Social data mining systems enable people to share opinions and obtain a benefit from each other's experience. These systems do this by mining and redistributing information from computational records of social activity such as Usenet messages, system usage history, We report here on social data mining applications, systems that mine information from the structure and content of web pages and provide an exploratory information workspace interface. We carried out experiments that yielded positive answers to both evaluation questions. First, a number of automatically computable features about web sites do a good job of predicting expert quality judgments about sites. Second, compared to popular web search interfaces, the Topic Shop interface to this information lets users select significantly higher quality sites, in less time and with less effort, and to organize the sites they select into personally meaningful collections quickly and easily. We conclude by discussing how our results may be applied and considering how they touch on general issues concerning quality, expertise, and consensus The motivation for the social data mining approach goes back at least to Vannevar Bush's As We May Think essay. Bush envisioned scholars blazing trails through repositories of information and realized that these trails subsequently could be followed by others. Everyone could walk in the footsteps of the masters. In our work, we have formulated a similar intuition using the metaphor of a path through the woods. However, this metaphor highlights the role of collective effort, rather than the individual. A path results from the decisions of many individuals, united only by where they choose to walk, yet still reflects a rough notion of what the walkers find to be a good path. The path both reflects history of use and serves as a resource for future users. Social data mining approaches seek analogous situations in the computational world.Researchers look for situations where groups of people are producing computational records (such as documents, Usenet messages, or web sites and links) as part of their normal activity. Potentially useful information implicit in these records is identified, computational techniques to harvest and aggregate the information are invented, and visualization techniques to present the results are designed. Thus, computation discovers and makes explicit the "paths through the woods" created by particular user communities. And, unlike ratings-based collaborative filtering systems (Resnick et al., 1994), social data mining systems do not require users to engage in any new activity; rather, they seek to exploit user preference information implicit in records of existing activity. The "history-enriched digital objects" line of work (Hill et al., 1992) was a seminal effort in this approach. It began from the observation that objects in the real world accumulate wear over the history of their use, and that this wear -such as the path through the woods or the dog-eared pages in a paperback book or the smudges on...