Yeah, we are agree, but whatβs your limit, I know itβs complicated to explain
Discussion
Another thing is "sending in the bots". If someone doesn't like what you wrote, they start auto-generating npubs to crap all over your threads and make them unreadable, and it can be impossible to get away from it, even by removing relays.
Your npub can just get drowned in hate spam or kiddie porn, or even something benign like "Happy New Year".
Geez π€¦ββοΈ
Hey is this why you switched npubs?
I was being earnestly threatened by a crazy person and took a break, and then tried to start over. Didn't work. There are always more crazy people. Wish muting really worked.
Got hit with a botswarm on the new npub. I had to get off all public relays and unfollow a bunch of people who were following the bots, to get rid of it. No more public relays, for me. Full of npub viruses, basically.
Npub viruses, good label there. Make efforts to not catch them
How can we fix this?
Configurable web of trust
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:
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.
m'brain. Tan is interesting, can you explain for non-mathy people like myself?
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.
Coracle's WOT formula, where mutes are the argument and follows are the parameter.
proof-of-work, this is what NIP-13 is for. Filter out any npubs without a kind-0 rated at a difficulty of at least 20.