The Internet has fostered an unconventional and powerful style of collaboration: "wiki" web sites, where every visitor has the power to become an editor. In this paper we investigate the dynamics of Wikipedia, a prominent, thriving wiki. We make three contributions. First, we introduce a new exploratory data analysis tool, the history flow visualization, which is effective in revealing patterns within the wiki context and which we believe will be useful in other collaborative situations as well. Second, we discuss several collaboration patterns highlighted by this visualization tool and corroborate them with statistical analysis. Third, we discuss the implications of these patterns for the design and governance of online collaborative social spaces. We focus on the relevance of authorship, the value of community surveillance in ameliorating antisocial behavior, and how authors with competing perspectives negotiate their differences.

Abstract. We present an end-to-end system that extracts a user’s social network and its members’ contact information given the user’s email inbox. The system identifies unique people in email, finds their Web presence, and automatically fills the fields of a contact address book using conditional random fields—a type of probabilistic model well-suited for such information extraction tasks. By recursively calling itself on new people discovered on the Web, the system builds a social network with multiple degrees of separation from the user. Additionally, a set of expertise-describing keywords are extracted and associated with each person. We outline the collection of statistical and learning components that enable this system, and present experimental results on the real email of two users; we also present results with a simple method of learning transfer, and discuss the capabilities of the system for addressbook population, expert-finding, and social network analysis. 1

We discuss the design, implementation and evaluation of two related visualizations of authors’ activities in Usenet newsgroups. Current Usenet news browsers focus on messages and thread structures while disregarding valuable information about the authors of messages and the participants of the various discussions. Newsgroup Crowds graphically represents the population of authors in a particular newsgroup. Authors are displayed according to the number of messages they contribute to each thread and the number of different days they appear in the space, illustrating and contrasting the interaction patterns of participants within the newsgroup. AuthorLines visualizes a particular author’s posting activity across all newsgroups over a period of one year. This visualization reveals temporal patterns of thread initiation and reply that can broadly characterize the roles authors play in Usenet. We report the results of a user study that explored the value of these interfaces for developing high-level awareness of the activity and population in these conversational spaces. We suggest that interfaces that convey information about the social histories of populations and individuals may support better selection and evaluation of newsgroup content. 1.

In this paper we describe an evaluation of behavioral descriptors generated from an analysis of a large collection of Usenet newsgroup messages. The metrics describe aspects of newsgroup authors ’ behavior over time; such information can aid in filtering, sorting, and recommending content from public discussion spaces like newsgroups. To assess the value of a variety of these behavioral descriptors, we compared 22 participants’ subjective evaluations of authors whose messages they read to behavioral metrics describing the same authors. We found that many metrics, particularly the longevity and frequency of participation, the number of newsgroups to which authors contribute messages, and the amount they contribute to each thread, correlate highly with readers ’ subjective evaluations of the authors.

Process mining techniques allow for the discovery of knowledge based on so-called “event logs”, i.e., a log recording the execution of activities in some business process. Many information systems provide such logs, e.g., most WFM, ERP, CRM, SCM, and B2B systems record transactions in a systematic way. Process mining techniques typically focus on performance and control-flow issues. However, event logs typically also log the performer, e.g., the person initiating or completing some activity. This paper focuses on mining social networks using this information. For example, it is possible to build a social network based on the hand-over of work from one performer to the next. By combining concepts from workflow management and social network analysis, it is possible to discover and analyze social networks. This paper defines metrics, presents a tool, and applies these to a real event log from a Dutch organization.

We investigate how social networks can be used in recommendation generation in taste related domains. Social Filtering (using social networks for neighborhood generation) is compared to Collaborative Filtering with respect to prediction accuracy in the domain of rating clubs. After reviewing background and related work, we present an extensive empirical study where over thousand participants from a social networking community where asked to provide ratings for clubs in Munich. We then compare a typical traditional CFapproach to a social recommender / social filtering approach where friends from the underlying social network are used as rating neighborhood and analyze the experiments statistically. Surprisingly, the social filtering approach outperforms the CF approach in all variants of the experiment. The implications of the experiment for professional and private-life collaborative environments and services where recommendations play a role are discussed. We conclude with future perspectives on social recommender systems, especially in upcoming mobile environments.

Anthropomorphic visualization is a new approach to presenting historical information about participants in online spaces using the human form as the basis for the visualization. Various data about an individual's online behavior are mapped to different parts of a "body", resulting in an abstract yet humanoid representation of a person. We explain the details of the approach and make some initial observations about the visualization in use. We also discuss broader issues relating to presenting data that has been mined from individuals' messages, using the human form to depict this data, and evaluating visualizations used for social purposes.

Online communities need regular maintenance activities such as moderation and data input, tasks that typically fall to community owners. Allowing all members to perform maintenance might make communities more robust and valuable. A key challenge in creating member-maintained communities is that we have little knowledge of how to design interfaces to motivate contributors. Social science theories of motivation may be helpful for making and evaluating design choices. We have used Karau and Williams ’ collective effort model to develop and evaluate designs that motivate members by making salient the unique contributions they could offer to a group. We also show that using oversight to check contributions to a community database of movies increases members ’ motivation to contribute and that peers can provide motivation as well as experts in this domain. Our experiments suggest a number of design elements and guidelines for increasing members ’ motivation to maintain their communities. The Problem Max is the movie czar, the de facto dictator of which movies get into the MovieLens recommender system. He’s the guardian of quality, the finder of movie facts, the defender of decency–the final authority on film. This suits Max well; he’s pleased with the quality of the movie database. Not all MovieLens users agree, however. Max is busy and adds movies slowly, and members sometimes disagree with his criteria for including movies. Useful information (DVD releases, MPAA ratings, etc.) is also not in the database. Max’s community is not unique—many groups have a Max. Most members ’ contributions to an online community pertain to what Preece calls its purpose (Preece 2000). Members post to discussion groups, rate movies, receive recommendations, and read each others ’ blogs. These contributions are visibly important and constitute the day to day business of the community. However, communities need inputs besides conversation, such as moderation, governance, and the maintenance of databases (i.e., members, movies, FAQs, and histories). These duties usually fall to the owners of the

Abstract. Social scientists have stores of data on individuals and groups but relatively little on social interactions, the basis of all social life. That is likely to change due to the spread of computer-mediated interactions that leave a digital record. The flood of available on-line information – from corporate web pages to news groups, wikis, and blogs – has the potential to open up new frontiers in social science research on the diffusion of innovations and beliefs, the self-organization of on-line communities, and the collective behavior of individuals. The Cornell Yesternet project will create a research laboratory for social science research based on the Internet Archive's 40-billion page Web collection. These snapshots of the Web have been captured and archived every two months for nearly ten years. The Yesternet project will copy and reconfigure large portions of this massive collection as a relational database that can be used for research on social and information networks. The Cornell team, composed of social, computer, and information scientists, will develop, test, and refine the necessary tools as part of a series of testbed research applications that track the diffusion of innovation on the Web. The Challenge

In this article we argue that social visualization can motivate contributors to social production projects, such as Wikipedia and open source development. As evidence, we present CodeSaw, a social visualization of open source software development that we studied with real open source communities. CodeSaw mines open source archives to visualize group dynamics that currently lie buried in textual databases. Furthermore, CodeSaw becomes an active social space itself by supporting comments directly inside the visualization. To demonstrate CodeSaw, we apply it to a popular open source project, showing how the visualization reveals group dynamics and individual roles. The article concludes by presenting evidence that CodeSaw, and social visualization more generally, can motivate contributors to social production projects if the visualization leaves the laboratory and makes it to the community visualized.