liminal 🦠 on Nostr: Quite curious at the reasoning or background for the formula vs others. Little ...

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 😅

quoting nevent1q…jzu6

Actually curious about the reasoning for it 😀

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 ±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.