As more and more forms of AI become prevalent, it becomes increasingly important to understand how people develop mental models of these systems. In this work we study people's mental models of an AI agent in a cooperative word guessing game. We run a study in which people play the game with an AI agent while ``thinking out loud''; through thematic analysis we identify features of the mental models developed by participants. In a large-scale study we have participants play the game with the AI agent online and use a post-game survey to probe their mental model. We find that those who win more often have better estimates of the AI agent's abilities. We present three components---global knowledge, local knowledge, and knowledge distribution---for modeling AI systems and propose that understanding the underlying technology is insufficient for developing appropriate conceptual models---analysis of behavior is also necessary.

As more and more forms of AI become prevalent, it becomes increasingly important to understand how people develop mental models of these systems. In this work we study people's mental models of AI in a cooperative word guessing game. We run think-aloud studies in which people play the game with an AI agent; through thematic analysis we identify features of the mental models developed by participants. In a large-scale study we have participants play the game with the AI agent online and use a post-game survey to probe their mental model. We find that those who win more often have better estimates of the AI agent's abilities. We present three components for modeling AI systems, propose that understanding the underlying technology is insufficient for developing appropriate conceptual models (analysis of behavior is also necessary), and suggest future work for studying the revision of mental models over time.

Gamification, the use of game mechanics in non-gaming applications, has been applied to various systems to encourage desired user behaviors. In this paper, we examine patterns of user activity in an enterprise social network service after the removal of a points-based incentive system. Our results reveal that the removal of the incentive scheme did reduce overall participation via contribution within the SNS. We also describe the strategies by point leaders and observe that users geographically distant from headquarters tended to comment on profiles outside of their home country. Finally, we describe the implications of the removal of extrinsic rewards, such as points and badges, on social software systems, particularly those deployed within an enterprise.

We present the system design and rational for a novel social microcalendar called Timely. Our system has been inspired by previous research on calendaring and popular social network applications, in particular microblogging. Timely provides an open, social space for enterprise users to share their events, socialize, and discover what else is going on in their network and beyond. A detailed analysis of the events shared by users during the site's first 47 days reveals that users willingly share their time commitments despite an existing culture of restricted calendars.

In this paper we feature a set of research projects done at several IBM Research laboratories across the world. The work featured here focuses on the topic of smart social collaboration, which studies, designs, and develops social collaboration principles and technologies that can help customize and enhance existing social collaboration tools to suit specific user needs, including cultural, business, and personal needs.

There is a growth in the popularity of social file sharing systems. This paper describes the design of Cattail, a social file sharing system for the enterprise. Through a 'Recent Events' stream, Cattail supports social navigation and exploratory search by inferring relevant social connections rather than purely relying on user-specified contacts. Social navigation is further supported through pivot browsing from a consolidated history of user actions on an individual's files. Through usage log analysis over an 8-month period, we found that Cattail's novel network inference and social navigation features enabled a net gain of clickthroughs. Interviews with users revealed that this led to increased discovery of relevant people and content. We conclude with a discussion of several possible enhancements to the system. Findings from our research provide a strong foundation for the design of social file sharing systems for enterprise settings.

For large global enterprises, providing adequate resources for organizational acculturation, the process in which employees learn about an organization's culture, remains a challenge. We present results from a survey of 802 users from an enterprise social networking site that identifies two groups of employees (new to the company and geographically distant from headquarters) that perceive higher benefit from using a SNS to learn about the organization's values and beliefs. In addition, we observe regional differences in viewing behaviors between two groups of new employees. These results suggest that a SNS can also potentially contribute to the information-seeking and sense-making activities that underlie organization acculturation.

Blogging in the enterprise is increasingly popular and recent research has shown that there are numerous benefits for both individuals and the organization, e.g. developing reputation or sharing knowledge. However, participation is very low, blogs are often abandoned and few users realize those benefits. We have designed and implemented a novel system -- called Blog Muse -- whose goal is to inspire potential blog writers by connecting them with their audience through a topic-suggestion system. We describe our system design and report results from a 4-week study with 1004 users who installed our tool. Our data indicate that topics requested by users are effective at inspiring bloggers to write and lead to more social interactions around the resulting entries.

How do people use an enterprise file-sharing service? We describe patterns of usage in a social file-sharing service that was deployed in a large multinational enterprise. Factor analyses revealed four factors: Upload & Publicize (regarding one's own files); Annotate & Watch (add information to files and maintain awareness); Discover & Tell (find files uploaded by other users, and communicate to additional users about those files); and Refind (re-use one's own files). We explore the attributes of users who score highly on each of these factors, and we propose implications for design to encourage innovation in usage.

In this work we analyze the behavior on a company-internal social network site to determine which interaction patterns signal closeness between colleagues. Regression analysis suggests that employee behavior on social network sites (SNSs) reveals information about both professional and personal closeness. While some factors are predictive of general closeness (e.g. content recommendations), other factors signal that employees feel personal closeness towards their colleagues, but not professional closeness (e.g. mutual profile commenting). This analysis contributes to our understanding of how SNS behavior reflects relationship multiplexity: the multiple facets of our relationships with SNS connections.

Web 2.0 tools are penetrating into organizations after their successful adoption in the consumer domain (e.g., social networking; sharing of photos, videos, tags, or bookmarks; wiki-based editing). Some of these new tools and the collaborative processes that they support on the large scale are often referred to as Collective Intelligence (CI). The workshop brings together leading researchers and designers who are studying or developing CI tools aimed at workers in organizations. The goal is to further articulate the emerging research agenda for this new CSCW area and define new observed forms of CI in organization. Studies of communities, CI tools, and new methods are discussed.

Most knowledge repositories focus on the role of knowledge-creators. In this paper, by contrast, we examined the work of Lurkers in an enterprise file-sharing service, and we compared their lurking behaviors to the lurking behaviors of users who uploaded files (Uploaders), and users who contributed metadata about files (Contributors). For comparability, we restricted our analyses to the consuming behaviors that are common to the three roles (Uploaders, Contributors, and Lurkers). Independent principal components analysis showed highly similar seven-factor solutions of lurking activities across all three roles, although the relative emphases of those factors varied across roles. Uploaders tended to view and download more groups of files, showed less emphasis on searching for files, and tended to work directly with the file-sharing application, unmediated by external applications. Contributors showed the opposite pattern: more emphasis on searching and responding to recommendations from other users, often via a form of remote access. Lurkers' lurking behaviors were less intense, and showed little difference in emphases among the lurker factors. We use these results, and the published research literature, to motivate a research agenda for lurkers in social media.

Social networking sites have begun to be used in the enterprise as a method of connecting employees. Recommender systems may be used to recommend social contacts in order to increase user engagement, encourage collaboration and facilitate expertise discovery. This paper evaluates the effects of four recommendation algorithms on the network as a whole and the social structure. We demonstrate that depending on the basis of the recommendation algorithm the effects on the network vary greatly and their potential impact should be understood. It is hoped this research can be used as guidance for future recommendation algorithms.

In this paper, we focus on the role that photo viewing plays within a large distributed enterprise. We describe the results of an analysis of users' viewing behavior through log activity and semi-structured interviews with respect to a photo sharing application embedded within an internal social networking site. Specifically, we investigate how these forms of expression can assist in the transmission of the norms and values associated with the culture of the organization through impression formation. We conclude by discussing how photos might act as a resource for newcomers to learn about the various aspects of the organizational culture and offer design suggestions for photo viewing systems within organizations.

Success and sustainability of social networking sites is highly dependent on user participation. To encourage contribution to an opt-in social networking site designed for employees, we have designed and implemented a feature that rewards contribution with points. In our evaluation of the impact of the system, we found that employees are initially motivated to add more content to the site. This paper presents the analysis and design of the point system, the results of our experiment, and our insights regarding future directions derived from our post-experiment user interviews.

Social networking sites support a variety of shared content types such as photos, videos, or music. More structured or form-based social content types are not mainstream but we have started seeing sites evolve that support them. This paper describes the design and use of structured lists in an enterprise social networking system. As a major feature of our shared lists, we introduced the ability to reuse someone else's list. We report the results on the use and reuse of shared lists based on three months of usage data from 285 users and interviews with 9 users. Our findings suggest that despite the structured nature of lists, our users socialize more around lists than photos, and use lists as a medium for self-representation.

Expertise locator systems have been designed to help find experts within organizations. While there are many examples of these systems in the literature, there has not been any systematic analysis of the factors that predict whether a particular expertise search result will be selected for further exploration. This paper describes a study of 67 employees from 21 countries that performed a specific expertise search to find an expert using an expertise locator system. Rank order and social connection information displayed in snippets of search results were found to significantly predict whether a user considers a particular search result for further exploration. Implications for the design of expertise location systems and future research directions are discussed.

Social tagging systems provide users with the opportunity to employ tags in a communicative manner. To explore the use of tags for communication in these systems, we report results from 33 user interviews and employ the concept of social roles to describe audience-oriented tagging, including roles of community-seeker, community-builder, evangelist, publisher, and team-leader. These roles contribute to our understanding of the motivations and rationales behind social tagging in an international company, and suggest new features and services to support social software in the enterprise.

The introduction of a social networking site inside of a large enterprise enables a new method of communication between colleagues, encouraging both personal and professional sharing inside the protected walls of a company intranet. Our analysis of user behavior and interviews presents the case that professionals use internal social networking to build stronger bonds with their weak ties and to reach out to employees they do not know. Their motivations in doing this include connecting on a personal level with coworkers, advancing their career with the company, and campaigning for their projects.

User profiles on today's social networking sites support only a small set of predefined questions. We report on an alternative way for users to richly describe themselves, by entering not only responses, but their own questions as well. Data from 10 months of usage shows that users of a social networking site created thousands of diverse questions and reused existing questions from other users. Our findings suggest that those with highly diverse user profiles have a higher number of friends.

Knowledge workers perform many different activities daily. Each activity defines a distinct work context with different information needs. In this paper we leverage users' activity representations, stored in an activity management system, to automatically recommend resources to support knowledge workers in their current activity. We developed a collaborative activity predictor to both predict the current work activity and measure a resource's relevance to a specific activity. Relevant resources are then displayed in a contextual side bar on the desktop. We describe the system, our new activity-centric search algorithm, and experimental results based on the data from 50 real users.

Traditional social networking sites allow users to enter responses to a set of predefined fields when populating their personal profiles. In the system discussed in this work, freeform 'About You' entries allow users to craft their own questions / topics. We found that this kind of flexibility often leads to low content contributions and infrequent updates. The 'About You' recommender system described in this paper differs from many recommender systems in that it recommends content for users to create, rather than consume. We present empirical data from an experiment with 2,000 users of a social networking site during a one month period. Our findings suggest that users who receive recommendations create more entries and update them more over time. Further, using articulated social network information for recommendations performed better than content-based matching.

Tagclouds are visual presentations of a set of words, typically a set of "tags" selected by some rationale, in which attributes of the text such as size, weight, or color are used to represent features, such as frequency, of the associated terms. This note describes two studies to evaluate the effectiveness of differently constructed tagclouds for the various tasks they can be used to support, including searching, browsing, impression formation and recognition. Based on these studies, we propose a paradigm for evaluating tagclouds and ultimately guidelines for tagcloud construction.

Activity-centric collaboration environments help knowledge workers to manage the context of their shared work activities by providing a representation for an activity and its resources. Activity management systems provide more structure and organization than email to execute the shared activity but, as the number of shared activities increases, it becomes more and more difficult for users to focus on important activities that need their attention. This paper describes a personalized activity prioriti-zation approach implemented on top of the Lotus Connections Activities management system. Our prototype implementation allows each user to view activities ordered by her/his predicted priorities. The predictions are made using a ranking Support Vector Machine model trained with the user’s past interactions with the activities system. We describe the prioritization interface and the results of an offline experiment based on data from 13 users over 6-months. Our results show that our feature set derived from shared activity structures can significantly increase prediction accuracy compared to a recency baseline.

As the use of social networking websites becomes increasingly common, the types of social relationships managed on these sites are becoming more numerous and diverse. This research seeks to gain an understanding of the issues related to managing different social networks through one system, in particular looking at how users of these systems present themselves when they are using one site to keep in contact with both their past social groups from school and their current social connections in the workplace. To do this, we examined online profile pages and interviewed employees at a large software development company who frequently use the website Facebook, a site primarily used by college students and young graduates transitioning into the work force. The outcome of this initial case study is a framework for understanding how users manage self-presentation while maintaining social relationships in heterogeneous networks.

We describe the Dogear Game, which works with an enterprise social bookmarking system. The game is designed to accomplish individual, collaborative, and organization goals. Individual players receive entertainment and learn about their colleagues' bookmarks. The player's colleagues receive recommendations of websites and documents of potential interest to them. And the organization benefits from a richer knowledge-base of bookmarks as recommendations are accepted. The Dogear Game builds on von Ahn's "serious games," useful in motivating and distributing game-like entertaining "work" to a large group of game players. This note presents the design and implementation of a working prototype and some initial user feedback.

We describe a social bookmarking service de-signed for a large enterprise. We discuss design principles addressing online identity, privacy, information discovery (including search and pivot browsing), and service extensi-bility based on a web-friendly architectural style. In addi-tion we describe the key design features of our implementa-tion. We provide the results of an eight week field trial of this enterprise social bookmarking service, including a de-scription of user activities, based on log file analysis. We share the results of a user survey focused on the benefits of the service. The feedback from the user trial, comprising survey results, log file analysis and informal communica-tions, is quite positive and suggests several promising en-hancements to the service. Finally, we discuss potential extension and integration of social bookmarking services with other corporate collaborative applications.

As the number of alerts generated by collaborative applications grows, users receive more unwanted alerts. FeedMe is a general alert management system based on XML feed protocols such as RSS and ATOM. In addition to traditional rule-based alert filtering, FeedMe uses techniques from machine-learning to infer alert preferences based on user feedback. In this paper, we present and evaluate a new collaborative naive Bayes filtering algorithm. Using FeedMe, we collected alert ratings from 33 users over 29 days. We used the data to design and verify the accuracy of the filtering algorithm and provide insights into alert prediction.

This paper describes a new collaboration technology that is based on the support of lightweight, informally structured, opportunistic activities featuring heterogeneous threads of shared items with dynamic membership. We introduce our design concepts, and we provide a detailed analysis of user behavior during a five month field study. We present the patterns of media use that we observed, using a variety of analytical methods including thread clustering and analysis. Major findings include four patterns of media use: communicating, exchanging mixed objects, coordinating, (e.g., of status reports), and semi-archival filing. We observed differential use of various media including highly variable use of chats and surprisingly informal uses of files. We discuss the implications for the design of mixed media collaborative tools to support the work activities of small to medium sized work teams.

Chat Spaces are rich persistent chats that provide light-weight shared workspaces for small to medium-scale group activities. Chat Spaces can accommodate brief, informal interactions (similar to Instant Messaging), and can also support longer-term complex threaded conversations including large numbers of people and shared resources. Our design maps a hierarchical thread representation onto a time-ordered two-column user interface. This mapping allows a user to follow the global dynamics of the entire thread in the chronological column on the left while being able to participate in a selected topical branch in a second column on the right. We also present a dynamic thread map that provides an overview of the entire conversation and supports quick navigation of topical branches in the thread.

How much history of the dialogue should a chat client include? Some chat clients have minimized the dialogue history to deploy the space for other purposes. A theory of conversational coordination suggests that stripping away history raises the cost of conversational grounding, creating problems for both writers and readers. To test this proposition and inform design, we conducted an experiment in which one person instructed another on how to solve a simple puzzle. Participants had chat clients that showed either a single conversational turn or six of them. Having the dialogue history helped collaborators communicate efficiently and led to faster and better task performance. The dialogue history was most useful when the puzzles were more linguistically complex and when instructors could not see the work area. We present evidence of participants adapting their discourse to partially compensate for deficits in the communication media.

This paper describes a new collaboration technology that is carefully poised between informal, ad hoc, easy-to-initiate collaborative tools, vs. more formal, structured, and high-overhead collaborative applications. Our approach focuses on the support of lightweight, informally structured, opportunistic activities featuring heterogeneous threads of shared objects with dynamic membership. We introduce our design concepts, and we provide a detailed first look at data from the first 100 days of usage by 20 researchers and 13 interns, who both confirmed our hypotheses and surprised us by reinventing the technology in several ways.

Information hiding is one of the most important and influential principles in software engineering. It prescribes that software modules hide implementation details from other modules in order to decrease the dependency between them. This separation also decreases the dependency among software developers implementing modules, thus simplifying some aspects of collaboration. A common instantiation of this principle is in the form of application programming interfaces (APIs). We performed a field study of the use of APIs and observed that they served many roles. We observed that APIs were successful indeed in supporting collaboration by serving as contracts among stakeholders as well as by reifying organizational boundaries. However, the separation that they accomplished also hindered other forms of collaboration, particularly among members of different teams. Therefore, we think argue that API's do not only have beneficial purposes. Based on our results, we discuss implications for collaborative software development tools.

The principle of information hiding has been very influential in software engineering since its inception in 1972. This principle prescribes that software modules hide implementation details from other modules in order to decrease their interdependencies. This separation also decreases the dependency among software developers implementing modules, thus simplifying some aspects of collaboration. A common instantiation of this principle is in the form of application programming interfaces (APIs). We performed a qualitative study on how practitioners use APIs in their daily work. Although particularly interested in aspects of collaboration, we report all findings about their observed use. The findings include mundane observations that are predicted by theory, ways that APIs support collaborative software development. But the findings also include some surprises, ways that APIs hinder collaboration. The surprises indicate directions for further improvement of collaborative software development practices and tools.

Organizations have been increasingly providing communities of workers with resources, time, physical space and collaborative technologies to enhance the exchange and flow of knowledge and information. In this paper the results of a multi-organizational survey of four work-based communities is presented. These results offer insights into the resource usage, time use for various work activities, and reported individual, community and organizational benefits. A descriptive model is developed and discussed to show the relationships between community participation, time use, and individual and organizational outcomes.

We provide the first study of instant messaging (IM) based on large samples of users' self reports. Previous studies have relied on ethnographic methods or analysis of server logs. Our self-report approach has its own strengths (large-sample; focus on attitudes, beliefs, and value attributions), as well as weaknesses (self-selection by respondents). We describe the introduction of Lotus Sametime™, an IM product, into three business organizations. Across the three organizations, we found substantially similar patterns in savings (reduced use of other communications channels), attitudes, and social networks. In one organization, we made a detailed study of the maturation of IM over a 24-month period, showing early and stable savings accompanied by much more gradual developments in chat behaviors, control of visibility and awareness, social networks, and attitudes. We conclude with a methodological self-critique, and an outline of an Instant Messaging Maturity Model.

One of the most challenging problems facing builders and facilitators of community networks is to create and sustain social engagement among members. In this paper, we investigate the drivers of social engagement in a community network through the analysis of three data sources: activity logs, a member survey, and the content analysis of the conversation archives. We describe three important ways to encourage and support social engagement in online communities: through system design elements such as conversation channeling and event notification, by various selection criteria for community members, and through facilitation of specific kinds of discussion topics.

Computer simulation of human faces has been an active research area for a long time, resulting in a multitude of facial models and several animation systems. Current interest for this technology is clearly shown by its inclusion in the MPEG-4 standard. However, it is less clear what the actual applications of facial animation (FA) will be. We have therefore undertaken experiments on 190 subjects in order to explore the benefits of FA. Part of the experiment was aimed at exploring the objective benefits, i.e., to see if FA can help users to perform certain tasks more accurately or efficiently. The other part of the experiment was aimed at more subjective benefits, like raising the level of appeal to the user, gaining more users interest, filling in the waiting times for server access so the users do not get bored. We present the experiment design and the results, The results show that FA aids users in understanding spoken numbers in noisy conditions (error rates drop from 16% to 8%); that it can make waiting times more acceptable to the user; and that it makes services more attractive to the users.