Depending on how a filter tests and scores a message, the filter might be able to determine the type of spam. By ordering and weighting the tests, you can place more or less importance on certain message characteristics. Early in the evaluation, tests might check for HTML formatting, whether the sender is on a blacklist, and whether any URLs match those in a known spam database. The cumulative score of these tests might be enough to classify the message as spam. Further tests (e.g., looking for vulgar words) could then classify the message as offensive spam.
No filter can completely eliminate false positives because some legitimate messages will have enough spamlike attributes to earn a spam classification. You can mitigate the risk of false positives by tuning the filter rules to account for your organization's message profiles. For example, a pharmaceutical company might need to configure tests so that the filter doesn't look at drug names or to ensure that drug names don't contribute significantly to the overall spam score. . . .
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Excellent Article.
Best regards
Sergio.
Sergio February 13, 2004