Online communities provide important functions in their participants' lives, from providing spaces to discuss topics of interest to supporting the development of close, personal relationships. Volunteer moderators play key roles in maintaining these spaces, such as creating and enforcing rules and modeling normative behavior. While these users play important governance roles in online spaces, less is known about how the work they do is impacted by platform design and culture. r/AskHistorians, a Reddit-based question and answer forum dedicated to providing users with academic-level answers to questions about history, provides an interesting case study on the impact of design and culture because of its unique rules and their strict enforcement by moderators. In this article I use interviews with r/AskHistorians moderators and community members, observation, and the full comment log of a highly upvoted thread to describe the impact of Reddit's design and culture on moderation work. Results show that visible moderation work that is often interpreted as censorship, and the default masculine whiteness of Reddit create challenges for moderators who use the subreddit as a public history site. Nonetheless, r/AskHistorians moderators have carved a space on Reddit where, through their public scholarship work, the community serves as a model for combating misinformation by building trust in academic processes.
We propose a data-driven approach to detect conversational groups by identifying spatial arrangements typical of these focused social encounters. Our approach uses a novel Deep Affinity Network (DANTE) to predict the likelihood that two individuals in a scene are part of the same conversational group, considering their social context. The predicted pair-wise affinities are then used in a graph clustering framework to identify both small (e.g., dyads) and large groups. The results from our evaluation on multiple, established benchmarks suggest that combining powerful deep learning methods with classical clustering techniques can improve the detection of conversational groups in comparison to prior approaches. Finally, we demonstrate the practicality of our approach in a human-robot interaction scenario. Our efforts show that our work advances group detection not only in theory, but also in practice.
OT (Operational Transformation) was invented for supporting real-time co-editors in the late 1980s and has evolved to become core techniques widely used in today's working co-editors and adopted in industrial products. CRDT (Commutative Replicated Data Type) for co-editors was first proposed around 2006, under the name of WOOT (WithOut Operational Transformation). Follow-up CRDT variations are commonly labeled as "post-OT" techniques capable of making concurrent operations natively commutative in co-editors. On top of that, CRDT solutions have made broad claims of superiority over OT solutions, and often portrayed OT as an incorrect and inefficient technique. Over one decade later, however, CRDT is rarely found in working co-editors; OT remains the choice for building the vast majority of today's co-editors. Contradictions between the reality and CRDT's purported advantages have been the source of much confusion and debate in co-editing researcher and developer communities. To seek truth from facts, we set out to conduct a comprehensive and critical review on representative OT and CRDT solutions and working co-editors based on them. From this work, we have made important discoveries about OT and CRDT, and revealed facts and evidences that refute CRDT claims over OT on all accounts. These discoveries help explain the underlying reasons for the choice between OT and CRDT in the real world. We report these results in a series of three articles. In this article (the second in the series), we reveal the differences between OT and CRDT in their basic approaches to realizing the same general transformation and how such differences had resulted in different technical challenges and consequential correctness and complexity issues. Moreover, we reveal hidden complexity and algorithmic flaws with representative CRDT solutions, and discuss common myths and facts related to correctness and complexity of OT and CRDT. We hope the discoveries from this work help clear up common myths and confusions surrounding OT and CRDT, and accelerate progress in co-editing technology for real world applications.
Today, the prominence of data science within organizations has given rise to teams of data science workers collaborating on extracting insights from data, as opposed to individual data scientists working alone. However, we still lack a deep understanding of how data science workers collaborate in practice. In this work, we conducted an online survey with 183 participants who work in various aspects of data science. We focused on their reported interactions with each other (e.g., managers with engineers) and with different tools (e.g., Jupyter Notebook). We found that data science teams are extremely collaborative and work with a variety of stakeholders and tools during the six common steps of a data science workflow (e.g., clean data and train model). We also found that the collaborative practices workers employ, such as documentation, vary according to the kinds of tools they use. Based on these findings, we discuss design implications for supporting data science team collaborations and future research directions.
Sanctions play an essential role in enforcing and sustaining social norms. On social networking sites (SNS), sanctions allow individuals to shape community norms on appropriate privacy respecting behaviors. Existing theories of privacy assume the use of such sanctions but do not examine the extent and effectiveness of sanctioning behaviors. We conducted a qualitative interview study of young adults (N=23), and extend research on collective boundary regulation by studying sanctions in the context of popular SNS. Through a systematization of sanctioning strategies, we find that young adults prefer to use indirect and invisible sanctions to preserve strong-tie relationships. Such sanctions are not always effective in helping the violator understand the nature of their normative violation. We offer suggestions on supporting online sanctioning that make norms more visible and signal violations in ways that avoid direct confrontation to reduce the risk of harming on-going social relationships.
Over 35% of Americans belong to racial minority groups. Racism targeting these individuals results in a range of harmful physical, psychological, and practical consequences. The present work aims to shed light on the current sense-making and support-seeking practices exhibited by targets of racism, as well as to identify the core needs and barriers that future socio-technical interventions could potentially address. The long-term goal of this work is to understand how CSCW researchers and designers could best support members of marginalized groups to make sense of and to seek support for experiences with racism. Narrative episode interviews with targets of racism revealed a number of key entry points for intervention. For example, participants' personal stories confirmed that uncertainty, both about the nature and consequences of the experience of racism, is a key motivator for support-seeking. In addition, despite the need for support, participants largely do not trust public forms of social media for support-seeking. We discuss how participants' accounts of the complex labor involved in determining who "gets it" in identifying potential supporters, and in navigating the complexities of trust and agency in sharing their experiences, present clear implications for the design of new socio-technical platforms for members of racial minority groups.
Time-bounded events such as hackathons have become a global phenomenon. Scientific communities in particular show growing interest in organizing them to attract newcomers and develop technical artifacts to expand their code base. Current hackathon approaches presume that participants have sufficient expertise to work on projects on their own. They only provide occasional support by domain experts serving as mentors which might not be sufficient for newcomers. Drawing from work on workplace and educational mentoring, we developed and evaluated an approach where each hackathon team is supported by a community member who serves in a mentor role that goes beyond providing occasional support. Evaluating this approach, we found that teams who took ownership of their projects, set achievable goals early while building social ties with their mentor and receiving learning-oriented support reported positive perceptions related to their project and an increased interest in the scientific community that organized the hackathon. Our work thus contributes to our understanding of mentoring in hackathons, an area which has not been extensively studied. It also proposes a feasible approach for scientific communities to attract and integrate newcomers which is crucial for their long-term survival.
The Blue Whale Challenge (BWC) is an online viral "game" that allegedly encourages youth and young adults towards self-harming behaviors that could eventually lead to suicide. The BWC can be situated within a larger phenomenon of viral online self-harm challenges, which may be propagated through both social media and news sources. Research has established that suicide is a global public health issue that is known to be influenced by media reporting. Violation of safe messaging guidelines has been shown to increase imitative suicides, particularly in youth and young adults. Given the confirmed effects of news media reporting, we analyzed 150 digital newspaper articles reporting on the BWC to assess whether they adhered to suicide prevention safe messaging guidelines. Overall, 81% of the articles violated at least one contagion-related guideline, most commonly normalizing suicide, discussing means of suicide, and sensationalizing. Even though the majority (91%) of the articles adhered to at least one health-promotion guideline, such as emphasizing prevention, the articles did not follow these guidelines on a deep and comprehensive level. Through thematic analysis, we also found evidence of potential misinformation in reporting, where the articles unequivocally attributed many suicides to the BWC with little or no evidence. Additionally, articles often stated an individual's reason for participating in the challenge without interviewing the individual or those close to the individual, another aspect of potential misinformation due to lack of evidence. A contribution of the current study is the synthesis of safe messaging guidelines that can be used in future research. This study contributes to the understanding of news reporting practices regarding suicide and self-harm in regard to the BWC and similar online challenges. We discuss how sensationalized news media reports on the BWC could unintentionally propagate suicide contagion effects that normalize self-harming behaviors among youth. We then examine implications for practice and policy, such using automated approaches to aid reporters in adhering to safe messaging guidelines.
Prior studies of technology non-use demonstrate the need for approaches that go beyond a simple binary distinction between users and non-users. This paper proposes a set of two different methods by which researchers can identify types of non/use relevant to the particular sociotechnical settings they are studying. These methods are demonstrated by applying them to survey data about Facebook non/use. The results demonstrate that the different methods proposed here identify fairly comparable types of non/use. They also illustrate how the two methods make different trade offs between the granularity of the resulting typology and the total sample size. The paper also demonstrates how the different typologies resulting from these methods can be used in predictive modeling, allowing for the two methods to corroborate or disconfirm results from one another. The discussion considers implications and applications of these methods, both for research on technology non/use and for studying social computing more broadly.
To increase capacity and safety in air traffic control, digital strip systems have superseded paper strips in lower airspace control centers in Europe. Previous ethnographic studies on paper strip systems anticipated a radical change in work practices with digital strip systems, but we are not aware of any studies that evaluated these predictions. We carried out contextual inquiries with controllers and focused on face-to-face and radio communication, interactions with the digital strip system and the workspace in general. In turn, we contribute (1) detailed descriptions of controllers' work practices, such as using tacit information from radio communication and 'standard advocates vs. tinkerers' operation modes, (2) respective implications for design and (3) discuss how the observed work practices are similar or different from the reported practices in the literature of the two preceding decades. Our key insights are, that documentation speed is faster with digital strips, although a high load in the case of radio frequency persists. Controllers retrieve tacit information from the radio communication and combine it with scattered cues from several displays to form empathic decisions that sometimes exceed the standard protocol. We conclude that the role of tacit information holds opportunities for future flight systems and should be considered in a holistic approach to individualized workspaces for controllers.
Online social networks (OSN) play an essential role for connecting people and allowing them to communicate online. OSN users share their thoughts, moments, and news with their network. The messages they share online can include sarcastic posts, where the intended meaning expressed by the written text is different from the literal one. This could result in miscommunication. Previous research in psycholinguistics has studied the sociocultural factors the might lead to sarcasm misunderstanding between speakers and listeners. However, there is a lack of such studies in the context of OSN. In this paper we fill this gap by performing a quantitative analysis on the influence of sociocultural variables, including gender, age, country, and English language nativeness, on the effectiveness of sarcastic communication online. We collect examples of sarcastic tweets directly from the authors who posted them. Further, we ask third-party annotators of different sociocultural backgrounds to label these tweets for sarcasm. Our analysis indicates that age, English language nativeness, and country are significantly influential and should be considered in the design of future social analysis tools that either study sarcasm directly, or look at related phenomena where sarcasm may have an influence. We also make observations about the social ecology surrounding sarcastic exchanges on OSNs. We conclude by suggesting ways in which our findings can be included in future work.
In January 2019, the Chinese Communist Party launched the online platform XueXi QiangGuo, which translates into "Learning for the Rise of China." Within two months, XueXi became the top-downloaded item of the month on Apple's App Store in China. In response, we conducted an interview study with 28 active XueXi users to investigate their uses and gratifications of this state-owned online platform. Our results reveal seven key motivations: compliance, self-status seeking, general information seeking, job support, entertainment, patriotism, and learning. This state-owned platform introduced a new model for official information dissemination and political communication through direct surveillance and monitoring, leveraging and fostering emotional attachment, and offering heterogeneous apolitical content. We discuss the intended and unintended ramifications of these components, highlighting the importance of future CSCW research to critically engage with pluralist political narratives situated in varied societies, especially non-Western democracies.
Chatbots are becoming increasingly popular. One promising application for chatbots is to elicit people's self-disclosure of their personal experiences, thoughts, and feelings. As receiving one's deep self-disclosure is critical for mental health professionals to understand people's mental status, chatbots show great potential in the mental health domain. However, there is a lack of research addressing if and how people self-disclose sensitive topics to a real mental health professional (MHP) through a chatbot. In this work, we designed, implemented and evaluated a chatbot that offered three chatting styles; we also conducted a study with 47 participants who were randomly assigned into three groups where each group experienced the chatbot's self-disclosure at varying levels respectively. After using the chatbot for a few weeks, participants were introduced to a MHP and were asked if they would like to share their self-disclosed content with the MHP. If they chose to share, the participants had the option of changing (adding, deleting, and editing) the content they self-disclosed to the chatbot. Comparing participants' self-disclosure data the week before and the week after sharing with the MHP, our results showed that, within each group, the depth of participants' self-disclosure to the chatbot remained after sharing with the MHP; participants exhibited deeper self-disclosure to the MHP through a more self-disclosing chatbot; further, through conversation log analysis, we found that some participants made different edits on their self-disclosed content before sharing it with the MHP. Participants' interview and survey feedback suggested an interaction between participants' trust in the chatbot and their trust in the MHP, which further explained participants' self-disclosure behavior.
Crowdsourcing marketplaces have provided a large number of opportunities for online workers to earn a living. To improve satisfaction and engagement of such workers, who are vital for the sustainability of the marketplaces, recent works have used conversational interfaces to support the execution of a variety of crowdsourcing tasks. The rationale behind using conversational interfaces stems from the potential engagement that conversation can stimulate. Prior works in psychology have also shown that conversational styles can play an important role in communication. There are unexplored opportunities to estimate a worker's conversational style with an end goal of improving worker satisfaction, engagement and quality. Addressing this knowledge gap, we investigate the role of conversational styles in conversational microtask crowdsourcing. To this end, we design a conversational interface which supports task execution, and we propose methods to estimate the conversational style of a worker. Our experimental setup was designed to empirically observe how conversational styles of workers relate with quality-related outcomes. Results show that even a naive supervised classifier can predict the conversation style with high accuracy (80%), and crowd workers with an Involvement conversational style provided a significantly higher output quality, exhibited a higher user engagement and perceived less cognitive task load in comparison to their counterparts. Our findings have important implications on task design with respect to improving worker performance and their engagement in microtask crowdsourcing.
In "smart speaker'' digital assistant systems such as Google Home, there is no visual user interface, so people must learn about the system's capabilities and limitations by experimenting with different questions and commands. However, many new users give up quickly and limit their use to a few simple tasks. This is a problem for both the user and the system. Users who stop trying out new things cannot learn about new features and functionality, and the system receives less data upon which to base future improvements. Symbiosis---a mutually beneficial relationship---between AI systems like digital assistants and people is an important aspect of developing systems that are partners to humans and not just tools. In order to better understand requirements for symbiosis, we investigated the relationship between the types of digital assistant responses and users' subsequent questions, focusing on identifying interactions that were discouraging to users when speaking with a digital assistant. We conducted a user study with 20 participants who completed a series of information seeking tasks using the Google Home, and analyzed transcripts using a method based on applied conversation analysis. We found that the most common response from the Google Home, a version of "Sorry, I'm not sure how to help'', provided no feedback for participants to build on when forming their next question. However, responses that provided somewhat strange but tangentially related answers were actually more helpful for conversational grounding, which extended the interaction. We discuss the connection between grounding and symbiosis, and present recommendations for requirements for forming partnerships with digital assistants.
While mobile instant messaging (MIM) facilitates ubiquitous interpersonal communication, its constant connectivity could build the expectation of an immediate response to messages, and its notifications flood could cause interruptions at inopportune moments. We examine two design concepts for MIM-private status sharing and sender-controlled notifications-that aim to lower the pressure for an immediate reply and reduce unnecessary interruptions by untimely notifications. Private status sharing reactively reveals a customized status with a selected partner(s) only when the partner has sent a message. Sender-controlled notifications give senders the control of choosing whether to send a notification for their own messages. We built MyButler, an Android app prototype that instantiates these two concepts and integrated it with KakaoTalk, a commercial MIM app. During a two-week field study with 11 pairs (5 couples and 6 friend pairs), participants expressed themselves through a total of 210 different statuses, 64.3% of which indicated the current activity or task of the user. Participants reported that private status sharing enabled them to explain their unavailability and relieved the pressure and expectations for timely attendance. We reveal more findings on the types of privately shared statuses and their roles in MIM communication; the in-situ behaviors and patterns of using sender-controlled notifications; and the motivations of MIM users in choosing whether to alert their messages. In terms of message notifications, senders chose to send 25.4% of the messages without any notification. We found that senders' decisions to alert are affected by the receiver's status, their own status to chat, and the possibility of message content exposure to others through notifications. Based on our findings, we draw insights into how the concepts of private status sharing and sender-controlled notifications can be applied in future designs and explorations.
Although reproducibility--the idea that a valid scientific experiment can be repeated with similar results--is integral to our understanding of good scientific practice, it has remained a difficult concept to define precisely. Across scientific disciplines, the increasing prevalence of large datasets, and the computational techniques necessary to manage and analyze those datasets, has prompted new ways of thinking about reproducibility. We present findings from a qualitative study of a NSF--funded two-week workshop developed to introduce an interdisciplinary group of domain scientists to data-management techniques for data-intensive computing, with a focus on reproducible science. Our findings suggest that the introduction of data-related activities promotes a new understanding of reproducibility as a mechanism for local knowledge transfer and collaboration, particularly as regards efficient software reuse.
Peer production projects involve people in many tasks, from editing articles to analyzing datasets. To facilitate mastery of these practices, projects offer a number of learning resources, ranging from project-defined FAQsto individually-oriented search tools and communal discussion boards. However, it is not clear which project resources best support participant learning, overall and at different stages of engagement. We draw onSørensen's framework of forms of presence to distinguish three types of engagement with learning resources:authoritative, agent-centered and communal. We assigned resources from the Gravity Spy citizen-science into these three categories and analyzed trace data recording interactions with resources using a mixed-effects logistic regression with volunteer performance as an outcome variable. The findings suggest that engagement with authoritative resources (e.g., those constructed by project organizers) facilitates performance initially. However, as tasks become more difficult, volunteers seek and benefit from engagement with their own agent-centered resources and community-generated resources. These findings suggest a broader scope for the design of learning resources for peer production
Corporations are increasingly empowering employees to hire on-demand workers via freelance platforms. We interviewed full-time employees of a global technology company who hired freelancers as part of their job responsibilities. While there has been prior work describing freelancers' perspectives there has been little research on those that hire them, the "clients", especially in the corporate context. We found that while freelance platforms reduce many administrative burdens, there are number of conditions in which using freelance platforms in a corporate context creates high transaction costs and power asymmetries that make it difficult for clients to negotiate work rights and responsibilities. This leads corporate employee clients to feel "stuck in the middle" between their employer, the platform, and the freelancer. Ultimately, these transactions costs are a potential barrier to wider adoption. If corporations want to leverage the value of the freelance economy then better guardrails, guidelines, and perhaps even creative technology solutions will be needed.
Depression is common but under-treated in patients with cancer, despite being a major modifiable contributor to morbidity and early mortality. Integrating psychosocial care into cancer services through the team-based Collaborative Care Management (CoCM) model has been proven to be effective in improving patient outcomes in cancer centers. However, there is currently a gap in understanding the challenges that patients and their care team encounter in managing co-morbid cancer and depression in integrated psycho-oncology care settings. Our formative study examines the challenges and needs of CoCM in cancer settings with perspectives from patients, care managers, oncologists, psychiatrists, and administrators, with a focus on technology opportunities to support CoCM. We find that: (1) patients with co-morbid cancer and depression struggle to navigate between their cancer and psychosocial care journeys, and (2) conceptualizing co-morbidities as separate and independent care journeys is insufficient for characterizing this complex care context. We then propose the parallel journeys framework as a conceptual design framework for characterizing challenges that patients and their care team encounter when cancer and psychosocial care journeys interact. We use the challenges discovered through the lens of this framework to highlight and prioritize technology design opportunities for supporting whole-person care for patients with co-morbid cancer and depression.
Interruption dissemination in proactive systems remains a challenge for efficient human-machine collaboration, especially in real-time distributed collaborative environments. In this paper a real-time interruption management system (IMS) is proposed that leverages speech information, the most commonly used and available means of communication within collaborative distributed environments. The key aspect of this paper includes a proposed real-time IMS system that leverages lexical affirmation cues to infer the end of a task or task boundary as a candidate interruption time. The performance results show the proposed real-time lexical Affirmation Cues based Interruption Management System (ACE-IMS) outperforms the current baseline real-time IMS system within the existing literature. ACE-IMS has the potential of reducing disruptive interruptions without incurring excessive missed opportunities to disseminate interruptions by utilizing only the most frequently used mode of human communication: voice. Thereby, providing a promising new baseline to further the system development of real-time interruption management systems within the ever-growing distributed collaborative domain.
For individuals with mental illness, social media platforms are considered spaces for sharing and connection. However, not all expressions of mental illness are treated equally on these platforms. Different aggregates of human and technical control are used to report and ban content, accounts, and communities. Through two years of digital ethnography, including online observation and interviews, with people with eating disorders, we examine the experience of content moderation. We use a constructivist grounded theory approach to analysis that shows how practices of moderation across different platforms have particular consequences for members of marginalized groups, who are pressured to conform and compelled to resist. Above all, we argue that platform moderation is enmeshed with wider processes of conformity to specific versions of mental illness. Practices of moderation reassert certain bodies and experiences as 'normal' and valued, while rejecting others. At the same time, navigating and resisting these normative pressures further inscribes the marginal status of certain individuals. We discuss changes to the ways that platforms handle content related to eating disorders by drawing on the concept of multiplicity to inform design.
Despite their pervasiveness, current text-based conversational agents (chatbots) are predominantly monolingual, while users are often multilingual. It is well-known that multilingual users mix languages while interacting with others, as well as in their interactions with computer systems (such as query formulation in text-/voice-based search interfaces and digital assistants). Linguists refer to this phenomenon as code-mixing or code-switching. Do multilingual users also prefer chatbots that can respond in a code-mixed language over those which cannot? In order to inform the design of chatbots for multilingual users, we conduct a mixed-method user-study (N=91) where we examine how conversational agents, that code-mix and reciprocate the users' mixing choices over multiple conversation turns, are evaluated and perceived by bilingual users. We design a human-in-the-loop chatbot with two different code-mixing policies -- (a) always code-mix irrespective of user behavior, and (b) nudge with subtle code-mixed cues and reciprocate only if the user, in turn, code-mixes. These two are contrasted with a monolingual chatbot that never code-mixed. Users are asked to interact with the bots, and provide ratings on perceived naturalness and personal preference. They are also asked open-ended questions around what they (dis)liked about the bots. Analysis of the chat logs, users' ratings, and qualitative responses reveal that multilingual users strongly prefer chatbots that can code-mix. We find that self-reported language proficiency is the strongest predictor of user preferences. Compared to the Always code-mix policy, Nudging emerges as a low-risk low-gain policy which is equally acceptable to all users. Nudging as a policy is further supported by the observation that users who rate the code-mixing bot higher typically tend to reciprocate the language mixing pattern of the bot. These findings present a first step towards developing conversational systems that are more human-like and engaging by virtue of adapting to the users' linguistic style.
When online platforms rise and fall, sometimes communities fade away, and sometimes they pack their bags and relocate to a new home. To explore the causes and effects of online community migration, we examine transformative fandom, a longstanding, technology-agnostic community surrounding the creation, sharing, and discussion of creative works based on existing media. For over three decades, community members have left and joined many different online spaces, from Usenet to Tumblr to platforms of their own design. Through analysis of 28 in-depth interviews and 1,886 survey responses from fandom participants, we traced these migrations, the reasons behind them, and their impact on the community. Our findings highlight catalysts for migration that provide insights into factors that contribute to success and failure of platforms, including issues surrounding policy, design, and community. Further insights into the disruptive consequences of migrations (such as social fragmentation and lost content) suggest ways that platforms might both support commitment and better support migration when it occurs.
Cooking and eating together is a prominent social experience amongst families. Older adults and their adult children who live apart often communicate about these experiences to stay aware of each other's health and wellbeing. In this paper, we examine current practices surrounding the communication of eating habits and meal preparation between older adults and their adult children living apart. We interviewed 18 older parents and nine adult children to understand their experiences. While most participants found the sharing of eating experiences to be rewarding and enlightening of family health behaviors, family roles and contexts could create tensions around this type of conversation. Applying the lens of symbolic interactionism theory, we examine how changing roles and contexts influence the conversation of eating and meal preparation and how participants manage tensions. We discuss future design opportunities to support family collaboration around food and eating, accounting for the transition of roles and contexts.
People often create social media accounts and pages named after crisis events. We call such accounts and pages Crisis Named Resources (CNRs). CNRs share information about crisis events and are followed by many. Yet, they also appear suddenly (at crisis onset) and in most cases, the owners are unknown. Thus, it can be challenging for audiences in particular to know whether to trust (or not trust) these CNRs and the information they provide. In this study, we conducted surveys and interviews with members of the public and experts in crisis informatics, emergency response, and communication studies to evaluate the trustworthiness of CNRs named after the 2017 Hurricane Irma. Findings showed that participants evaluated trustworthiness based on their perceptions of a CNR's content, information source, profile, and owner. Findings also show that if people perceive that a CNR owner has prior experience in crisis response, can help the public to respond to the event, understands the situation, has the best interests of affected individuals in mind, or will correct misinformation, they tend to trust that CNR. Participant demographics and expertise showed no effect on perceptions of trustworthiness.
Professional designers create mood boards to explore, visualize, and communicate hard-to-express ideas. We present ImageCascade, an intelligent, collaborative ideation tool that combines individual and shared work spaces, as well as collaboration with multiple forms of intelligent agents. In the collection phase, ImageCascade offers fluid transitions between serendipitous discovery of curated images via ImageCascade, combined text- and image-based Semantic search, and intelligent AI suggestions for finding new images. For later composition and reflection, ImageCascade provides semantic labels, generated color palettes, and multiple tag clouds to help communicate the intent of the mood board. A study of nine professional designers revealed nuances in designers' preferences for designer-led, system-led, and mixed-initiative approaches that evolve throughout the design process. We discuss the challenges in creating effective human-computer partnerships for creative activities, and suggest directions for future research.
Domestic and personal photography are topics of longstanding interest to CSCW and HCI researchers. In this paper, we explore these topics in Bungoma County, Kenya. We used interview and observation methods to investigate how photographs are taken, displayed, organized, and shared, in 23 rural households. To more deeply understand participants' photography practices, we also gave them digital cameras, observed what they did with them, and asked them to engage in a photo-elicitation exercise. Our findings draw attention to the ways photographs are "relational objects" - that is, material objects that support the maintenance, reproduction, and transformation of social relations. We then describe these relations: economic (i.e., working as cameramen producing and distributing printed images); family (i.e., parents and children using printed images to tell family histories); and community (i.e., people using printed images to present an idealized self). The introduction of digital cameras into these households did not appear to change these practices; instead, it reinforced them. We discuss how considering relational objects in CSCW/HCI is useful for balancing the technologically determinist perspectives that are the basis of many prior studies of photography in these fields. In particular, we detail how considering the concept provides new perspectives on materiality, as well an alternative to the individualistic perspective, which underlies these communities' understanding of photography.
Live streaming services allow people to concurrently consume and comment on media events with other people in real time. Durkheim's theory of "collective effervescence" suggests that face-to-face encounters in ritual events conjure emotional arousal, so people often feel happier and more excited while watching events like the Super Bowl with family and friends through the television than if they were alone. Does a stronger emotional intensity also occur in live streaming? Using a large-scale dataset of comments posted to news and media events on YouTube, we address this question by examining emotional intensity in live comments versus those produced retrospectively. Results reveal that live comments are overall more emotionally intense than retrospective comments across all temporal periods and all event types examined. Findings support the emotional amplification hypothesis and provide preliminary evidence for shared attention theory in explaining the amplification effect. These findings have important implications for live streaming platforms to optimize resources for content moderation and to improve psychological well-being for content moderators, and more broadly as society grapples with using technology to stay connected during social distancing required by the COVID-19 pandemic.
Search engines are the primary gateways of information. Yet, they do not take into account the credibility of search results. There is a growing concern that YouTube, the second largest search engine and the most popular video-sharing platform, has been promoting and recommending misinformative content for certain search topics. In this study, we audit YouTube to verify those claims. Our audit experiments investigate whether personalization (based on age, gender, geolocation, or watch history) contributes to amplifying misinformation. After shortlisting five popular topics known to contain misinformative content and compiling associated search queries representing them, we conduct two sets of audits-Search-and Watch-misinformative audits. Our audits resulted in a dataset of more than 56K videos compiled to link stance (whether promoting misinformation or not) with the personalization attribute audited. Our videos correspond to three major YouTube components: search results, Up-Next, and Top 5 recommendations. We find that demographics, such as, gender, age, and geolocation do not have a significant effect on amplifying misinformation in returned search results for users with brand new accounts. On the other hand, once a user develops a watch history, these attributes do affect the extent of misinformation recommended to them. Further analyses reveal a filter bubble effect, both in the Top 5 and Up-Next recommendations for all topics, except vaccine controversies; for these topics, watching videos that promote misinformation leads to more misinformative video recommendations. In conclusion, YouTube still has a long way to go to mitigate misinformation on its platform.
Online multiplayer games like Minecraft, gaining increasing popularity among present-day youth, include rich contexts for social interactions but are also rife with interpersonal conflict among players. Research shows that a variety of socio-technical mechanisms (e.g., server rules, chat filters, use of in-game controls to ban players, etc.) aim to limit and/or eliminate social conflict in games like Minecraft. However, avoiding social conflict need not necessarily always be a useful approach. Broadly defined in CSCW literature as a phenomenon that may arise even amidst mutual cooperation, social conflict can yield positive outcomes depending on how it is managed (e.g., [Easterbrook et al.,1993]). In fact, the aforementioned approaches to avoid conflict may not be helpful as they do not help youth understand how to address similar interpersonal differences that may occur in other social settings. Furthermore, prior research has established the value of developing conflict-resolution skills during early adolescence within safe settings, such as school/after-school wellness and prevention interventions (e.g.,[Shure, 1982], [Aber et al., 1998]), for later success in any given interpersonal relationship. While games like Minecraft offer authentic contexts for encountering social conflict, little work thus far has explored how to help youth develop conflict-resolution skills by design interventions within online interest-driven settings. Drawing from prior literature in CSCW, youth wellness and prevention programs, we translated offline evidence-based strategies into the design of an online, after-school program that was run within a moderated Minecraft server. The online program, titled Survival Lab, was designed to promote problem-solving and conflict-resolution skills in youth (ages 8-14 years). We conducted a field study for six months (30 youth participants, four college-age moderators, and one high-school volunteer aged 15 years) using in-game observations and digital trace ethnographic approaches. Our study data reveals that participating youth created community norms and developed insightful solutions to conflicts in Survival Lab. Our research offers three key takeaways. Firstly, online social games like Minecraft lend themselves as feasible settings for the translation of offline evidence-based design strategies in promoting the development of conflict-resolution and other social competencies among youth. Secondly, the design features that support structured and unstructured play while enabling freedom of choice for youth to engage as teams and/or individuals are viable for collective or community-level outcomes. Third and finally, moderators, as caring adults and near-peer mentors, play a vital role in facilitating the development of conflict-resolution skills and interest-driven learning among youth. We discuss the implications of our research for translating offline design to online play-based settings as sites and conclude with recommendations for future work.
Attracting and retaining newcomers is critical and challenging for online production communities such as Wikipedia, both because volunteers need specialized training and are likely to leave before being integrated into the community. In response to these challenges, the Wikimedia Foundation started the Wiki Education Project (Wiki Ed), an online program in which college students edit Wikipedia articles as class assignments. The Wiki Ed program incorporates many components of institutional socialization, a process many conventional organizations successfully use to integrate new employees through formalized on-boarding practices. Research has not adequately investigated whether Wiki Ed and similar programs are effective ways to integrate volunteers in online communities, and, if so, the mechanisms involved. This paper evaluates the Wiki Ed program by comparing 16,819 student editors in 770 Wiki Ed classes with new editors who joined Wikipedia in the conventional way. The evaluation shows that the Wiki Ed students did more work, improved articles more, and were more committed to Wikipedia. For example, compared to new editors who joined Wikipedia in the conventional way they were twice as likely to still be editing Wikipedia a year after their Wiki Ed class was finished. Further, students in classrooms that encouraged joint activity, a key component of institutional socialization, produced better quality work than those in classrooms where students worked independently. These findings are consistent with an interpretation that the Wiki Ed program was successful because it incorporated elements of institutionalized socialization.
Converting widely-available 2D images and videos, captured using an RGB camera, to 3D can help accelerate the training of machine learning systems in spatial reasoning domains ranging from in-home assistive robots to augmented reality to autonomous vehicles. However, automating this task is challenging because it requires not only accurately estimating object location and orientation, but also requires knowing currently unknown camera properties (e.g., focal length). A scalable way to combat this problem is to leverage people's spatial understanding of scenes by crowdsourcing visual annotations of 3D object properties. Unfortunately, getting people to directly estimate 3D properties reliably is difficult due to the limitations of image resolution, human motor accuracy, and people's 3D perception (i.e., humans do not "see" depth like a laser range finder). In this paper, we propose a crowd-machine hybrid approach that jointly uses crowds' approximate measurements of multiple in-scene objects to estimate the 3D state of a single target object. Our approach can generate accurate estimates of the target object by combining heterogeneous knowledge from multiple contributors regarding various different objects that share a spatial relationship with the target object. We evaluate our joint object estimation approach with 363 crowd workers and show that our method can reduce errors in the target object's 3D location estimation by over 40%, while requiring only $35$% as much human time. Our work introduces a novel way to enable groups of people with different perspectives and knowledge to achieve more accurate collective performance on challenging visual annotation tasks.
For modern data analytics, practices from software development are increasingly necessary to manage data, but they must be incorporated alongside other statistical and scientific skills. Therefore, we ask: how does a community recontextualize software development through the unique pressures of their work? To answer this, we explore the analytic community around baseball, or sabermetrics. To discover software development's place in the search for robust statistical insight in sports, we interview 10 participants in the sabermetric community and survey over 120 more data analysts, both in baseball and not. We explore how their work lives at the intersection of science and entertainment, and as a consequence, baseball data serves as an accessible yet deep subject to practice analytic skills. Software development exists within an iterative research process that cycles between defining rigorous statistical methods and preserving the flexibility to chase interesting problems. In this question-driven process, members of the community inhabit several overlapping roles of intentional work, in which software development can become the priority to support research and statistical infrastructure, and we discuss the way that the community can foster the balance of these skills.
We studied the topical preferences of social media campaigns of India's two main political parties by examining the tweets of 7382 politicians during the key phase of campaigning between Jan - May of 2019 in the run up to the 2019 general election. First, we compare the use of self-promotion and opponent attack, and their respective success online by categorizing 1208 most commonly used hashtags accordingly into the two categories. Second, we classify the tweets applying a qualitative typology to hashtags on the subjects of nationalism, corruption, religion and development. We find that the ruling BJP tended to promote itself over attacking the opposition whereas the main challenger INC was more likely to attack than promote itself. Moreover, while the INC gets more retweets on average, the BJP dominates Twitter's trends by flooding the online space with large numbers of tweets. We consider the implications of our findings hold for political communication strategies in democracies across the world.
Information and Communication Technologies (ICTs) can support grassroots social movements toward greateroutreach and better day-to-day communication. In this paper, we present the results of action research with alarge-scale grassroots social movement, the Southern Movement Assembly (SMA), exploring their uses andperceptions of ICTs. We find that the ICTs chosen by the SMA are often at odds with their grassroots cultureof inclusion and participation-which results in inequitable sociotechnical realities within the movement. Forexample, people with technical skills gain more power as they start to control organizational processes. Astechnical skills are most commonly associated with racial, gender, and socioeconomic privileges, this leads toinequitable participation. We conclude by calling for a grassroots culture of technology practice rooted inanalyses of systemic exclusion with a continuous effort to center the marginalized voices and experienceswith technology.
Social conformity occurs when individuals in group settings change their personal opinion to be in agreement with the majority's position. While recent literature frequently reports on conformity in online group settings, the causes for online conformity are yet to be fully understood. This study aims to understand how social presencei.e., the sense of being connected to others via mediated communication, influences conformity among individuals placed in online groups while answering subjective and objective questions. Acknowledging its multifaceted nature, we investigate three aspects of online social presence: user representation (generic vs.user-specific avatars), interactivity (discussion vs.no discussion ), and response visibility (public vs.private ). Our results show an overall conformity rate of 30% and main effects from task objectivity, group size difference between the majority and the minority, and self-confidence on personal answer. Furthermore, we observe an interaction effect between interactivity and response visibility, such that conformity is highest in the presence of peer discussion and public responses, and lowest when these two elements are absent. We conclude with a discussion on the implications of our findings in designing online group settings, accounting for the effects of social presence on conformity.
Much HCI research seeks to contribute to technological agendas that lead to more just and participative labor relations and practices, yet that research also raises concerns about forms of exploitation associated with them. In this paper, we explore how U.S. independent [indie] game developers' socio-technological practices inject forms of labor, capital, and production into the game development industry. Our findings highlight that indie game development 1) seeks to promote an alternative to business models of game development that depend on free and immaterial labor; 2) builds offline networks at different scales to develop collectives that can sustain their production; and 3) emphasizes how distributed collaboration, co-creation, and the use of free tools and middleware make game production more widely accessible. The research contributes to HCI research that seeks to explicate and mitigate emerging forms of exploitation enabled by new technologies and processes. Our critical review of indie developers' practices and strategies also extends the current conceptualization of labor and technology in CSCW.
In recent years, the peer-to-peer economy has grown exponentially, particularly in the ride-sharing sector. This growth has been accompanied by a muddying between the sharing and gig economy, and it has become unclear when an activity is sharing a resource vs. providing a service. To unpack this difference, we studied two successful carpooling groups (university students traveling home and commuting among professionals), which we contrast with previous literature on ride-hailing apps (e.g., Uber). The two communities that we studied differ in that: professionals, had more routine ride-sharing needs based on their commute; and students, arranged rides to return home for school breaks or long weekends. We detail how common needs and backgrounds impacted how carpoolers treated each other. Leveraging these findings, we outline design paths for both the sharing and gig economies to better realize the ideas of the sharing economy.
Race and gender have long sociopolitical histories of classification in technical infrastructures-from the passport to social media. Facial analysis technologies are particularly pertinent to understanding how identity is operationalized in new technical systems. What facial analysis technologies can do is determined by the data available to train and evaluate them with. In this study, we specifically focus on this data by examining how race and gender are defined and annotated in image databases used for facial analysis. We found that the majority of image databases rarely contain underlying source material for how those identities are defined. Further, when they are annotated with race and gender information, database authors rarely describe the process of annotation. Instead, classifications of race and gender are portrayed as insignificant, indisputable, and apolitical. We discuss the limitations of these approaches given the sociohistorical nature of race and gender. We posit that the lack of critical engagement with this nature renders databases opaque and less trustworthy. We conclude by encouraging database authors to address both the histories of classification inherently embedded into race and gender, as well as their positionality in embedding such classifications.
Digital technology that is prevalent in people's everyday lives, including smart home devices, mobile apps and social media, increasingly lack regulations for how the user data can be collected, used or disseminated. The CSCW and the larger computing community continue to evaluate and understand the potential negative impacts of research involving digital technologies. As more research involves digital data, Institutional Review Boards (IRBs) take on the difficult task of evaluating and determining risks--likelihood of potential harms--from digital research. Learning more about IRBs' role in concretizing harm and its likelihood will help us critically examine the current approach to regulating digital research, and has implications for how researchers can reflect on their own data practices. We interviewed 22 U.S.-based IRB members and found that, for the interviewees, "being digital" added a risk. Being digital meant increasing possibilities of confidentiality breach, unintended collection of sensitive information, and unauthorized data reuse. Concurrently, interviewees found it difficult to pinpoint the direct harms that come out of those risks. The ambiguous, messy, and situated contexts of digital research data did not fit neatly into current human subjects research protection protocols. We discuss potential solutions for understanding risks and harms of digital technology and implications for the responsibilities of the CSCW and the larger computing community in conducting digital research.
An important concern in end user development (EUD) is accidentally embedding personal information in program artifacts when sharing them. This issue is particularly important in GUI-based programming-by-demonstration (PBD) systems due to the lack of direct developer control of script contents. Prior studies reported that these privacy concerns were the main barrier to script sharing in EUD. We present a new approach that can identify and obfuscate the potential personal information in GUI-based PBD scripts based on the uniqueness of information entries with respect to the corresponding app GUI context. Compared with the prior approaches, ours supports broader types of personal information beyond explicitly pre-specified ones, requires minimal user effort, addresses the threat of re-identification attacks, and can work with third-party apps from any task domain. Our approach also recovers obfuscated fields locally on the script consumer's side to preserve the shared scripts' transparency, readability, robustness, and generalizability. Our evaluation shows that our approach (1) accurately identifies the potential personal information in scripts across different apps in diverse task domains; (2) allows end-user developers to feel comfortable sharing their own scripts; and (3) enables script consumers to understand the operation of shared scripts despite the obfuscated fields.
Review processes involve complex and often subjective decision-making tasks in which individual reviewers must read and rate submissions, such as a college application, along many relevant dimensions and typically with a rubric in mind. A common part of the work is committee review, where individual reviewers meet to discuss the merits of a particular submission in order to recommend an accept or reject decision. Prior work indicates that visualization and sensemaking support may be beneficial in such processes where reviewers must present the "story" of the applicant under question. We conducted a series of participatory design workshops with reviewers in the domain of holistic college admissions to better understand the challenges and opportunities regarding storytelling. Based on these workshops, we contribute a characterization for how reviewers in this domain construct visual stories, we provide guidance for designing for evidence capture and storytelling, and we draw parallels and distinctions between this domain and other reviewing domains.
When people communicate with each other, their choice of what to say is tied to their perceptions of the audience. For many communication channels, people have some ability to explicitly specify their audience members and the different roles they can play. While existing accounts of communication behavior have largely focused on how people tailor the content of their messages, we focus on the configuring of the audience as a complementary family of decisions in communication. We formulate a general description of audience configuration choices, highlighting key aspects of the audience that people could configure to reflect a range of communicative goals. We then illustrate these ideas via a case study of email usage-a realistic domain where audience configuration choices are particularly fine-grained and explicit in how email senders fill the To and Cc address fields. In a large collection of enterprise emails, we explore how people configure their audiences, finding salient patterns relating a sender's choice of configuration to the types of participants in the email exchange, the content of the message, and the nature of the subsequent interactions. Our formulation and findings show how analyzing audience configurations can enrich and extend existing accounts of communication behavior, and frame research directions on audience configuration decisions in communication and collaboration.
Cultural probes have long been used in HCI to provide designers with glimpses into the local cultures for which they are designing, and thereby inspire novel design proposals. HCI4D/ICTD researchers are increasingly interested in more deeply understanding local cultures in the developing regions where they work, in designing technologies that are not strictly related to socioeconomic development, and in considering new design approaches. However, few use this subjective, design-led method in their research. In this paper, I present a case study detailing my experience designing and deploying cultural probes in Bungoma, Kenya. Returns from my comment cards and digital camera activities draw attention to probe recipients' unique experiences and to Bungoma's distinctive characteristics; they also inspired a series of speculative design proposals. My experience motivates a discussion that elaborates on how a cultural probes approach can benefit HCI4D/ICTD research by raising questions about generalizability, objectivity, and the pursuit of a single solution in design. More broadly, I offer a case study demonstrating an alternative way to approach design in HCI4D/ICTD.
Public benefits programs help people afford necessities like food, housing, and healthcare. In the US, such programs are means-tested: applicants must complete long forms to prove financial distress before receiving aid. Online benefits screening tools provide a gloss of such forms, advising households about their eligibility prior to completing full applications. If incorrectly implemented, screening tools may discourage qualified households from applying for benefits. Unfortunately, errors in screening tools are difficult to detect because they surface one at a time and difficult to contest because unofficial determinations do not generate a paper trail. We introduce a method for auditing such tools in four steps: 1) generate test households, 2) automatically populate screening questions with household information and retrieve determinations, 3) translate eligibility guidelines into computer code to generate ground truth determinations, and 4) identify conflicting determinations to detect errors. We illustrated our method on a real screening tool with households modeled from census data. Our method exposed major errors with corresponding examples to reproduce them. Our work provides a necessary corrective to an already arduous benefits application process.
Social media platforms are widely used by people to report, access, and share information during outbreaks and epidemics. Although government agencies and healthcare institutions in developed regions are increasingly relying on social media to develop epidemic forecasts and outbreak response, there is a limited understanding of how people in developing regions interact on social media during outbreaks and what useful insights this dataset could offer during public health crises. In this work, we examined 28,688 tweets to identify public health issues during dengue epidemic in Bangladesh and found several insights, such as irregularities in dengue diagnosis and treatment, shortage of blood supply for Rh negative blood groups, and high local transmission of dengue during Eid-ul-Adha, that impact disease preparedness and outbreak response. We discuss the opportunities and challenges in analyzing tweets and outline how government agencies and healthcare institutions can use social media health data to inform policy making during public health crises.
Many parents of young children find it challenging to deal with their children's eating problems, and parent--child mealtime interaction is fundamental in forming children's healthy eating habits. In this paper, we present the results of a three-week study through which we deployed a mealtime assistant application, MAMAS, for monitoring parent--child mealtime conversation and food intake with 15 parent--child pairs. Our findings indicate that the use of MAMAS helped 1) increase children's autonomy during mealtime, 2) enhance parents' self-awareness of their words and behaviors, 3) promote the parent--child relationship, and 4) positively influence the mealtime experiences of the entire family. The study also revealed some challenges in eating behavior interventions due to the complex dynamics of childhood eating problems. Based on the findings, we discuss how a mealtime assistant application can be better designed for parents and children with challenging eating behaviors.
A team's early interactions are influential: small behaviors cascade, driving the team either toward successful collaboration or toward fracture. Would a team be more viable if it could undo initial interactional missteps and try again? We introduce a technique that supports online and remote teams in creating multiple parallel worlds: the same team meets many times, led to believe that each convening is with a new team due to pseudonym masking while actual membership remains static. Afterward, the team moves forward with the parallel world with the highest viability by using the same pseudonyms and conversation history from that instance. In two experiments, we find that this technique improves team viability: teams that are reconvened from the highest-viability parallel world are significantly more viable than the same group meeting in a new parallel world. Our work suggests parallel worlds can help teams start off on the right foot - and stay there.
As the use of machine learning (ML) models in product development and data-driven decision-making processes became pervasive in many domains, people's focus on building a well-performing model has increasingly shifted to understanding how their model works. While scholarly interest in model interpretability has grown rapidly in research communities like HCI, ML, and beyond, little is known about how practitioners perceive and aim to provide interpretability in the context of their existing workflows. This lack of understanding of interpretability as practiced may prevent interpretability research from addressing important needs, or lead to unrealistic solutions. To bridge this gap, we conducted 22 semi-structured interviews with industry practitioners to understand how they conceive of and design for interpretability while they plan, build, and use their models. Based on a qualitative analysis of our results, we differentiate interpretability roles, processes, goals and strategies as they exist within organizations making heavy use of ML models. The characterization of interpretability work that emerges from our analysis suggests that model interpretability frequently involves cooperation and mental model comparison between people in different roles, often aimed at building trust not only between people and models but also between people within the organization. We present implications for design that discuss gaps between the interpretability challenges that practitioners face in their practice and approaches proposed in the literature, highlighting possible research directions that can better address real-world needs.
Asian American and Pacific Islander (AAPI) communities use online platforms like Reddit to build capacity for resilience from white hegemony. We conduct interviews with 21 moderators of AAPI subreddits to understand how sociotechnical systems contour and contribute to the marginalization of online communities. We examine marginalization through the analytic framework of decolonization and uncover the threats and tactics that AAPI redditors encounter and employ to decolonize their collective identity. We find that moderators of AAPI subreddits develop collective resilience within their online communities by reclaiming space to confront brigade invasion, recording collective memory to circumvent systemic erasure, and revising cultural narratives to deconstruct colonial mentality. We describe how algorithmic configurations within sociotechnical systems reaffirm existing hegemonic values and discuss how users and designers of sociotechnical systems may work toward resisting white hegemony.
Parents and their school-age children can impact one another's sleep. Most sleep-tracking tools, however, are designed for adults and make it difficult for parents and children to track together. To examine how to design a family-centered sleep tracking tool, we designed DreamCatcher. DreamCatcher is an in-home, interactive, shared display that aggregates data from wrist-worn sleep sensors and self-reported mood. We deployed DreamCatcher as a probe to examine the design space of tracking sleep as a family. Ten families participated in the study probe between 15 and 50 days. This study uses a family systems perspective to explore research questions regarding the feasibility of children actively tracking health data alongside their parents and the effects of tracking and sharing on family dynamics. Our results indicate that children can be active tracking contributors and that having parents and children track together encourages turn-taking and working together. However, there were also moments when family members, in particular parents, felt discomfort from sharing their sleep and mood with other family members. Our research contributes to a growing understanding of designing family centered health-informatics tools to support the combined needs of parents and children.