liminal @liminal - 6mo
And we can experiment with WOT Score built from the your followers+broader network, weight stronger if score comes from in network - count mutes - count follows? - count/classify comments?? - count/classify reactions??? Minimum threshold for interaction π/πΈ if < lower bound π if close to zero (less interactions, less data to work with) π if > upper bound
Take the raw count, divide by total number of interactions, throw it into a tan function, scale appropriately and you'll have a WOT that pretty much does that nostr:npub1jlrs53pkdfjnts29kveljul2sm0actt6n8dxrrzqcersttvcuv3qdjynqn could you give some intuition behind your WOT metric? (follows - ln(mutes)^2)
Npub viruses, good label there. Make efforts to not catch them
Think WOT is the most straightforward path. Cant stop people from viewing, following, commenting on your public account but you can filter them from your view and put bad actors into a sinkhole where they need a new npub to build trust. Parameters for wot score: nostr:nevent1qqst0r4ky5565r6t8wk9eqgxt3slken8af5z5nlmh6xtx7cckwad85qpz3mhxue69uhhyetvv9ujuerpd46hxtnfdupzphzv6zrv6l89kxpj4h60m5fpz2ycsrfv0c54hjcwdpxqrt8wwlqxqvzqqqqqqyt8lwu5 Multiply by a scaling factor and throw into tan function. At least thats my initial run at it. coracle uses followers- log(mutes)^2 but im not sure where that formula comes from.
You mean there should already exist a formula that is standardized or that it should be formally defined for nostr as a nip?
Actually curious about the reasoning for it π We care about some sort of threshold classification, with some sort of "pending"/leniancy state for newcomers. Maybe even a sinkhole point-of-no-return-make-a-new-npub-bitch for spam/noted toxic individuals given your network. A sigmoid is standard practice for classification, but asymptotic bounds of 0 and 1 don't really help those that have been in the game for a while. So we want to classify yes and no, in between state, and also note the very (un)trustworthy individuals. Tan and cubic curves accomplish that. They grow very fast after a certian point. With minimal assumptions, you can just encode every data point as Β±1 for positive/negative interactions, and put the average * scaling constant into the function. Or you can weight the interactions by type (mutes>follows> sentiment classification of comments > reaction classification, or weight based on if the points are coming from your follows) and compute the weighted average. https://i.nostr.build/LeV86.jpg https://i.nostr.build/4oG23.jpg https://i.nostr.build/VwVnj.jpg Coracle's WOT formula, where mutes are the argument and follows are the parameter.
Feel like i didnt understand math till Calc 3 π€£π€£π π°ππ even more so when i took Dynamic Systems
Brainstorming WOT nostr:nevent1qqs82gc4rzml32fxkvwpv3d58fa6cqzmh5w7q22tc8tg9xgu7lyzlzspz3mhxue69uhhyetvv9ujuerpd46hxtnfdupzphzv6zrv6l89kxpj4h60m5fpz2ycsrfv0c54hjcwdpxqrt8wwlqxqvzqqqqqqyu499uh
derGigi β‘𧑠@derGigi β‘𧑠- 6mo
#WoT #WebOfTrust nostr:nevent1qqs82gc4rzml32fxkvwpv3d58fa6cqzmh5w7q22tc8tg9xgu7lyzlzspp4mhxue69uhkummn9ekx7mqzyrwye5yxe47wtvvr9t05lhgjzy5f3qxjcl3ft09su6zvqxkwua7qvqcyqqqqqqg9ag6rm
liminal @liminal - 4mo
Quite curious at the reasoning or background for the formula vs others. Little brainstorm about using some formulas in this thread. What i'd like out of a WOT is 1) newcomers get leniency, so small number of interactions/ small score is okay 2) below a certian level i dont care, above a certian level i dont care either Think the cubic seems to accomplish that. Forget the mention of tan in this thread though π nostr:nevent1qqs27cam2wcnc8gvr4alja47fm8gf8xvcx3gn4lczj5hhcg9f8f900gpzemhxue69uhhyetvv9ujumn0wd68ytnzv9hxgq3qm3xdppkd0njmrqe2ma8a6ys39zvgp5k8u22mev8xsnqp4nh80srqxpqqqqqqzd2jzu6