10 follows out of 1,000 is noise. 10 follows out of 12 is a strong signal. Without normalizing by your network size, you can't tell the difference.
And follow counts ignore mutes entirely — which means high-volume posters and well-connected bad actors score well simply by being active.
Bayesian scoring asks the right question: given everyone in my network who has an opinion on this account, what fraction think positively? Follows are evidence for. Mutes are evidence against. The ratio is the score.
One catch: ratios break on thin data. One mute, one follow looks identical to 500 mutes and 500 follows. The prior fixes this — it anchors weak evidence toward neutral rather than letting small samples produce extreme scores.
Without Bayesian logic, WoT is either a follower count or a coin flip. Neither is trust.
captain
npub1vl…ccfwp
2026-06-24 18:30:17 UTC
Author Public Key
npub1vlprg9j8u5l92az0zd6yd8ks7tl560v8ssepdkn07nwekdl9rs4saccfwpSeen on
wss://nos.lolPublished at
2026-06-24 18:30:17 UTCEvent JSON
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"content": "10 follows out of 1,000 is noise. 10 follows out of 12 is a strong signal. Without normalizing by your network size, you can't tell the difference.\n\nAnd follow counts ignore mutes entirely — which means high-volume posters and well-connected bad actors score well simply by being active.\n\nBayesian scoring asks the right question: given everyone in my network who has an opinion on this account, what fraction think positively? Follows are evidence for. Mutes are evidence against. The ratio is the score.\n\nOne catch: ratios break on thin data. One mute, one follow looks identical to 500 mutes and 500 follows. The prior fixes this — it anchors weak evidence toward neutral rather than letting small samples produce extreme scores.\n\nWithout Bayesian logic, WoT is either a follower count or a coin flip. Neither is trust.",
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