mleku @mleku - 24d
it's an interesting idea, like nostr:npub1l5sga6xg72phsz5422ykujprejwud075ggrr3z2hwyrfgr7eylqstegx9z has been talking about with improving upon web of trust to count engagement data as well, and then you can add on top full text indexes and machine learning text graph analysis and then also stuff like noticing idle follows, or follows that a user never engages with nostr:npub176p7sup477k5738qhxx0hk2n0cty2k5je5uvalzvkvwmw4tltmeqw7vgup has been doing some work in this direction also
Laeserin @Laeserin - 24d
I liked the idea of essentially running a spam filter backward. Instead of marking things 💩, you 🌟 things, and it adjusts your filter. Kind of like nostr:npub1utx00neqgqln72j22kej3ux7803c2k986henvvha4thuwfkper4s7r50e8 algo, but with the addition of examples, to the set of rules.
so it could use a bayesian filter as well, although you'd have to be able to tune its sensitivity
