The rising popularity of social media in the enterprise presents new opportunities for one of the organization's most important needs--expertise location. Social media data can be very useful for expertise mining due to the variety of existing applications, the rich metadata, and the diversity of user associations with content. In this work, we provide an extensive study that explores the use of social media to infer expertise within a large global organization. We examine eight different social media applications by evaluating the data they produce through a large user survey, with 670 enterprise social media users. We distinguish between two semantics that relate a user to a topic: expertise in the topic and interest in it and compare these two semantics across the different social media applications.

This paper presents Faces, an application built to enable effective people search in the enterprise. We take advantage of the popularity Faces has gained within a globally distributed enterprise to provide an extensive analysis of how and why people search is used within the organization. Our study is primarily based on an analysis of the Faces query log over a period of more than four months, with over a million queries and tens of thousands of users. The analysis results are presented across four dimensions: queries, users, clicks, and actions, and lay the foundation for further advancement and research on the topic.

Recent studies on people recommendation have focused on suggesting people the user already knows. In this work, we use social media behavioral data to recommend people the user is not likely to know, but nonetheless may be interested in. Our evaluation is based on an extensive user study with 516 participants within a large enterprise and includes both quantitative and qualitative results. We found that many employees valued the recommendations, even if only one or two of nine recommendations were interesting strangers. Based on these results, we discuss potential deployment routes and design implications for a stranger recommendation feature.

No abstract available.

The CSCW conference is celebrating its 20th birthday. This is a perfect time to analyze the coherence of the field, to examine whether it has a solid core or sub-communities, and to identify various patterns of its development. In this paper we analyze the structure of the CSCW conference using structural analysis of the citation graph of CSCW and related publications. We identify the conference's core and most prominent clusters. We also define a measure to identify chasm-papers, namely papers cited significantly more outside the conference than within, and analyze such papers.

Collaboration plays a vital role in today's new business environment. Knowledge that resides within people's heads has become an invaluable resource. Many formal tools, such as e-mail or teamrooms, have been introduced to support formal collaboration and have been studied extensively. However, support for informal communication is still in its infancy. Much work has been done to analyze the functions that informal communication plays in the workplace. Recently, several studies have evaluated the roles that instant messaging (IM) plays in similar settings. Research shows that in the workplace, IM is used primarily for work-related purposes and accelerates the completion of important business tasks. Clearly, new tools that combine both formal and informal interaction can bring organizations tremendous rewards. ReachOut is a tool for semi-persistent collaboration and peer support developed by the Collaboration Technologies Group at the IBM Haifa Research Lab. This paper studies the role ReachOut plays in the workplace. We analyzed the collaboration activity of the community of IBM Haifa Labs employees who used ReachOut for a period of two months. As a result, we summarize the important functions played by tools that bridge between formal and informal communication in a workplace-based community.