Honestly, whoever has an idea for a spam detection measure for Mastodon, and by that I do mean an implementation, get in touch with me, I'll pay for it.

I've been thinking about solutions for the past few days but the more I think about them the more they appear pointless.


Defining an account as suspicious when it has no local followers can be circumvented by just pre-following them, using account age can be circumvented with sleeper accounts, blacklisting URLs does nothing when the spam does not include URLs, checking for duplicate messages sent to different recipients can be circumvented by randomizing parts of the message...


E-mail deals with spam using Bayesian filters or machine learning. The more training data there is, the more accurate the results, a monolith like GMail benefits from this greatly. Mastodon's decentralization means everyone has separate training data, and starts from scratch, which means high inaccuracy. It also means someone spamming a username could potentially lead to any mention of that username be considered spam due to the low overall volume of data, unless you strip usernames



@Gargron Dealing with spam is hard. I worked at an email anti-spam company for 10 years. Bayesian was one method, but was troublesome and needed some hand-holding; we often cleared the bayesian data because it started to false-positive a lot. Most of our effective spam blocking came from: greylisting, DNS blacklists, a spam rules database that we added to based on the spam we saw, and users reporting messages (which we had tooling to analyse to then create more rules).

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