Data

Came up with some metrics for Farcaster profiles. If anybody has experience with ML or similar tech, I would be curious to hear ideas on how to feed casts and profile metadata into an algorithm that identifies spam.

Came up with some metrics for Farcaster profiles. If anybody has experience with ML or similar tech, I would be curious to hear ideas on how to feed casts and profile metadata into an algorithm that identifies spam. https://dune.com/queries/3540715

headmaster @duneanalytics, previously mirror and consensys

A classic ML approach is releasing labeled data (users marked as spam or not) to a competition. You hold back some data to evaluate submissions 

Probably good candidate features for spam detection using NLP to parse text in names/bios/casts 

Maybe a good potential community challenge @ilemi?

building /royalpatron, data lover, jpeg collector. ex-data science at warby parker 

I’m also happy to chat and share more specifics @jacek — feel free to dc

data science @ coinbase                                


t0ny.base.eth

I was thinking the same thing...and if they held back a test set you could determine a winner pretty easily