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Most studies of social tools examine usage of each tool in isolation. Instead, we explore how online communities (a) combine multiple social tools, and (b) use social tools together with external tools. Based on interviews with community leaders and quantitative analysis of 128 online community spaces, we explored the combined use of six social software tools-wikis, blogs, forums, social bookmarks, social file repositories, and task-management tools. We contribute a detailed characterization of how enterprise online communities combine multiple social tools, adding to our understanding of community behaviors: Communities combine social tools to curate and organize complex information spaces. When combined, each tool is used for limited "core" functions; thus "social" features are not always leveraged for every tool. Leaders and members divide labor by tool boundaries. Our results suggest that an important overlooked aspect of social media concerns how different tools can be effectively combined. While most prior work on communities emphasizes end user content, we identify additional important design activities where community participants curate and organize pre-existing content from multiple tools to serve their community needs.

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Online communities are successful only if they achieve their goals, but there has been little direct study of goals. We analyze novel data characterizing the goals of enterprise online communities, assessing the importance of goals for leaders, how goals influence member perceptions of community value, and how goals relate to success measures proposed in the literature. We find that most communities have multiple goals and common goals are learning, reuse of resources, collaboration, networking, influencing change, and innovation. Leaders and members agree that all of these goals are important, but their perceptions of success on goals do not align with each other, or with commonly used behavioral success measures. We conclude that simple behavioral measures and leader perceptions are not good success metrics, and propose alternatives based on specific goals members and leaders judge most important.

REACH is an intelligent, people-finding system that helps users to find someone in their social directory, especially those whom they do not fully remember or barely know. It analyzes a user's communication and social networking data to automatically extract all the contacts and derive multiple facets to characterize each contact in relation to the user. It then employs a personalized, faceted search to retrieve and present a ranked list of matched contacts based on their properties. A preliminary evaluation shows the effectiveness of our approach.