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Varun Srinivasan
@v
We've been working on improving our spam detection. A big source of alpha has been taking algos used to rank content on the web and modifying them to work in Farcaster-space. @akshaan and @notawizard collaborated to add: - PageRank - Hyperlink Induced Topic Search - Louvain Clustering
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Varun Srinivasan
@v
A quick primer on spam handling in Warpcast: 1. Accounts are categorized roughly as "definitely not spammy", "probably not spammy", "unknown", "maybe a little spammy" and "definitely spammy". 2. Roughly 5% of the network is manually labelled by the team, and this seed data is used to train an ML model. 3. The model looks at a lot of signals and gives the user a score. For example, if you like things 24 hours a day, you're likely not a human. Multiple "bad" signals like this move accounts closer to the "definitely spammy" label. 4. The model has gotten quite good and rarely misses. In the cases where it does, we manually override it and retrain it on misses periodically so it gets better. 5. The model also tries to re-evaluate users periodically, so as users get more active and there is more data it can update its opinion.
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patxol 🔷 anser.social
@patxol.eth
Assuming non-human = spammy is going to be problematic when at the same time agentic bots are promoted
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Varun Srinivasan
@v
We never assume that non humans are spammy or that non humans are not spammy. Spam is a distinct quality from humanness.
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