In Wikipedia, conflict plays a vital role in refining knowledge. Yet some disputes persist without clear resolution or rule violations, resulting in long, circular discussions without moderator intervention. These deter neutral and third-party editors and often end only when one side gives up. In such cases, what is needed is not moderation but mediation-support that helps participants voice perspectives and reach consensus. However, mediation is hard to implement manually, as it requires summarizing complex discussions with a full grasp of both content and the Wikipedia community's culture. One potential solution is delegating this task to AI-but what content should be included in summaries that mediate and facilitate disagreements? Through a three-phase interview study with 14 Wikipedians, we examined how they read and interpret discussions, create their own summaries, and evaluate large language model (LLM)-generated summaries presented as technology probes. Our findings show that Wikipedians expect summaries to include key discussion elements-usernames, topics, arguments, sources, editor behavior, rule violations, and resolution-with particular emphasis on community-related context that reflects insider understanding. Based on these insights, we apply a new theoretical framework grounded in computer-mediated communication (CMC) theories such as common ground theory, warranting theory, and social presence theory to examine Wikipedians' interpretations of user identity. We also discuss design implications for discussion summaries on Wikipedia talk pages.

Developing AI/ML models and incorporating them into a product is complex work. While AI models are generally non-deterministic and have high capability uncertainty, the advent of foundation models further exacerbates the complexity of working with AI and ensuring responsible innovation. This complex work is achieved by not just AI practitioners, but through coordination and collaboration of different groups of practitioners, not all of whom might be experts in AI. Our primary objective is to explore how the tools and systems used by practitioners help achieve this complex work. To that end, we conducted a design space analysis of 18 relevant tools using corresponding research publications and constructed a design space with the dimensions - User, Axis of AI work, Semantics of Use, Tool Architecture, Artifact Type and Availability, and Collaboration Goals. Using these dimensions we derive four spirits of the tool in supporting collaborations - groupware, core practice & communication, community of practice, and visibility & bridging. Through this work, we contribute a conceptual design space of how tools can be designed to support collaborations in AI development and discuss how our design space can be leveraged by system designers and researchers working at the intersection of HCI, CSCW, and AI.

Social media feeds are deeply personal spaces that reflect individual values and preferences. However, top-down, platform-wide content algorithms can reduce users’ sense of agency and fail to account for nuanced experiences and values. Drawing on the paradigm of interactive machine teaching (IMT), an interaction framework for non-expert algorithmic adaptation, we map out a design space for teachable social media feed experiences to empower agential, personalized feed curation. To do so, we conducted a think-aloud study (N = 24) featuring four social media platforms—Instagram, Mastodon, TikTok, and Twitter—to understand key signals users leveraged to determine the value of a post in their feed. We synthesized users’ signals into taxonomies that, when combined with user interviews, inform five design principles that extend IMT into the social media setting. We finally embodied our principles into three feed designs that we present as sensitizing concepts for teachable feed experiences moving forward.

UX designers and researchers who work with AI/ML face different kinds of challenges throughout the design process. Though close collaborations with AI/ML developers and data scientists could address some of these challenges, such interdisciplinary collaborations are non-routine and hard to realize. In this work, we investigate barriers for effective collaboration with ML practitioners, how they affect UX practice of AI/ML applications, and what UX practitioners need to overcome these challenges. We conducted a qualitative study with 14 UX practitioners who are working on AI/ML products as designers or researchers. Our findings show that UX practitioners face challenges in communication, understanding the model and model development processes, establishing ways to collaborate, and reconciling model-centric metrics of evaluation with user-centric outcomes. They described various needs in terms of more visibility into model development processes, access to comprehensible and contextual model information, and hypothetical tools that can potentially support collaboration with ML practitioners and enhance UX design processes. We discuss implications of this research for designing collaborative tools and empowering UX practitioners.

UX practitioners (UXPs) face novel challenges when working with and communicating artificial intelligence (AI) as a design material. We explore how UXPs communicate AI concepts when given hands-on experience training and experimenting with AI models. To do so, we conducted a task-based design study with 27 UXPs in which they prototyped and created a design presentation for a AI-enabled interface while having access to a simple AI model training tool. Through analyzing UXPs’ design presentations and post-activity interviews, we found that although UXPs struggled to clearly communicate some AI concepts, tinkering with AI broadened common ground when communicating with technical stakeholders. UXPs also identified key risks and benefits of AI in their designs, and proposed concrete next steps for both UX and AI work. We conclude with a sensitizing concept and recommendations for design and AI tools to enhance multi-stakeholder communication and collaboration when crafting human-centered AI experiences.

UX practitioners face novel challenges when designing user interfaces for machine learning (ML)-enabled applications. Interactive ML paradigms, like AutoML and interactive machine teaching, lower the barrier for non-expert end users to create, understand, and use ML models, but their application to UX practice is largely unstudied. We conducted a task-based design study with 27 UX practitioners where we asked them to propose a proof-of-concept design for a new ML-enabled application. During the task, our participants were given opportunities to create, test, and modify ML models as part of their workflows. Through a qualitative analysis of our post-task interview, we found that direct, interactive experimentation with ML allowed UX practitioners to tie ML capabilities and underlying data to user goals, compose affordances to enhance end-user interactions with ML, and identify ML-related ethical risks and challenges. We discuss our findings in the context of previously established human-AI guidelines. We also identify some limitations of interactive ML in UX processes and propose research-informed machine teaching as a supplement to future design tools alongside interactive ML.

Understanding the values that collaborators bring to a collaboration is important for the design of new systems. In collaborative systems understanding differing values could help design solutions to mitigate conflicts and more effectively coordinate collaboration. We review prior studies of Commons-Based Peer Production (CBPP) identifying four common value dimensions previously noted as present in CBPP: usage value, social value, ideological value, and monetary value. We use this synthetic framework to analyze a dataset of 32 interviews with contributors to Wikimedia Commons and editors of Wikipedia who use Commons resources. Our analysis supports the prior values categories while expanding how some dimensions are expressed by participants. We also highlight four additional value dimensions that were not previously identified in CBPP: cultural heritage value, rarity value, aesthetic value, and administrative value. We discuss the implications of our findings for the design of collaborative systems.

Continuous blood glucose monitoring (CGM) is a bio-sensing technology designed to help individuals with diabetes understand and manage their blood sugar levels. Better control and management have the promise of extending and improving the quality of life with diabetes. For teenagers with Type 1 Diabetes (T1D), CGM has the potential to allow for a more collaborative T1D management and control experience by sharing real-time blood glucose data with caregivers. To understand how well CGM designs are living up to this promise and to identify potential challenges, we used the Asynchronous Remote Communities (ARC) with 16 teenagers with T1D and their informal caregivers over a 6 month period to investigate families' lived experiences with CGM and its data sharing function. We found three challenges in caregiver-teenager collaboration; lack of visibility into behaviors behind data, difficulties balancing teenage daily life with T1D control, and technical limitations of current CGM systems. We also found three issues that hinder informal caregiving coordination; unclear task assignment, difficulties in maintaining standards of care, and a lack of informal caregiver rotation. We provide a number of design implications based on these findings to better facilitate home care for teenagers' T1D.

Network embedding – finding a low dimensional representation of the nodes with attributes in a hierarchical, directed network remains a challenging problem in the machine learning community. An emerging approach is to embed complex networks – networks of real-world systems – into hyperbolic space due to the fact that hyperbolic space can better naturally represent such a network’s hierarchical structure. Existing hyperbolic embedding approaches, however, cannot handle the embedding of attributed directed networks to an arbitrary embedding dimension. To fill this gap, we introduce HEADNet, for Hyperbolic Embedding of Attributed Directed Networks, an algorithm based on extending previous works for embedding directed attributed networks to Gaussian distributions in hyperbolic space of arbitrary dimension. Through experimentation on a variety of both synthetic and real-world networks, we show that HEADNet can achieve competitive performance on common downstream machine learning tasks, including predicting directed links for previously unseen nodes. HEADNet provides an inductive hyperbolic embedding method for directed attributed networks, which opens the door to hyperbolic manifold learning on a wider range of real-world networks. The source code is freely available athttps://github.com/DavidMcDonald1993/HEADNET.

The period of transition for foster youth into independent adulthood is an important life stage, and one that has yet to be explored in HCI circles. We studied an online community centered on the experiences of former foster youth through the first year of its existence to better understand how online spaces are being used by this population. Our mixed-method study included the coding of all posts from the first year of the online community and offers a mix of quantitative and qualitative findings. These findings include alignments and gaps in an established descriptive framework from the field of social work as it relates to the online communication of former foster youth. It also includes how the domains from the framework co-occur, and some potential implications of these co-occurrences. Future research on this subject is warranted, particularly related to why former foster youth seek online platforms to engage in conversations on these topics and how effective community members perceive the platform to be in safely and securely facilitating their needs.

The study of work practices across two or more collaborative platforms is relatively rare. Participants and researchers have to be competent, or even experts, in both just to begin to make sense of what is happening. With the growing popularity of peer-production systems, the integration of resources across various platforms is more and more common. The framework of stitching is one analytical stance that has been used to describe the cross-platform work to build and highlight informational and social networks. Through a qualitative study with 32 participants who have different foci on Wikipedia and Wikimedia Commons, we reveal their practices in three essential stitching processes, production, curation, and dynamic integration. We highlight how their practices enact stitching and extend the conceptual framework to explain barriers that inhibit effective stitching across platforms. We further discuss implications for research in cross-platform work and design to facilitate Wikipedia-Commons collaboration.

Drones are increasingly used to support humanitarian crises and events that involve dangerous or costly tasks. While drones have great potential for remote collaborative work and aerial telepresence, existing drone technology is limited in its support for synchronous collaboration among multiple remote users. Through three design iterations and evaluations, we prototyped Squadrone, a novel aerial telepresence platform that supports synchronous mid-air collaboration among multiple remote users. We present our design and report results from evaluating our iterations with 13 participants in 3 different collaboration configurations. Our first design iteration validates the basic functionality of the platform. Then, we establish the effectiveness of collaboration using a 360-degree shared aerial display. Finally, we simulate a type of search task in an open environment to see if collaborative telepresence impacts members’ participation. The results validate some initial goals for Squadrone and are used to reflect back on a recent telepresence design framework.

Do models of collaboration among contributors of Wikipedia generalize beyond the larger, western editions of the encyclopedia? In this study, we expanded upon the known collaborative mechanisms on the English Wikipedia and demonstrated that the collaboration model is best captured through the interplay of these mechanisms. We annotated talk page conversations for types of power plays or vies for control over edits that are made to articles, to understand how policy and power play mechanisms in editors' discussions account for behavior in English (EN), Farsi (FA), and Chinese (ZH) language editions of Wikipedia. Our findings show that the same power plays used in EN exist in both FA and ZH but the frequency of their usage differs across the editions. These variations suggest that editors in different language communities value contrasting types of policies to compete for power while discussing and editing articles. Our study contributes to a deeper understanding of how collaboration models developed from a western perspective translate to non-western languages.

Qualitative coding schemas are an essential part of qualitative research used in methods like Grounded Theory. To date, there is no platform to share these coding schemas. Sharing and exchanging these coding schemas has a great potential when it comes to the traceability of qualitative research and well as the re-use of coding schemas. Based on an interview study with qualitative researchers, we propose concepts for integrating a new platform for sharing qualitative coding schemas. Based on theoretical work by Birnholtz and Bietz (2002), it became clear that an easy-to-use system can foster the acceptance and the willingness of researchers to share their coding schemas. We identified three major points to focus for this on: the governance of the platform, the development of the ontology itself and integrating the sharing of qualitative coding schemas into the workflow of researchers by enabling direct upload from the qualitative coding software.

The gender gap in participation on Wikipedia is well established. The impacts of the participation bias on content may be expressed through a number of different behaviors. This research considers potential content biases that may result from efforts to delete content. We collect deletion data on a sample of article pages selected based on a method designed to identify content of likely interest to a specific group of people; men and women in this case. The analysis illustrates that there does not appear to be a systematic bias resulting in more deletions or nominations for deletions against content of likely interest to women. We consider how these results improve our understanding of bias and the ways peer production systems can mitigate the potential for biases.

One promise of commons-based peer production systems is that they provide content (e.g., text, images, sounds, lines of code) for anyone to use, edit, and distribute. However, when some kinds of content are stripped of context and made available for reuse, there are several knock-on effects. In this paper, we discuss how these knock-on effects can complicate the illustration of Wikipedia's encyclopedic articles. We focus on the particularly difficult case of illustrating articles about female anatomy and women's health. Through our analysis, we demonstrate several barriers to illustrating these kinds of articles, including a lack of representative images on Wikimedia Commons, complex issues of consent, and editorial disagreements about the intended audiences for such images. We argue that these barriers are, in part, the unanticipated effects of decontextualization and subsequent recontextualization as images are removed from their original contexts and made available in Wikimedia Commons. Drawing on prior work in language acquisition and archival studies, we critique the design of commons-based peer production systems and suggest ways in which they might be re-designed to account better for the nuances of context. Finally, we raise questions about the costs of recontextualization in peer production systems in general.

The English language Wikipedia is notable for its large number of articles and for the intricate collaborative interactions that create and sustain it. However, 288 other active language editions of Wikipedia have also developed through the coordination of contributing editors. While collaboration in the English Wikipedia has been researched extensively, these other language editions remain understudied. Our study leverages an influential collaboration model based on behaviors in the English Wikipedia as a lens to consider collaborative activity in the Spanish and French language editions. Through an analysis of collaborative interactions across article talk pages, we demonstrate that talk pages, the locus of most collaboration on the English Wikipedia, are used differently in these different language editions. Our study raises broader questions about how results from studies of the English Wikipedia generalize to other language editions, demonstrates the need to account for variations in collaborative behaviors in all language editions of Wikipedia and presents evidence that collaborative practices on the English Wikipedia have changed overtime.

This paper describes the design and development of a preliminary qualitative coding tool as well as a method to improve the process of achieving inter-coder reliability (ICR) in small teams. Software applications that support qualitative coding do not sufficiently assist collaboration among coders and overlook some fundamental issues related to ICR. We propose a new dimension of collaborative coding called 'coders' certainty" and demonstrate its ability to illustrate valuable code disagreements that are missing from existing approaches. Through a case study, we describe the utility of our tool, Code Wizard, and how it helped a group of researchers effectively collaborate to code naturalistic observation data. We report the valuable lessons we learned from the development of our tool and method: (1) identifying coders' certainty constitutes an important part of determining the quality of data analysis and facilitates identifying overlapping and ambiguous codes, (2) making the details of coding process visible helps streamline the coding process and leads to a sense of ownership of the research results, and (3) there is valuable information hidden in coding disagreements that can be leveraged for improving the process of data analysis.

Donation-based support for open, peer production projects such as Wikipedia is an important mechanism for preserving their integrity and independence. For this reason understanding donation behavior and incentives is crucial in this context. In this work, using a dataset of aggregated donation information from Wikimedia's 2015 fund-raising campaign, representing nearly 1 million pages from English and French language versions of Wikipedia, we explore the relationship between the properties of contents of a page and the number of donations on this page. Our results suggest the existence of a reciprocity mechanism, meaning that articles that provide more utility value attract a higher rate of donation. We discuss these and other findings focusing on the impact they may have on the design of banner-based fundraising campaigns. Our findings shed more light on the mechanisms that lead people to donate to Wikipedia and the relation between properties of contents and donations.

This methods paper details an approach for identifying the representativeness of content in a user generated content (UGC) system while also accounting for endogeneity bias. We leverage metadata from an independent content provider to generate sets of commercially viable terms presumed to be of interest to specific audiences linking those terms to UGC. We describe our method and heuristics at a level of detail allowing others to follow or modify it to study both content representativeness and content gaps in UGC systems. We illustrate the method by investigating how well the English language Wikipedia addresses the content interests of four sample audiences: readers of men's and women's periodicals, and readers of political periodicals geared toward either liberal or conservative ideologies. We also share preliminary findings from each case study to demonstrate our method.

Social computing researchers are beginning to apply machine learning tools to classify and analyze social media data. Our interest in understanding politeness in an online community focused our attention on tools that would help automate politeness analysis. This paper highlights one popular classification tool designed to score the politeness of text. Our application of this tool to Wikipedia data yielded some unexpected results. Those unexpected results led us to question how the tool worked and its effectiveness relative to human judgment and classification. We designed a user study to revalidate the tool with crowdworkers labeling samples of content from Wikipedia talk pages, imitating the original efforts to validate the tool. This revalidation points to challenges for automatic labeling. Based on our results, this paper reconsiders politeness in online communities as well as broader trends in the use of machine classifiers in social computing research.

OBJECTIVE: Online health communities offer a diverse peer support base, yet users can struggle to identify suitable peer mentors as these communities grow. To facilitate mentoring connections, we designed a peer-matching system that automatically profiles and recommends peer mentors to mentees based on person-generated health data (PGHD). This study examined the profile characteristics that mentees value when choosing a peer mentor. MATERIALS AND METHODS: Through a mixed-methods user study, in which cancer patients and caregivers evaluated peer mentor recommendations, we examined the relative importance of four possible profile elements: health interests, language style, demographics, and sample posts. Playing the role of mentees, the study participants ranked mentors, then rated both the likelihood that they would hypothetically contact each mentor and the helpfulness of each profile element in helping the make that decision. We analyzed the participants' ratings with linear regression and qualitatively analyzed participants' feedback for emerging themes about choosing mentors and improving profile design. RESULTS: Of the four profile elements, only sample posts were a significant predictor for the likelihood of a mentee contacting a mentor. Communication cues embedded in posts were critical for helping the participants choose a compatible mentor. Qualitative themes offer insight into the interpersonal characteristics that mentees sought in peer mentors, including being knowledgeable, sociable, and articulate. Additionally, the participants emphasized the need for streamlined profiles that minimize the time required to choose a mentor. CONCLUSION: Peer-matching systems in online health communities offer a promising approach for leveraging PGHD to connect patients. Our findings point to interpersonal communication cues embedded in PGHD that could prove critical for building mentoring relationships among the growing membership of online health communities.

Online health communities provide a rich source of expertise from experienced patients, but uncovering “peer mentors” with shared circumstances is like finding a needle in a haystack—a problem that will escalate as these communities grow and diversify. We investigated interactive health interest profiles (HIPs) that summarize health-related terms extracted from users’ community posts. Through iterative design, we explored practical designs that accommodate differences in users’ community participation in three HIP prototypes: Text , Word Cloud, and Timeline . By comparing prototype usability with patients and design experts, we found that patients accurately used each prototype but completed some tasks faster with the Timeline HIP . Despite this advantage, patients preferred the Text HIP . Design experts and patients agreed that simple data overviews and granular details with salient cues that invite interactivity are key design considerations for HIPs. Findings offer key design considerations for HIPs that patients find most useful when forging critical connections.

Social botnets have become an important phenomenon on social media. There are many ways in which social bots can disrupt or influence online discourse, such as, spam hashtags, scam twitter users, and astroturfing. In this paper we considered one specific social botnet in Twitter to understand how it grows over time, how the content of tweets by the social botnet differ from regular users in the same dataset, and lastly, how the social botnet may have influenced the relevant discussions. Our analysis is based on a qualitative coding for approximately 3000 tweets in Arabic and English from the Syrian social bot that was active for 35 weeks on Twitter before it was shutdown. We find that the growth, behavior and content of this particular botnet did not specifically align with common conceptions of botnets. Further we identify interesting aspects of the botnet that distinguish it from regular users.

Many research applications and popular commercial applications include features for sharing personally collected data with others in social awareness streams. Prior work has identified several barriers to use as well as discrepancies between designer goals and how these features are used in practice. We develop a framework for designing and evaluating these features based on an extensive review of prior literature. We demonstrate the value of this framework by analyzing physical activity sharing on Twitter, coding 4,771 tweets and their responses and gathering 444 reactions from 97 potential tweet recipients, learning that specific user-generated content leads to more responses and is better received by the post audience. We conclude by extending our findings to other sharing problems and discussing the value of our design framework.

We report a study of Wikipedia in which we use a mixed-methods approach to understand how participation in specialized workgroups called WikiProjects has changed over the life of the encyclopedia. While previous work has analyzed the work of WikiProjects in supporting the development of articles within particular subject domains, the collaborative role of WikiProjects that do not fit this conventional mold has not been empirically examined. We combine content analysis, interviews and analysis of edit logs to identify and characterize these alternative WikiProjects and the work they do. Our findings suggest that WikiProject participation reflects community concerns and shifts in the community's conception of valued work over the past six years. We discuss implications for other open collaborations that need flexible, adaptable coordination mechanisms to support a range of content creation, curation and community maintenance tasks.

Many people struggle with their weight and are turning to online communities for social and informational support. The aim of this study is to understand the issues commonly discussed in online weight loss communities. Through observation and content analysis of threads in one specific weight loss community, we identified 17 distinct categories discussed by the participants. We detail four categories specifically: Personal Experience, Consumption Choices, Dieting Strategy, and Exercise. Our analysis describes some key user roles and states that often relate to different phases of a person's weight loss journey. We identify a set of transient states, which are not proper roles but are significant in an online community where individuals are attempting to change their own behaviors. We close with design suggestions for encouraging and maintaining participant engagement in an online health community

In online groups, increasing explicit coordination can increase group cohesion and member productivity. On Wikipedia, groups called WikiProjects employ a variety of explicit coordination mechanisms to motivate and structure member contribution, with the goal of creating and improving articles related to particular topics. However, while explicit coordination works well for coordinating article-level actions, coordinating group tasks and tracking progress towards group goals that involve tracking hundreds or thousands of articles over time requires different coordination strategies.

WikiProjects have contributed to Wikipedia's success in important ways, yet the range of work that WikiProjects perform and the way they coordinate that work remains largely unexplored. In this study, we perform a content analysis of 788 work-related discussions from the talk pages of 138 WikiProjects in order to understand the role WikiProjects play in collaborative work on Wikipedia. We find that the editors use WikiProjects to coordinate a wide variety of work activities beyond content production and that non-members play an active role in that work. Our research suggests that WikiProject collaboration is less structured and more open than that of many virtual teams and that WikiProjects may function more like FLOSS projects than traditional groups.

The relationships and work that facilitate content creation in large online contributor system are not always visible. Social translucence is a stance toward the design of systems that allows users to better understand collaborative system participation through awareness of contributions and interactions. Like many socio-technical constructs, social translucence is not something that can be simply added after a system is built; it should be at the core of system design. In this paper, we conduct a domain analysis to understand the space of architectural support required to facilitate social translucence in systems. We describe an instantiation of those requirements as a system architecture that relies on data from Wikipedia and illustrate how translucence can be propagated to some basic visualizations which we have created for Wikipedia users. We close with some reflections on the state of social translucence research and some openings for this important design perspective.

Category systems have traditionally been created by small committees of people who had authority over the system they were designing. With the rise of large-scale social media systems, category schemes are being created by groups with differing perspectives, values, and expectations for how categories will be used. Prior studies of social tagging and folksonomy focused on the application and evolution of the collective category scheme, but struggled to uncover some of the collective rationale undergirding the decision-making processes in those schemes. In this paper, we qualitatively analyze the early discussions among editors of Wikipedia about the design and creation of its category system. We highlight three themes that dominated the discussion: hierarchy, scope and navigation, and relate these themes to their more formal roots in the information science literature. We distill out four styles of collaboration with regard to category systems that apply broadly to social tagging and other folksonomies. We conclude the paper with implications for collaborative tools and category systems as applied to large-scale collaborative systems.

The growing volume of digital music, photos and video challenges media management software and organizing schemes alike. Through 20 in situ, two hour interviews we explored the when, why and how of our participants' organizational schemes. We sought and studied significantly larger media collections than in previous studies. For these larger media collections some common assumptions like the distinction between popular and classical music collectors do not hold. Our analysis identifies organizing schemes commonly used on a day-to-day basis. We found that participants often rely on overrides or exceptions to their organizational schemes that they consider idiosyncrasies. However, our findings illustrate that those idiosyncratic behaviors are more common than participants believe. Our analysis reflects upon prior research and on the relationship between physical and digital artifacts, relating computer supported cooperative work systems to contemporary media management applications. Our findings can inform the design of media management and media player software.

Managing personal aspects of health is challenging for many patients, particularly those facing a serious condition such as cancer. Finding experienced patients, who can share their knowledge from managing a similar health situation, is of tremendous value. Users of health-related social software form a large base of such knowledge, yet these tools often lack features needed to locate peers with expertise. Informed directly by our field work with breast cancer patients, we designed a patient expertise locator for users of online health communities. Using feedback from two focus groups with breast cancer survivors, we took our design through two iterations. Focus groups concluded that expertise locating features proved useful for extending social software. They guided design enhancements by suggesting granular user control through (1) multiple mechanisms to identify expertise, (2) detailed user profiles to select expertise, and (3) varied collaboration levels. Our user-centered approach links field work to design through close collaboration with patients. By illustrating trade-offs made when sharing sensitive health information, our findings inform the incorporation of expertise locating features into social software for patients.

More and more interaction is becoming code-based. Indeed, in online worlds, it is all there is. If software is providing a new basis for social interaction, then changing the infrastructure of interaction may necessarily change social interaction in important ways. As such, it is critical to understand the implications of code - we want to know what the use of code means for socio-technical design.In this paper, based on an ethnographic study of an online game, we examine social regulation in an online game world as a case study of socio-technical design using code. We wanted to know how changing interaction based in code conditioned use in our site. We found that code changed social regulation in three specific ways. First, code made some user actions that were socially unwanted to be immediately visible. Second, code could prevent some actions from occurring or punish users immediately. Finally, software was not able to see all action. Some user actions were too nuanced or subtle for code to catch; others were too ambiguous to place into code. Following Agre, we argue i that a grammar of action resulting from the use of code limits the kinds of behaviors that can be seen and dealt with.These findings suggest that there is more than just a gap between the social world and technical capabilities. There are new possibilities, tradeoffs, and limitations that must be considered in socio-technical design, and all come simultaneously.

In this paper, we propose design strategies for persuasive technologies that help people who want to change their everyday behaviors. Our strategies use theory and prior work to substantially extend a set of existing design goals. Our extensions specifically account for social characteristics and other tactics that should be supported by persuasive technologies that target long-term discretionary use throughout everyday life. We used these strategies to design and build a system that encourages people to lead a physically active lifestyle. Results from two field studies of the system - a three-week trial and a three-month experiment - have shown that the system was successful at helping people maintain a more physically active lifestyle and validate the usefulness of the strategies.

Communicating the subtleties of a craft technique, like putting a zipper into a garment or throwing a clay pot, can be challenging even when working side by side. Yet How-To content - including text, images, animations, and videos - is available online for a wide variety of crafts. We interviewed people engaged in various crafts to investigate how online resources contributed to their craft practice. We found that participants sought creative inspiration as well as technical clarification online. In this domain, keyword search can be difficult, so supplemental strategies are used. Participants sought information iteratively, because they often needed to enact their knowledge in order to evaluate it. Our description of people learning how allows us to elaborate existing understandings of information-seeking behavior by considering how search originates and is evaluated in knowledge domains involving physical objects and physical processes.

Coping with a new health issue often requires individuals to acquire knowledge and skills to manage personal health. Many patients turn to one another for experiential expertise outside the formal bounds of the health-care system. Internet-based social software can facilitate expertise sharing among patients, but provides only limited ways for users to locate sources of patient expertise. Although much prior research has investigated expertise location and systems to augment expertise sharing in workplace organizations, the transferability of this knowledge to other contexts, such as personal health, is unclear. Guided by expertise locating frameworks drawn from prior work, we conducted a field study to investigate expertise locating in the informal and everyday context of women diagnosed with breast cancer. Similarities between patients' expertise locating practices and practices of professionals in workplace organizations suggest similar support strategies could apply in both contexts. However, unlike professionals, unsolicited advice often triggered patients to locate expertise. They identified expertise through various forms of gatekeeping. The high-stakes nature of problems patients faced also led them to use triangulation strategies in anticipation of breakdowns in expertise location. Based on these key differences, we explored five design additions to social software that could support patients in their critical need to locate patient expertise.

As people collect more and more digital music, photos, and video, the growing scale of the collection challenges how families share and collaborate around home media collections. We studied the intersection between physical and digital media collections. Through 20 two hour, in home interviews, we explored the when, why, and how of the households' organization, access and sharing. Our grounded approach is framed through the use of the media lifecycle and the spectrum of intimacy of the collector and others involved in the stages of the lifecycle. We found a range of accommodations to facilitate collaboration around media collections in the home. For example, media collections often begin with an individual, but as they become shared and integrated into a household, a member of the household will often play a key curatorial role that includes making changes to the organizational scheme, setting aside sub-collections and selecting items to play that account for the entire household's taste. Our findings identify key practices that can inform the design of future media software for the home.

Goal-setting has been shown to be an effective strategy for changing behavior; therefore employing goal-setting in persuasive technologies could be an effective way to encourage behavior change. In our work, we are developing persuasive technologies to encourage individuals to live healthy lifestyles with a focus on being physically active. As part of our investigations, we have explored individuals' reactions to goal-setting, specifically goal sources (i.e., who should set the individual's goal) and goal timeframes (i.e., over what time period should an individual have to achieve the goal). In this paper, we present our findings related to various approaches for implementing goal-setting in a persuasive technology to encourage physical activity.

Recent advances in small inexpensive sensors, low-power processing, and activity modeling have enabled applications that use on-body sensing and machine learning to infer people's activities throughout everyday life. To address the growing rate of sedentary lifestyles, we have developed a system, UbiFit Garden, which uses these technologies and a personal, mobile display to encourage physical activity. We conducted a 3-week field trial in which 12 participants used the system and report findings focusing on their experiences with the sensing and activity inference. We discuss key implications for systems that use on-body sensing and activity inference to encourage physical activity.

Successful online communities have complex cooperative arrangements, articulations of work, and integration practices. They require technical infrastructure to support a broad division of labor. Yet the research literature lacks empirical studies that detail which types of work are valued by participants in an online community. A content analysis of Wikipedia barnstars -- personalized tokens of appreciation given to participants -- reveals a wide range of valued work extending far beyond simple editing to include social support, administrative actions, and types of articulation work. Our analysis develops a theoretical lens for understanding how wiki software supports the creation of articulations of work. We give implications of our results for communities engaged in large-scale collaborations.

Personal, mobile displays, such as those on mobile phones, are ubiquitous, yet for the most part, underutilized. We present results from a field experiment that investigated the effectiveness of these displays as a means for improving awareness of daily life (in our case, self-monitoring of physical activity). Twenty-eight participants in three experimental conditions used our UbiFit system for a period of three months in their day-to-day lives over the winter holiday season. Our results show, for example, that participants who had an awareness display were able to maintain their physical activity level (even during the holidays), while the level of physical activity for participants who did not have an awareness display dropped significantly. We discuss our results and their general implications for the use of everyday mobile devices as awareness displays.

In this paper we describe a system for creating and exposing relationships between documents: a user's interaction with digital objects (like documents) is interpreted as links - to be discovered and maintained by the system. Such relationships are created automatically, requiring no priming by the user. Using a very simple set of heuristics, we demonstrate the uniquely useful relationships that can be established between documents that have been touched by the user. Furthermore, this mechanism for relationship building is media agnostic, thus discovering relationships that would not be found by conventional content based approaches. We describe a proof-of-concept implementation of this basic idea and discuss a couple of natural expansions of the scope of user activity monitoring.

Academic conferences provide a social space for people to present their work and interact with one another. However, opportunities for interaction are unevenly distributed among the attendees. We seek to extend the opportunities for interaction among attendees by using technology to enable them to reveal information about their background and interests in different settings. We evaluate a suite of applications that augment three physical social spaces at an academic conference. The applications were designed to augment formal conference paper sessions and informal breaks. A mixture of qualitative observation and survey response data are used to frame the impacts from both individual and group perspectives. Respondents reported on their interactions and serendipitous findings of shared interests with other attendees. However, some respondents also identify distracting aspects of the augmentation. Our discussion relates these results to existing theory of group behavior in public places and how these social space augmentations relate to awareness as well as the problem of shared interaction models.

When large groups cooperate, issues of conflict and control surface because of differences in perspective. Managing such diverse views is a persistent problem in cooperative group work. The Wikipedian community has responded with an evolving body of policies that provide shared principles, processes, and strategies for collaboration. We employ a grounded approach to study a sample of active talk pages and examine how policies are employed as contributors work towards consensus. Although policies help build a stronger community, we find that ambiguities in policies give rise to power plays. This lens demonstrates that support for mass collaboration must take into account policy and power.

Social matching systems bring people together in both physical and online spaces. They have the potential to increase social interaction and foster collaboration. However, social matching systems lack a clear intellectual foundation: the nature of the design space, the key research challenges, and the roster of appropriate methods are all ill-defined. This article begins to remedy the situation. It clarifies the scope of social matching systems by distinguishing them from other recommender systems and related systems and techniques. It identifies a set of issues that characterize the design space of social matching systems and shows how existing systems explore different points within the design space. It also reviews selected social science results that can provide input into system design. Most important, the article presents a research agenda organized around a set of claims. The claims embody our understanding of what issues are most important to investigate, our beliefs about what is most likely to be true, and our suggestions of specific research directions to pursue.

Academic conferences provide a social space for people to present their work, learn about others' work, and interact informally with one another. However, opportunities for interaction are unevenly distributed among the attendees. We seek to extend these opportunities by allowing attendees to easily reveal something about their background and interests in different settings through the use of proactive displays: computer displays coupled with sensors that can sense and respond to the people nearby. We designed, implemented and deployed a suite of proactive display applications at a recent academic conference: AutoSpeakerID augmented formal conference paper sessions; Ticket2Talk augmented informal coffee breaks. A mixture of qualitative observation and survey response data are used to frame the impacts of these applications from both individual and group perspectives, highlighting the creation of new opportunities for both interaction and distraction. We end with a discussion of how these social space augmentations relate to the concepts of focus and nimbus as well as the problem of shared interaction models.

Collaborative authoring is a common workplace task. Yet, despite improvements in word processors, communication software, and file sharing, many problems continue to plague co-authors. We conducted a qualitative study in a setting where participants are loosely connected, physically separated, and work together over a period of 4-9 months to author a complex technical document-a clinical trial protocol. Our study differs from most prior work in that the collaboration is longer-lived, and that the collaborators do not share equivalent status, background, nor domains of expertise. Our data demonstrates that the participants do not share the same view or representation of the authoring process, even though it has a long organizational history. Nonetheless, the participants can still coordinate their activity while maintaining only partially consistent representations of what they are doing. We contend that partial consistency in the participants' concept of the collaborative process is a feature for their asynchronous collaboration at a distance. Based on our findings we suggest a number of improvements for both tools and tool usage that have direct impact on support for collaborative authoring.

Studies of information seeking and workplace collaboration often find that social relationships are a strong factor in determining who collaborates with whom. Social networks provide one means of visualizing existing and potential interaction in organizational settings. Groupware designers are using social networks to make systems more sensitive to social situations and guide users toward effective collaborations. Yet, the implications of embedding social networks in systems have not been systematically studied. This paper details an evaluation of two different social networks used in a system to recommend individuals for possible collaboration. The system matches people looking for expertise with individuals likely to have expertise. The effectiveness of social networks for matching individuals is evaluated and compared. One finding is that social networks embedded into systems do not match individuals' perceptions of their personal social network. This finding and others raise issues for the use of social networks in groupware. Based on the evaluation results, several design considerations are discussed.

Finding a person who has the experience to solve a specific precif is an imporKRI application ofr1SSWRI-1W systems to a difficultorcultRA1AWAR pr11Rr Pr11 systems have made attempts to implement solutions to thisprsRDAK but few systems haveunderC12 systematicuser evaluation. This wor descrDWA a systematic evaluation of theExperWAW Recommender (ER), a system thatrtRAKAC1R people who ar likely to haveexper1Ain a specific prcific ER and the or2-1CRICCW11 contextfor which it was designedar descredR toprAA2R a basisfor underDM-2RIC this evaluation.Pral t o conducting the evaluation, a baselineexpereRA2 showed that peoplear rpleRWKSgood at judging coworRD1- experRD when given an appr1RIM2W context. This finding prdingR a way to demonstrKW the effectiveness of ER bycompar-R ER's per-1MRIMK torRAAMM by coworARIM The evaluation, the design, andrdR2SW ar descrSRI in detail. ThereRDMD suggest that the parDMARIM-W agr with therRAKWAKRIM-W11 made by ER, and that ER significantly outpercant other experrc rperrcan systems when compared using similar metrics.

Locating the expertise necessary to solve difficult problems is a nuanced social and collaborative problem. In organizations, some people assist others in locating expertise by making referrals. People who make referrals fill key organizational roles that have been identified by CSCW and affiliated research. Expertise locating systems are not designed to replace people who fill these key organizational roles. Instead, expertise locating systems attempt to decrease workload and support people who have no other options. Recommendation systems are collaborative software that can be applied to expertise locating. This work describes a general recommendation architecture that is grounded in a field study of expertise locating. Our expertise recommendation system details the work necessary to fit expertise recommendation to a work setting. The architecture and implementation begin to tease apart the technical aspects of providing good recommendations from social and collaborative concerns.

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As people participate in the thousands of global conversations that comprise Usenet news, one thing they do is post their opinions of web resources. Phoaks is a collaborative filtering system that continuously parses, classifies, abstracts and tallies those opinions. About 3,500 users per day consult Phoaks web pages that reflect the results. Phoaks also features a general architecture for building similar collaborative filtering interfaces to conversational data. We report here on the Phoaks resource recommendation interface, the architecture, and the issues and experience that make up its rationale. Keywords human-computer interaction, human interface, computersupported cooperative work, organizational computing, social filtering, collaborative filtering, data mining,

This research examines a collaborative solution to a common problem, that of providing help to distributed users. The Answer Garden 2 system provides a secondgeneration architecture for organizational and community memory applications. After describing the need for Answer Garden 2’s functionality, we describe the architecture of the system and two underlying systems, the Cafe ConstructionKit and Collaborative Refinery. We also present detailed descriptions of the collaborative help and collaborative refining facilities in the Answer Garden 2 system.