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

In this paper, we describe the use of similarity metrics in a novel visual environment for storing and retrieving favorite web pages. The similarity metrics, called Implicit Queries, are used to automatically highlight stored web pages that are related to the currently selected web page. Two experiments explored how users manage their personal web information space with and without the Implicit Query highlighting and later retrieve their stored web pages. When storing and organizing web pages, users with Implicit Query highlighting generated slightly more categories. Implicit Queries also led to faster web page retrieval time, although the results were not statistically significant.