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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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Samir 🎩
@0xsamir
Any specific number of casts to begin labeling and classifying needed? Like we need to classify manually like 10k casts is that good? Or you do it in different way?
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