  It has recently been argued that a Naive Bayesian classifier can be used to
filter unsolicited bulk e-mail ("spam"). We conduct a thorough evaluation of
this proposal on a corpus that we make publicly available, contributing towards
standard benchmarks. At the same time we investigate the effect of
attribute-set size, training-corpus size, lemmatization, and stop-lists on the
filter's performance, issues that had not been previously explored. After
introducing appropriate cost-sensitive evaluation measures, we reach the
conclusion that additional safety nets are needed for the Naive Bayesian
anti-spam filter to be viable in practice.
