiment scores on customer satisfaction. This research provides a nuanced understanding of how the content of online customer reviews affects customer satisfaction, an important indicator of the quality of service or product.
In order to orchestrate collaborative group work, such as a brainstorming workshop or active learning class, a facilitator must monitor all groups simultaneously. In this paper, we propose a method for recognizing group activities, based on machine learning, where the input features are derived from the analysis of raw system events generated by a collaboration system known as "creative digital space" that we developed before. To verify the effectiveness of the proposed method, we also evaluated the results obtained by using the test data collected from active learning classes.
This interactive poster will discuss challenges and lessons learned designing and deploying ShareBox, a hardware-based system that enables people to share physical resources within local communities. Our goal in sharing the insights and struggles we encountered creating ShareBox is to help other researchers working on similar platforms to avoid the pitfalls that impacted our research.
Existing studies have not reached a consensus with regard to how social media may positively or negatively affect users' well-being. In this study, we endeavor to contribute towards HCI/CSCW knowledge on this open space and offer more empirical evidence. In doing so, we conducted 10 semi-structured in-depth interviews to explore the downsides of social media. We present four themes in social media use that adversely affect users' well-being emerging in participants' self-reports, namely, social comparison, fear of missing out, political discussions, and cyberbullying. Furthermore, we discuss two potential dynamics through which people consider their social media use negative to their well-being: 1) self-oriented internalization where users perceive the social media experience differently (negative or neutral) based on their personality traits; and 2) content-oriented internalization where users perceive the experience negative regardless of their personality traits. We argue that identifying these two dynamics of internalization helps understand the complex relationships between social media use and user well-being, which can then inform the future design of social media systems to remedy these adverse social and psychological impacts.
The Cambridge Analytica scandal triggered a conversation on Twitter about data practices and their implications. Our research proposes to leverage this conversation to extend the understanding of how information privacy is framed by users worldwide. We collected tweets about the scandal written in Spanish and English between April and July 2018. We created a word embedding to create a reduced multi-dimensional representation of the tweets in each language. For each embedding, we conducted open coding to characterize the semantic contexts of key concepts: "information", "privacy", "company" and "users" (and their Spanish translations). Through a comparative analysis, we found a broader emphasis on privacy-related words associated with companies in English. We also identified more terms related to data collection in English and fewer associated with security mechanisms, control, and risks. Our findings hint at the potential of cross-language comparisons of text to extend the understanding of worldwide differences in information privacy perspectives.
PYCIPEDIA is a web-based collaborative tool for social workers that support parents with intellectual disabilities taking care of their small children. These particular social workers have been certified in providing parenting-skills training and support for parents with intellectual disabilities or with learning difficulties. As only a relatively small number of social workers are certified in providing the relevant support, there may be few colleagues to discuss with and learn from, even in larger municipalities with many social workers. To support social workers in providing the best possible support, the web-based collaborative tool PYCIPEDIA has been co-designed with social workers from two Swedish municipalities. The tool allows social workers to log in, and independent from what municipality they work in, create, browse, edit and share material (e.g. text, images and video) that can support the parents. They can discuss cases, rate online material, and access a forum.
Hate crimes have risen in recent years in the United States and white nationalism appears related to the phenomenon . A portion of the communication regarding white nationalism is taking place online through social media. We report the preliminary results of emoji use by actors involved with the white nationalist conversation on Twitter, both those who support and who oppose the ideology. We investigated the frames and meanings of emoji characters expressed by actors in defining their own identities. We found strikingly different emoji use between the two groups of actors. We present on notable emoji use by those who support and those who oppose the white nationalist ideology.
Policing decisions, allocations and outcomes are determined by mapping historical crime data geo-spatially using popular algorithms. In this extended abstract, we present early results from a mixed-methods study of the practices, policies, and perceptions of algorithmic crime mapping in the city of Milwaukee, Wisconsin. We investigate this differential by visualizing potential demographic biases from publicly available crime data over 12 years (2005-2016) and conducting semi-structured interviews of 19 city stakeholders and provide future research directions from this study.
This paper presents a design and prototype for a collaborative tool aimed at facilitating scholarly discussion and reading groups. Organized yet informal reading and discussion groups are crucial resources for many scholars in interdisciplinary fields, yet such groups are difficult to start and maintain, very difficult to learn about and join, and only rarely involve participants from multiple institutions. Simply put, greater visibility into who's reading what and a way participate in these conversations would keep interdisciplinary scholars more connected with each other and with the various fields they follow. Perhaps even more importantly, a system which could collect this information could eventually offer a view into an otherwise part of the intellectual graph not visible via citation analysis or altmetrics, providing a platform for future research. Informal academic discussion groups are poorly studied, and though this paper relies on personal experience rather than qualitative research, it represents a first step in better understanding and serving these communities.
Although information and communication technologies (ICT's) have become pervasive across various aspects of human endeavors including educational settings, their adoption and diffusion vary among different developing and developed countries, often described as the digital divide. In this work, we examined concerns and considerations of students from developing countries transitioning into graduate and undergraduate studies in developed nations and assessed the personal and institutional resources available to them during their transition along with specific focus on the degree of their exposure to ICT's before and after their transition. The results showed that respondents had experienced different levels of technology exposure in their home countries, and that neither their sources of assistance in their home countries nor in their host countries provided the required aid for transitioning across the digital divide.
In this paper, I show the under heard voices of those whose primary media is radio, the proliferation of large trend analysis over fine-grained exploratory tools for large scale media data, and the absence of exploratory tools for hyper local media like local radio. In response, I present RadioSilenced, a web-based tool that enables the semi-serendipitous (not algorithmically curated) exploration and display of talk radio call-in data to encourage a deeper understanding of the 10 percent of US communities whose primary news consumption is radio, in a whole-system view but sample-by-sample exploration closer to ethnographic observation than a more reductive, big data analysis. To my knowledge, large scale analysis of talk radio, one of the oldest forms of media, has rarely been attempted.
In this paper, we propose the designs for two apps that benefit pediatric patients with asthma. We discuss the current state of the field through results from a survey of published apps related to asthma. We observed several key gaps that were unfilled and use these results to derive a set of requirements for a new app design. We describe two different designs that meet these requirements. The apps are designed to collect health metrics and educate kids aged 7 - 12 years through gameplay in two different contexts, at home and in hospital Emergency Departments (ED).
Social computing provides a variety of challenges and opportunities for people who are aging. In particular, following a recent diagnosis of dementia, older adults sometimes engage in online communities. In this work, we sought to understand what these adults are doing online, whether and how current solutions meet their needs, and what collaborative systems might be able to do to support the newly diagnosed better. We analyzed 57 posts from a forum for persons with dementia. Our findings indicate that users engaged with these forums as a means for self-expression, to connect with others, and to identify necessary resources. While seeking these types of information and support, persons with dementia sometimes provide private and vulnerable information as well as direct medical data, often in identifiable ways that would violate HIPPA standards. The tensions between need for support and the information shared presents interesting new challenges and opportunities for CSCW researchers.
WhatsApp, the world's most popular messaging application, offers significant opportunities for people to engage with each other, share their knowledge and experiences. In this paper, we investigate the nature of engagement in three WhatsApp groups, meant for promoting maternal and child health, consisting of 364 pregnant women, 249 new mothers, and three medical practitioners. We conducted thematic analysis of data obtained from these groups. Based on our analysis - 1732 messages, 121 multimedia artifacts and discussion with the medical practitioners - we found the participants' expectation from medical practitioners, their problem of repeatedly asking similar questions and multimedia practices of participants and practitioners in the groups.
Mobile robotic telepresence systems (MRPs) allow users to have a video conferencing communication channel with people in a distant environment where the MRP is physically located. In addition to having an audiovisual channel, the remote user can navigate the MRP. In this study, we interviewed ten undergraduate students after they had conducted a search-and-find task using an MRP. We found that students find it easy to maneuver the MRP and value the ability to move around in a remote space as well as having a physically embodied representation. We also found that students face challenges in approaching people due to three reasons, self-presentational concerns, novelty of robot-mediated communication, and difficulty with interpreting nonverbal cues. We will discuss our findings and provide design suggestions that could make MRPs more useful for social interactions.
This paper investigates whether subscribers (paying viewers) can be identified by their chat messages in live streaming services, Twitch. Collecting data of twenty streamers on Twitch, this study conducts two studies: (i) presenting sentimental differences of viewers' chat messages between unsubscribers (free viewers) and subscribers (paying viewers), and (ii) employing a set of machine learning-based classifiers which attempt to accurately identify subscribers. The results show that the machine learning-based classifiers with sentimental features provide the potentiality to identify paying viewers, suggesting their utility as a research toolkit.
Interest in artistry beer brewing has become increasingly popular over the past few decades. Within the brewing community, both professionals and amateurs engage their creativity when crafting their own beer. In this study, we are interested in understanding how members of the community support each other to be creative. Specifically, we investigated the forms of creativity support that professional crafters provide to amateurs. Our findings suggest that this creativity support holds an important role in enhancing creativity. We propose that technology design leverages the unique position of professionals to promote amateurs' creativity.
Spreading awareness among voters is a critical first step towards ensuring democratic participation. We report results from an ongoing deployment in India where we use an Interactive Voice Response (IVR) system to raise awareness about voting. People can call in to the number and answer a few questions on voting. They receive a mobile airtime top-up if they answer correctly, and an explanation of the correct answer if they do not. The system also serves as a survey instrument and we use it to collect data on the percentage of people who have their voter ID cards that enable them to vote. Extending past work on IVR-based mass awareness, we employ a different way of presenting content---a quiz instead of a tutorial---and of incentivizing usage---no monetary incentive for referrals except for specified referrers. In 24 days of deployment, over 1900 people have called the system, out of which 1245 answered all questions correctly in their first attempt, and 234 answered correctly after learning from their initial incorrect answers.
Light expressions can communicate and convey information in an unobtrusive manner. Smart-speakers employ light behaviors to indicate a wide range of device states as well as notifying users. However, no prior work has looked into the efficacy of these light behaviors in smart-speakers. That is, can users distinguish and understand information states associated with different light behaviors in smart-speakers? In this work, we aim to address this gap by investigating whether users can accurately identify light behaviors in Amazon Echo and Google Home devices. For this, we conducted an MTurk survey with 243 smart-speaker owners. Our findings reveal that only 34% of the light behaviors are correctly recognized by users on average. Moreover, we found that users find it easier to recognize light behaviors in Amazon Echo than in Google Home devices. These findings show a clear need for rethinking the design of light behaviors in smart-speakers. We also explored novel light behaviors that users might find useful but are not supported by current devices including expressing sentiment and privacy notifications.
Democracies around the world have different levels of dependence on influencers outside of mainstream politics in their outreach efforts. In this study, we examine the case of India, specifically the relationship between politicians in power and "influencers'', such as celebrities and media accounts, they follow on Twitter. We find that while politicians engage with these accounts to the same degree to which they follow them, the media accounts at the highest levels of engagement share the same ideology as the politicians who follow them. No similar alignment exists between celebrities and politicians, which requires future work of analyzing the content of engaging tweets in order to define the extent to which these accounts have political influence.
This poster provides an overview of an initial effort to examine the development of data interoperability from an infrastructural perspective applied to two geoscience examples, the International Geosample Number (IGSN) and EarthCube. Both activities represent efforts to develop data interoperability as part of computer supported cooperative work. In the case of the IGSN, the data interoperability provided by the cyberinfrastructure is arguably reaching the point of becoming a substrate for scientists. In the case of EarthCube, data interoperability through the envisaged cyberinfrastructure remains elusive. Recent themes in CSCW literature examine questions of infrastructure and infrastructuring . A goal of this poster is to illustrate the potential to use analysis of geoscience infrastructure to add to these discussions. The relevance of data interoperability is significant given the large amounts of funding and effort being put towards this goal and the potential for data interoperability to foster new scientific insights. Finally, examining this topic is even more salient given the recent emphasis on "FAIR data."
Maintaining design consistency is very important for IT organizations with complex products. In order to improve design consistency, we need a way to measure it first. Here we describe a method to evaluate GUI consistency, and we then develop a consistency score to quantify the consistency performance. This approach is initially designed towards our online cloud platform and we believe it can be applied to a variety of user interface types.
Studying abroad and adjusting to a new environment can be very stressful for non-native English-speaking international students due to challenges of language and culture. Previous research shows that computer-mediated communication (CMC) tools have the potential to help newcomers adjust to new environments. However, it is not clear what the role of communication tools is in newcomer international students' adaptation to the US. We conducted 17 semi-structured interviews with international students from China, Korea, and Japan to understand their adjustment challenges and how they use CMC tools to address them. We found that international students use CMC tools for finding reliable information, building new social networks, and providing an alternative channel for engaging in the US classroom. Based on these findings, we propose design implications for tools to better support international students' adjustment to the US.
Collaborative learning has potential to serve as a platform for fostering social connection, particularly in non-traditional contexts. Recent years have seen an increase in (long overdue) interest in supporting communities within such contexts through research. This has been often approached through ethnographic methods such as naturalistic observations, which are suitable for smaller, marginalized populations, However, the analysis of data produced by such methods often lack standardization, which limits generalizability of results and makes comparison across populations and learning contexts challenging. In this paper, we argue how greater grounding of data analysis in collaborative learning theories can provide standards for more meaningful comparison across contexts. We review Vygostky's social learning theories, shared social regulation of learning, and the trialogical approach. We discuss how anchoring inductive and deductive approaches in social frameworks may yield standardization metrics for unstructured, qualitative data from studies of social learning. We base this in our ongoing research on collaborative language learning between immigrant grandparents and grandchildren.
People with autism spectrum disorder (ASD) Level 1 require support with their activities of daily living. These individuals may have access to in-person learning programs to help develop social and independence skills such as interacting with peers and maintaining personal hygiene. However, in-class learning does not usually extend beyond the classroom. In this project, we explore a social network based digital support system that can bridge these gaps to extend and augment in-class learning.
We report how pair's empathy skills and dispositional empathy associate with their counterpart's evaluations of social closeness to the pair and subjective workload in a collaborative problem solving task completed both face-to-face and in virtual reality.
Twitter has become a prominent platform for world leaders to communicate with the public. As an exploratory analysis, we investigate its use by nine heads of state across the democracy index. Using a frequency analysis from qualitative coding, we did not find significant differences in Twitter use among leaders of democratic and authoritarian regimes, except when it comes to condemning others, requesting cooperation, or discussing the environment. We also find some indication that leaders of 'flawed democratic governments,' particularly Donald Trump, have communication patterns via Twitter similar to those of populist states.
Smart-speakers such as Amazon Alexa are becoming increasingly popular among the general population. These devices support a wide range of user-initiated tasks. However, their current interaction capabilities are limited to quick turn-takings and short dialogues, which leads to limited user engagement. In this project, we aim to enhance the user engagement and interaction abilities of a smart-speaker by transforming it into an active listener. Specifically, we explored how providing random back-channelling (i.e., verbal continuers including "hm", "uhum", "aha", "yeah") can result in longer interactions and more sustained user engagement. The findings of the study have implications for usability and future design of smart-speakers. We also explored how the enhanced interactions in smart-speakers through back-channeling can be used for self-centered therapy leading to betTer emotional and social support.
Animal shelters generate an enormous amount of data and require extensive information and communication management, however very little focus has been given to this field through the CSCW lens. The aim of this autoethnographic study is to portray the different events and resulting artifacts from daily shelter tasks using a combination of workflow modeling and customer journey mapping techniques. The Shelter Touchpoint Workflow sheds light on unique trends in the collaborative environment of animal welfare. Thus, demonstrating room for the CSCW community to help develop solutions to alleviate shortcomings of legacy technology and other tools that have been adapted to fit into their daily tasks. The two specific insights we are examining are the data fragmentation and siloing of information about an animal, as well as the contribution of social media to compassion fatigue.
Social media sites such as Facebook and Twitter use algorithms to filter information in order to reduce overload and selectively pick content for users. These algorithms create unique, individual, and isolated bubbles of information that users are not always aware of. We recommend that algorithmic awareness should be the first step in addressing the pitfalls of the filter bubble effect. We conducted an experimental study to investigate how simple visualizations can be used to achieve algorithmic awareness and to understand how it might influence users' behavior. The visualizations did not lead to increased understanding of the algorithm per se, but its presence created interesting effects that will inform future studies.
This study explores the notions of sharing public harassment experiences on social media and the associated construction of online social support and justice, based on an anonymous survey (n=340) and semi-structured interviews (n=10) in Bangladesh. Our initial findings show the wider prevalence of public harassment against women in Bangladesh and indicate the existence of implicit societal biases and socio-technical infrastructural weaknesses that manipulate the sharing experiences of harassment on social media as well as limit the expected social support and justice online. These findings advocate for better design policies on safer social media participation, especially for women and call for more attention on fixing the existing technical infrastructures that address justice online.
Asthma is a common childhood chronic disease. There are various metrics of asthma which are tracked and reported to healthcare providers. We investigated visualizations & concepts for tracking quantitative and qualitative asthma data for pediatric patients. We conducted a 3-part study with 14 participants where we (1) interviewed 2 parents and 6 children about their health tracking behavior (2) tested visualization alternatives with 6 children (3) designed concepts to track qualitative data for future testing. Our study shows that simple visualizations can be comprehended by children, so the use of these to report data to the healthcare provider is a powerful tool not just for the parents but also pediatric patients. We iterated the design of the visualizations based on qualitative feedback.
Maintaining a positive group emotion is important for team collaboration. It is, however, a challenging task for self-managing teams especially when they conduct intra-group collaboration via text-based communication tools. Recent advances in AI technologies open the opportunity of using chatbots for emotion regulation in group chat. However, little is known about how to design such a chatbot and how group members react to its presence. As an initial exploration, we design GremoBot based on text analysis technology and emotion regulation literature. We then conduct a study with nine three-person teams performing different types of collective tasks. In general, participants find GremoBot useful for reinforcing positive feelings and steering them away from negative words. We further discuss the lessons learned and considerations derived for designing a chatbot for group emotion management.
Conducting an effective literature review is an essential step in all scientific work. However, the process is difficult, particularly for interdisciplinary work. Here, we articulate a key avenue for improvement for literature review tools: supporting the appropriate unit of interaction, which we argue is a "grounded claim", a concise statement linked to key contextual details such as evidence. However, there are significant cognitive and interaction costs in creating them. We share insights from our development of a prototype literature review tool, the Knowledge Compressor, that aims to lower these costs.
In this paper, we analyze and compare the representation of social movements on social media from the perspective of morality. Following previous research, which found associations between morality, collective action, and social decision-making, we postulate that moral values are distinct across different movements since these movements represent moral values of those who support or oppose them. The result of our analysis of four movements as represented on Twitter (#BlackLivesMatter, #WhiteLivesMatter, #AllLivesMatter, and #BlueLivesMatter) reveal that \#BlueLivesMatter represents values such as Care, Harm, Loyalty, and Authority, while \#WhiteLivesMatter features Harm and Fairness. Moreover, we find that Harm is the most prominent moral value in all of our datasets. Our analysis provides a robust understanding of authors' moral stances, which contextualizes the influence of movements on people, and how these movements are perceived in society.
Working time estimation is known to be helpful for allowing crowd workers to select lucrative microtasks. We previously proposed a machine learning method for estimating the working times of microtasks, but a practical evaluation was not possible because it was unclear what errors would be problematic for workers across different scales of microtask working times. In this study, we formulate MicroLapse, a function that expresses a maximal error in working time prediction that workers can accept for a given working time length. We collected 60,760 survey answers from 660 Amazon Mechanical Turk workers to formulate MicroLapse. Our evaluation of our previous method based on MicroLapse demonstrated that our working time prediction method was fairly successful for shorter microtasks, which could not have been concluded in our previous paper.
Graduate school and academia can often be challenging and hard to navigate. This work explores how people are using Reddit to reach out to others in academic subreddits to talk about issues one might face in their academic journey. We also explore how such discussion differs between subreddits by comparing two popularly used academic subreddits: r/gradschool and r/academia. For each subreddit, we investigated 300 posts and 500 comments. Using topic modelling, we identify and distinguish the main emergent types of posts and comments we find in these two subreddits. We find that posts in r/academia center more on the challenging aspects of academia such as plagiarism, working in academia, and mental health, whereas r/gradschool posts deal with more generic issues on graduate school life. However, we find that the way the community reacts and provides support via comments is similar in both subreddits, mostly by providing moral support and solidarity.
The point-of-care processes of cancer diagnosis, treatment, and patient recovery require an integration of a variety of data and medical information to ensure optimal management. To better design and utilize these technologies, it is critical to understand the landscape of existing modalities and how they influence the care of cancer patients. Here, we review recent advances in the development and implementation of provider-oriented systems for oncologic care. We propose that these developments may be effectively characterized based on where they are situated in the timeline of cancer care; that is, technologies for cancer detection and diagnosis, intervention and surgery, and longitudinal patient therapy/management. The utility and impact of this class of informatics technologies is highlighted through a comprehensive case analysis of Figure1, a mobile application tailored to improve the continuing clinical education among providers. The advances and advantages showcased by Figure1 demonstrate the potential for provider-centered HIT to facilitate open clinical collaboration, collective growth of medical human capital, and ultimately more effective, personalized cancer care.
Outdoor activities are an important way for friends, family, and couples to spend time with one another and share exploration activities. Yet when people become separated by distance it can be difficult if not impossible to participate in outdoor activities together. We propose a design called Teledrone that uses a drone with video conferencing display to create feelings of social presence during shared outdoor activities over distance. With Teledrone, users connect into the video call display and can maneuver the drone to follow a friend or family member during an outdoor activity such as hiking. In this way, Teledrone provides an embodiment for a remote user and can help support spatial awareness.
This study reports research findings from a qualitative thematic analysis based study that examined the design aspect of the Bangladeshi ride-sharing service "Pathao''. We examined a total of 8000+ app review data for our study. Investigation of a ride-sharing app from a design perspective by analyzing app review data is novel. We categorized users complaints in (1) technical (2) user experience (UX) (3) payment and cost issues (4) driver-related issues as they were discovered from our data. Our research findings might not be generalizable across different platforms. Nevertheless, the outcome of this study will provide guidelines for designing new and improved ride-sharing experience in the future.
Asthma is the most common childhood chronic illness. Patients with asthma are recommended to have an asthma action plan (AAP) for long-term control and responding to worsening asthma symptoms. We investigated a mobile icon-based AAP (I-BAAP) to understand whether children comprehend the AAP and the icons which are a part of it. We conducted a 2-part study with 14 participants where we (1) assessed the comprehension via translucency rating of the icons (2) evaluated the comprehensibility and usability of the AAP through a scenario test. We compared our results of children with the results of caregivers in the previous study. Our study shows that there is a difference between children and caregivers in terms of the comprehension of the icons and I-BAAP. Redesign or adjustments of the icons and mobile I-BAAP are needed to accommodate the children's needs. We iterated the design and concluded with recommendations about how to improve mobile I-BAAP for asthmatic children.
There is an increasing interest in CSCW to understand how technology can be used for the monitoring of chronic conditions, and how collaboration for care planning can occur between clinicians and patients through its use. Many studies in this area have focussed on the patients' experience of using such technology. We report findings from a small-scale study, where a smartphone app for monitoring Chronic Obstructive Pulmonary Disease symptoms was introduced into a community respiratory service for patients' use. Our findings provide three key insights into the clinicians' experiences in receiving the patient reported data and supporting the patients' use of the app as part of their service.
In this poster, we present a case study of the100.io, an online platform that sorts gamers into groups that support gameplay and other activities. Through our analysis, we have identified that communities in which individuals are algorithmically sorted can thrive, even when the conditions of that sorting are somewhat arbitrary. We identify opportunities for CSCW researchers to further engage in the presence of shared identities, conversations about group atmosphere, and engagement with external tools as a means of indicating group health and success.
Group facilitators often organize large co-located crowds into small groups where people can more effectively interact and learn from each other. However, current approaches for facilitating small group interactions typically do not capture data about the rapport and future potential of interacting groups. We introduce ProtoTeams, a system designed to facilitate and gather data from small group interactions by dynamically connecting people from larger co-located crowds, such as classrooms and hackathons, through personal mobile devices. The system randomly assigns subjects into groups, then guides them through short activities, at the end of which they evaluate their teammate preferences. We report preliminary results from three deployments in a university-level design studio course where students (N = 50) interact with peers on activities before forming teams for a quarter-long project. Results suggest that the system is intuitive and groups form efficiently. Few subjects report the environment being too hectic and activities too short to accurately evaluate their peers. Future studies will continue investigating the value of this tool in co-located team dynamics.
In this paper, we report findings from our pilot study using semi-structured interviews to assess users' perceived receptivity to an adaptive system of engagement strategies, ewrapper, that "wraps" around decision points in a dietary self-monitoring mobile application. Study participants were asked to perform a "think-aloud" usability exercise with paper prototypes of the intervention's interface design. This was followed by a needs and preferences assessment of the operationalization of various engagement strategies used in the design of ewrapper. We provide future design guidelines for such systems to improve user engagement.
The US maternal mortality is double that of the UK and Canada and still rising. This is a public health crisis, and draws our attention to systemic issues with US health care, as maternal mortality is a core statistic used to indicate health care quality. Responding to the increasing number of women dying during one of the most common experiences with the medical system will require organizing a diverse group of stakeholders over a long period of time. Due to the unevenly distributed nature of the crisis, with Black women 243% more likely to die in childbirth or from childbirth-related causes will also require us to incorporate underrepresented voices into the current system. To examine these issues, we conducted interviews with public health experts working to address maternal mortality. We found that articulation work practices in public health limit the ability of experts to initiate contact with the very stakeholders they need to build relationships with to address the rise in maternal mortality.
In this report, we review our early steps in leveraging the promise of Artificial Intelligence to potentially create more inclusive co-design experiences for marginalized populations. We discuss our research agenda and the first phases where we utilized Wizard of Oz prototypes to engage participants in an online co-design environment. We found that the types of interactions between the facilitator and participant determined the amount of engagement with the co-design process.
We report of a new crowdsourcing work paradigm that we came across while interviewing crowdworkers in China mid-May 2019 - that of companies that solely focus in undertaking and doing crowdsourcing tasks en masse. In addition, we discuss why such companies emerged recently, and how it affects the crowdsourcing landscape in China. With this work we highlight an important change in the rapidly changing crowdsourcing landscape of China that merits more research in the future.
Joint improvised activity and synchrony of movement increases affiliation between people. The mirror game, where two people create joint motion in an improvised pattern, has been used to study different aspects of face-to-face collaboration and synchronization. To explore whether a similar game could be used to study computer-mediated interaction and as an ice-breaker activity to increase affiliation in remote collaboration, we developed a multiplayer online mouse coordination game inspired by the mirror game. The source code to the game is released as free open source software.
Cultural probes have long been used in HCI and CSCW to provide researchers with glimpses into the local cultures for which they are designing. Researchers in HCI4D/ICTD are increasingly interested in more deeply understanding local cultures in the developing regions where they work. However, few use cultural probes in their research. I present a case study describing my experience using this subjective, design-led method in Bungoma County, Kenya. Returns from my comment cards and digital camera activities draw attention to the diversity, idiosyncrasies, and complexity of everyday life in Bungoma. I conclude that cultural probes can benefit HCI4D/ICTD research.
This work examined the survey distribution methods used in a past study. The goal was to identify the most effective methods to reach marginalized voices to participate in technological research and thus, create more inclusive technologies. Initial analyses identified in-person onsite recruitment as one of the better methods for reaching hard-to-reach populations compared to M-Turk, social media, newsletters, mail, and text messages. Our results call for continued efforts to use more inclusive research methods in the field of HCI.
Numerous how-to tutorials and videos are pre-produced and archived for future learners' self-paced online learning. However, the co-presence of instructor and potential learners is missing when experts pre-produce how-to tutorials or videos offline. Hence, the content being shared by experts may not meet future learners' needs. To ensure the instructional value of archived tutorials for future learners, we explored ways to improve content production by analyzing interactive social prompts from a social partner during the time when experts share knowledge. We paired experts with partners of varying expertise and asked them to freely interact with the experts along the process. To simulate sharing knowledge via video-recording, a cooking task was deployed and we asked cooking experts to think-aloud while executing a cooking task with feedback from one of three types of paired partners, including another cooking expert, a novice, or no partner. This study identified six types of social prompts that can inform the design of computer-mediated knowledge sharing. Moreover, content quality was rated and evaluated, which shows that the best when knowledge was shared with the aid of expert's prompts. This study provides understanding of how various types of social prompts affect the externalization of how-to knowledge for asynchronous knowledge transfer between experts and future learners.
In this paper we explore the possibility to leverage the emerging social VR platforms in hopes of addressing the limitations in prior work concerning design technologies to support Long Distance Relationships. Using 650 social media posts and comments, we present three themes emerging in long distance couples' experiences of social VR applications. This study represents our first endeavor to explore how engaging with social VR affects some of the deepest and most meaningful aspects of human experiences such as romantic relationships and emotional connections, so as to inform the design of more socially and emotionally satisfactory VR systems in the future.
This workshop is aimed at bringing together a multidisciplinary group to discuss Machine Learning and its application in the workplace as a practical, everyday work matter. It's our hope this is a step toward helping us design better technology and user experiences to support the accomplishment of that work, while paying attention to workplace context. Despite advancement and investment in Machine Learning (ML) business applications, understanding workers in these work contexts have received little attention. As this category experiences dramatic growth, it's important to better understand the role that workers play, both individually and collaboratively, in a workplace where the output of prediction and machine learning is becoming pervasive. There is a closing window of opportunity to investigate this topic as it proceeds toward ubiquity. CSCW and HCI offer concepts, tools and methodologies to better understand and build for this future.
Discussions of online social technologies focus on their negatives in relation to wellbeing, prioritizing offline relationships and reduced screen time. However, many communities depend on online social technologies for building community, gaining social support, and even exploring identity. This makes the use of these technologies crucial for the wellbeing of these communities. Numerous CSCW-related disciplines examine marginalized people's use of social technology and its relationship to wellbeing. Yet opportunities to converse across domains and engage in thinking around these topics are scarce. We aim to bring together health, design, communication, HCI, STS, and a spectrum of diversity science researchers to discuss what digital wellbeing looks like for marginalized populations, share the state of knowledge in respective fields, and identify opportunities for leveraging social technologies for wellbeing across these communities. We will engage in exercises intended to foster mutual understanding, identify commonalities between areas of inquiry, and bridge gaps between research areas. Our goal is to stimulate interdisciplinary collaboration, with the hope of advancing a research agenda regarding digital wellbeing for marginalized populations.
Qualitative methods have long been an important component of CSCW research. However, it can be challenging to make qualitative work legible to a broader set of researchers, which is critical as mixed methods research becomes more common. Moreover, the shift towards larger scales of data and increasing calls for open data and more transparency pose new questions for qualitative methods in terms of data collection, analysis, reporting, and sharing. This workshop brings together researchers to discuss these challenges as well as new opportunities for qualitative methods, with goals to help build norms and best practices for (1) conducting qualitative research, (2) reporting that research, and (3) engaging and collaborating with CSCW researchers from other methodological traditions.
Artificial intelligence is revolutionizing work, including what it means for cooperative work to be supported by computers. The increased use of AI in CSCW can lead to many advantages, including increased productivity and efficiency, but it can also include several potential ethical trade-offs, such as invasions of privacy, loss of autonomy, and job displacement. This workshop will explore the ethical dimensions of AI in CSCW, building on Good Systems, a UT Grand Challenge. Specifically, the first half of the workshop will focus on the need to design AI to work for all users and to avoid bias through the use of universal design as well as the need for AI and CSCW researchers to interact with policy and legal experts to work together to ensure that AI will be developed in an ethical manner with sufficient consideration of its societal implications, and also that AI will be regulated and legislated in ways that will maximize its benefits to all people.
This workshop aims to advance our knowledge of how CSCW technologies can be better aligned with grassroots politics of collaboration. What politics are inherent in CSCW tools and techniques? How can we examine whether so- ciotechnical systems support collaboration in ways that lead to equitable solutions for all and not just a select few? What can we learn about collaborative systems and practices from other communities of people with lived experiences of politics of collaboration? Our workshop will incorporate communal practices of grassroots movement building to explore what it means to examine designs of CSCW arti- facts and practices for the politics they embody and pro- mote. The workshop simultaneously is about grassroots approaches, and also leverages lessons we have learned from grassroots movements in the workshop structure.
Social media platforms are deeply ingrained in society, and they offer many different spaces for people to engage with others. Unfortunately, accessibility barriers prevent people with disabilities from fully participating in these spaces. Social media users commonly post inaccessible media, including videos without captions (which are important for people who are deaf or hard of hearing) and images without alternative text (descriptions read aloud by screen readers for people who are blind). Users with motor impairments must find workarounds to deal with the complex user interfaces of these platforms, and users with cognitive disabilities may face barriers to composing and sharing information. Accessibility researchers, industry practitioners, and end-users with disabilities will come together to outline challenges and solutions for improving social media accessibility. The workshop starts with a panel of end-users with disabilities who will recount their Perspectives of Successes and Barriers. Industry professionals from social media companies (e.g., Facebook and LinkedIn) will detail their Design Process and Implementation Challenges in a panel with questions from attendees. The attendees will share their work and tackle Open Challenges and Future Research Directions. This workshop will forge collaborations between researchers and practitioners, and define high-priority accessibility challenges for social media platforms.
When people experience major changes in their lives (e.g., relationship changes, transition from high school to college, realizing an LGBTQ identity, etc.), they often turn to social technologies to help navigate shifting identities and networks and find support and resources. People's experiences using social technologies during times of life transition, and how to better design such technologies, has been a major focus of social computing research. This workshop will gather researchers working in this space to discuss eight themes: life events vs. processes; changing identities; multiple overlapping life events; physical and digital transitions; technology non-use during life transitions; liminality framework; theoretical frames; and methodological considerations. Collaboratively, we will 1) synergize insights from workshop organizers' and participants' research to determine how social technologies can be designed to better support people during life transitions and 2) outline an agenda for the future of social computing work on life transitions.
While the shift to on-demand labor may foster greater control over one's employment in some ways, it has removed much of the benefits that come with consistently working in shared physical spaces. Working in physical spaces allow opportunities for social support, long-term growth, and stability. The goal of this workshop is to facilitate a discussion around how physical spaces and online technologies influence each other in on-demand work. We plan to invite a diverse group of stakeholders, including researchers studying these topics, grassroots organizers who can represent and voice the concerns of their respective worker communities, and designers of on-demand work platforms. Discussion and ideas generated from this workshop will be archived online and made available to the larger research community and the general public.
Research on the governance of online communities often requires exchanges and interactions between researchers and moderators. While a growing body of work has studied commercial content moderation in the context of platform governance and policy enforcement, only a small number of studies have begun to explore the work of unpaid, volunteer community moderators who manage the millions of different subcommunities that exist on platforms. This workshop will create a pathway for future scholars to tackle the challenges and opportunities of research on volunteer community moderators and establish best practices for engaging with volunteer moderators without disrupting their work. Through lightning talks, collaborative brainstorming exercises, and small-group activities applying principles to research practice, workshop participants will bring together their diverse experiences and perspectives to map the future of moderation research. Both industry and academic researchers as well as experienced moderators will lead this one-day workshop that may accommodate up to 20 participants.
By 2019, diversity is an established fact in most workplaces, teams, and work-groups, presenting both old and new challenges to CSCW in terms of team structure and technological supports for increasingly diverse teams. The research literature on diversity and teams has examined many definitions and attributes of diversity, and has described different types of teams, tasks, and measures, with contrasting and even contradictory results. Diversity becomes a strength in some studies, and a burden in others. The literature is similarly complex regarding individual and organizational approaches to realize those strengths, or to mitigate those burdens. In this workshop, we collectively take stock of these complex findings; we consider the several theoretical and methodological efforts to organize these findings; and we propose new research directions to address the "diversity of diversity studies.",0
The proposed workshop will identify research questions that will enable the field to uncover the types of work, labor relations, and social impacts that should be considered when designing AI-based healthcare technology. The workshop aims to outline key challenges, guidelines, and future agendas for the field, and provide collaboration opportunities for CSCW researchers, social scientists, AI researchers, clinicians, and relevant stakeholders in healthcare, to share their perspectives and co-create sociotechnical approaches to tackle timely issues related to AI and automation in healthcare work.
Most health tech for daily health and wellbeing is focused on individuals. We know that, with even the best will in the world, trying to practice what we learn in our workplaces, if we are the lone person in our group with that practice, it can be very challenging to sustain that practice. WellComm2019's goal is to explore how we can design interactive technology assets to enable diverse workplace groups to explore, test, build, and create evidence to support practices that will both improve their wellbeing for themselves.
This day-long workshop aims to support and grow the community of CSCW and HCI scholars that investigate the past to inform the design, critique and conceptualization of technology. At this workshop, we will learn from examples of historically-based CSCW and HCI work, explore issues in historical method that come up in such work, share methods and techniques, provide feedback and support to ongoing investigations; and define a shared agenda for future research on this topic. The workshop will also highlight research and methods that focus on non-Western contexts and that give voice to historically marginalized groups. Based on the workshop, we will develop a white paper and a website that will collect resources to support CSCW based historical investigations.
As algorithmic (and particularly machine learning) decision making systems become both more widespread and make more important decisions, there are growing concerns about their embedded values and ability to establish legitimacy among decision subjects. We argue that designing for contestability in these systems can assist in surfacing values, aligning system design and use with context, and building legitimacy. However, designing for contestability can be challenging, particularly in systems that are designed to be opaque: systems need to accurately surface embedded values, expose decision making processes in ways that are meaningful for users, support engagement with and allow influence over system performance, and so on. In addition to these technical aspects, designing for contestability may by challenged by the need to protect intellectual property and prevent gaming of the system. In this workshop, we will address goals, audiences, and designs for contestability in algorithmic systems. We hope to develop a taxonomy of contestable systems and understand the value provided by contestability, while bringing together a community to work on this multidisciplinary problem.
Process has been a concern since the beginning of CSCW. Developments in sociotechnical landscapes raise new challenges for studying processes (e.g., massive online communities bringing together vast crowds; Big Data technologies connecting many through the flow of data). This re-opens questions about how we study, document, conceptualize, and design to support processes in complex, contemporary sociotechnical systems. This one-day workshop will bring together researchers to discuss the CSCW community's unique focus and methodological toolkit for studying process and workflow; provide a collaborative space for the improvement and extension of research projects within this space; and catalyze a network of scholars with expertise and interest in addressing challenging methodological questions around studying process in contemporary, sociotechnical systems.
This one-day workshop aims to explore ubiquitous privacy research and design in the context of mobile and IoT by facilitating discourse among scholars from the networked privacy and design communities. The complexity in modern socio-technical systems points to the potential of utilizing various design techniques (e.g., speculative design, design fiction, and research through design practices) in surfacing the potential consequences of novel technologies, particularly those that traditional user studies may not reveal. The results will shed light on future privacy designs for mobile and IoT technologies from both empirical and design perspectives.