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LazyWeb: Incoming mail with > n "To:" recipients?
Merlin Mann | Jun 5 2006
Related to "Thanks. No." and email filtering, I wonder how hard it would be for Mail.app, etc. to have a rule by which messages with more than n recipients in the "To:" line could be flagged for (depending on your preferences and courage) filtering, auto-archiving, or deletion. Maybe via AppleScript? I've heard from several friends who filter all non-work email for which they aren't the exclusive "To:" recipient, but it would be handy to have some flexibility in what your own magic number is -- plus of course what you'd then do with emails that exceed your limit would be up to you. But in an edge case, for example, if I get an email that went to [>=90 TO: recipients] and [<=25%] of the recipients were in my Address Book, the message would be flagged as "possible friend spam." (And, yes, I was once on a "Hey, this is funny" list that went to 96 people multiple times each day. Good times.) So, any thoughts? Bonus points if it's a rule that's easy for non-geeks to recreate in GUI apps like Mail.app, Entourage, and Outlook, etc. Comments open for brainstorming. (In related news, as I mentioned on MM.com, I'll soon be opening a thread on the Board to take suggestions on improving Thanks. No., so keep your powder dry on that one.) 16 Comments
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 Well, I would suggest using...Submitted by AlexK (not verified) on June 5, 2006 - 10:52pm.
Well, I would suggest using POPfile (http://popfile.sourceforge.net/) which is a bayesian filter. The nice thing abut POPfile is (1) that it is independent of your mail client, because it is a mail proxy, (2) it works for as many different topics as you like, meaning it is not a spam filter, but a general filter that filters everything you want. I have it set up to automatically filter my mail into work (and here into two different companies I run), personal, sparetime (newsletters, etc.) and spam. But I am sure it could recognize mails that are important for you (no matter what the number of TO: people is). It of course needs a bit of training, like all automatic filter solutions, but the error rate get low very quickly. On my home machine I reached a 90% success rate after two days, which is not too bad. » POSTED IN:
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