Dan Romero
@dwr.eth
The spam filtering dilemma Sharing how we think about it so people can better understand: 1. Most people will only use social networks if they are fun 2. Casting something and getting dozens of spammy replies is not fun 3. If you get enough spammy replies, you'll just stop using the app 4. So, aggressively filtering for spam is necessary to keep your existing users happy 5. However, this means some good new users will hit spam filters. Understatement: this is a bad user experience for those people. It would be great to not have this happen. 6. But given the choice of who to make unhappy — an existing user who has invested a ton of time and energy into the network or a new user that is most likely to churn (top tier social networks lose 50% of users!), then you have to optimize for your existing users. 7. So does that mean you don’t care about new users? No! It’s existential to have new users be able to join Farcaster and find people to connect with — without being labeled as spam. 1/2
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Dan Romero
@dwr.eth
8. So here’s what we are doing to improve this so new good users aren’t mislabeled as spam: a. Continually refine the spam model by identifying incorrectly labeled people (that feeds back into the model and makes it better); good existing users on the network reporting incorrect labels is really valuable here b. Warnings to users before they are labeled spam so they have a chance to adjust their behavior c. Increase the number of signals our model uses to help filter out bots vs. people d. Working a new version of channels that give owners / moderators complete control of who appears in their channel (so a Warpcast spam label won’t affect casts in the channel) 2/2
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Apurv
@apurvkaushal
a & c were expected, b is something very interesting. Might be an additional data point for spammers to fine tune their messaging strategy basis this feedback. Guess one of those cases where false positive is more costly than false negative? (positive = labelled as spam )
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Apurv
@apurvkaushal
Also has the spam algo already shown robust results to meaningless gen AI replies?
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Dan Romero
@dwr.eth
B. Yes, that's why we've avoided doing it for a while.
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