About that Junk Folder

I use a pretty standard mail filtering setup – a fairly vanilla SpamAssassin setup on the front end, combined with naive bayesian content filters in my mail client. So I don’t reject any mail, it just ends up in one of my inboxes or a junk folder. And I have a mix of normal consumer mail – facebook, twitter, lots of commercial newsletters, mail from friends and colleagues and spam. (As well as that I have a lot of high traffic industry mailing lists, but overall it’s a fairly normal mix.)
My bayesian filter gets trained mostly by me hitting “this is spam” when spam makes it to my inbox. If I’m expecting an email “immediately” – something like a mailing list COI confirmation or email as part of buying something online – I’ll check my spam filter and move the mail to my inbox in the rare case it ended up there. Other than that I let it and spamassassin chug along with no tweaking.
I’m starting a data analysis project, based on my own inboxes, and as part of that I’m using some tools to look for false positives in my junk folders, and manually fixing anything that’s misclassified. I’ve been doing this for a couple of hours now, and I’ve found some interesting things.

1. Simple content filters work remarkably well out of the box, at least for my mail stream. Spectacularly well. There’s very little in the way of false positives. Very, very little.
2. Of those false positives there’s nothing I’d have been bothered about. It’s generic, unexciting junk mail.
3. Most of the systemic false positives seem to be correlated with the senders doing something bad. Heathrow Express, for instance, sent me mail every two weeks or so since I’d signed up. Then for no obvious reason they stopped sending for three months, then started sending again. Every mail they sent after that pause ended up in the junk folder, and I never missed them.
4. I get regular newsletters from ThinkGeek. Every one of those goes to the inbox. I occasionally get mails from them about my account (“you’ve got 420 geek points left”) that are kinda transactional, but not something I expect to see – and they all end up in the junk folder. Several other senders do the same thing, and get the same result.
5. Several companies have used tagged addresses to send me newsletters for a while, and also used them to send unsolicited facebook invites (to the tagged address, from facebook servers). The regular newsletters all go to the inbox, while the facebook invites all go to the junk folder. “Legitimate” facebook mail, meanwhile, keeps going to the inbox.
6. Apple send me a lot of newsletters – I’m a Mac and iPhone developer, I get their consumer newsletters, transactional stuff from our local store – lots and lots of newsletters. They all made it to the inbox except for one. The one that ended up in the junk folder was a one-off about recycling, and it wasn’t up to their usual design standards – it had ugly big green “call to action” headlines in it, very different to their usual clean design.
7. Just one sender hit the junk folder every time. The distinctive thing about their messages (apart from them not being something I missed) was that the plain text part of them was dreadful, just a bad lynx dump of the html section. Even a “No plain text for you! Go to this link!” would have been better.

I was surprised at how effective this simple content-based filtering setup had worked with little tuning other than hitting the this-is-spam button – both in it’s accuracy at removing spam while keeping a very low false positive rate, but also how well the false positives matched my judgement of “Meh. This mail isn’t interesting.”.
We spend a lot of time talking about the things you should do to make the mail relevant to the recipient – compelling content, consistent, predictable delivery schedules, clear consistent branding, use of a single consistent mail stream to communicate with a recipient rather than several different streams. Until I went through this exercise it wasn’t clear to me how much of an effect those things also have on fairly simple recipient-trained filters.