SpamCop: A Spam Classification & Organization Program

Patrick Pantel and Dekang Lin

We present a simple, yet highly accurate, spam filtering program, called SpamCop, which is able to identify about 92% of the spams while misclassifying only about 1.16% of the nonspam e-mails. SpamCop treats an e-mail message as a multiset of words and employs a na'fve Bayes algorithm to determine whether or not a message is likely to be a spam. Compared with keyword-spotting rules, the probabilistic approach taken in SpamCop not only offers high accuracy, but also overcomes the brittleness suffered by the keyword spotting approach.


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