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Colin
@colin
I think this is a very interesting problem space. It’s similar to some work I was doing at Google: labeling & predicting abusive behavior for millions of SMS messages across 1b phone numbers a month. We would: - manually label millions of spam reports - create heuristic rules & train ML models using labeled data - deploy heuristics & models to the device and on the server (bc different access to signals on each) - rinse & repeat Heuristics got us surprisingly far. The vast amount of accessible onchain and FC data (metadata, behavior, content) should make it much easier to build decent classifiers.
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Dvyne🎩
@dummie.eth
Was it specific words used that you were able to highlight these SMS as spam Cause the algo seems to be weird when it comes to spamming on farcaster
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