I'm mulling over writing some code to find emails in a maildir that are duplicates, ish. That is to say that sometimes the same message doesn't quite show up as an exact match. Like some ad company may send you three identical messages, except they aren't actually EXACTLY identical, the message-IDs are different, and may the to address quoted part is different, so normal duplicate finders fail to find them.
Before I start, is this a solved problem?
-- I thought that they were angels, but to my surprise, we climbed aboard their starship, we headed for the skies.
On 03/02/2022 12:00 PM @lbutlr <kremels@kreme.com> wrote:
I'm mulling over writing some code to find emails in a maildir that are duplicates, ish. That is to say that sometimes the same message doesn't quite show up as an exact match. Like some ad company may send you three identical messages, except they aren't actually EXACTLY identical, the message-IDs are different, and may the to address quoted part is different, so normal duplicate finders fail to find them.
Before I start, is this a solved problem?
Besides the fact that you've pretty much described how modern AV/AS systems work? :)
Joking aside, isn't this what Bayesian classification is essentially doing? Comparing the similarities between text (via tokens) in messages and then using Bayesian probabilities to emphasize certain terms/relationships? Although this requires training and is not comparing any messages directly...
Maybe some form of perceptual hashing (or similar idea) would work? E.g. http://phash.org/
michael
On Wed, 2 Mar 2022, @lbutlr wrote:
I'm mulling over writing some code to find emails in a maildir that are duplicates, ish. That is to say that sometimes the same message doesn't quite show up as an exact match. Like some ad company may send you three identical messages, except they aren't actually EXACTLY identical, the message-IDs are different, and may the to address quoted part is different, so normal duplicate finders fail to find them.
Before I start, is this a solved problem?
Not perfectly, and maybe impossible in the general sense.
If you've ever had to anonymize mail by comparing samples sent by a mailing list provider to 2 different recipients, you can see various hashes and identifiers that show up in tracking headers and URLs. Adding customized name labels e.g. "Dear Alfred P. Sloan" or individual specific information, and this becomes a complex question how different is different.
If you make some simplifying assumptions (e.g. exact same message body, same header for From/Sending network or IP/time-range/Subject, you can do a fairly good job.
Joseph Tam <jtam.home@gmail.com>
participants (3)
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＠lbutlr
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Joseph Tam
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Michael Slusarz