> why are those scores low? What gives them negative score? > those rules have quite high score...
Here is an example (without my rules): http://pastebin.com/m4400a74d The ones that get through are relatively short and simple, and many are very "clean". This example is just one that focuses on weight loss, some are regarding tea or satellite companies or coffee makers or the like. I worry about increasing FPs of real e-mails by training of "clean" spams as spam, when they are short and sweet and many times look like they could be legitimate e-mails. Also would training bayes on this sort of e-mail help if many things are different between each e-mail, and if the e-mail is so short and relatively "clean"? Addresses change, company names change, sender domains are always different, etc I've been thinking about maybe writing an SA plugin that counts the three repeated URL patterns that are always present in all of these spams, but I don't know where to start in trying to do that. I was hoping I could just handle this with SA rules or something (like using another RBL or something). Thank you! -- View this message in context: http://www.nabble.com/please-help%2C-getting-hammered-with-snowshoe-spam-tp21627042p21627664.html Sent from the SpamAssassin - Users mailing list archive at Nabble.com.
