  The growing problem of unsolicited bulk e-mail, also known as "spam", has
generated a need for reliable anti-spam e-mail filters. Filters of this type
have so far been based mostly on manually constructed keyword patterns. An
alternative approach has recently been proposed, whereby a Naive Bayesian
classifier is trained automatically to detect spam messages. We test this
approach on a large collection of personal e-mail messages, which we make
publicly available in "encrypted" form contributing towards standard
benchmarks. We introduce appropriate cost-sensitive measures, investigating at
the same time the effect of attribute-set size, training-corpus size,
lemmatization, and stop lists, issues that have not been explored in previous
experiments. Finally, the Naive Bayesian filter is compared, in terms of
performance, to a filter that uses keyword patterns, and which is part of a
widely used e-mail reader.
