TREC 2007 Spam Track Overview

TREC’s Spam Track uses a standard testing framework that presents a set of chronologically ordered email messages a spam filter for classification. In the filtering task, the messages are presented one at at time to the filter, which yields a binary judgment (spam or ham [i.e. non-spam]) which is compared to a humanadjudicated gold standard. The filter also yields a spamminess score, intended to reflect the likelihood that the classified message is spam, which is the subject of post-hoc ROC (Receiver Operating Characteristic) analysis. Four different forms of user feedback are modeled: with immediate feedback the gold standard for each message is communicated to the filter immediately following classification; with delayed feedback the gold standard is communicated to the filter sometime later (or potentially never), so as to model a user reading email from time to time and perhaps not diligently reporting the filter’s errors; with partial feedback the gold standard for only a subset of email recipients is transmitted to the filter, so as to model the case of some users never reporting filter errors; with active on-line learning (suggested by D. Sculley from Tufts University [11]) the filter is allowed to request immediate feedback for a certain quota of messages which is considerably smaller than the total number. Two test corpora – email messages plus gold standard judgments – were used to evaluate subject filters. One public corpus (trec07p) was distributed to participants, who ran their filters on the corpora using a track-supplied toolkit implementing the framework and the four kinds of feedback. One private corpus (MrX 3) was not distributed to participants; rather, participants submitted filter implementations that were run, using the toolkit, on the private data. Twelve groups participated in the track, each submitting up to four filters for evaluation in each of the four feedback modes (immediate; delayed; partial; active).