i think it’s possible to combine WoT with (normalized) zaps, to prioritize (thus filter) data: https://github.com/baumbit/peercuration?tab=readme-ov-file#how-to-integrate-zap-into-peercuration

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Zaps can definitely serve as a trust signal and contribute towards WoT calculations. Many other trust signals: reactions, replies, labels, badges, reports, mutes … there’s no limit to the number of trust signals we might want to use. This is why one of the most important steps in GrapeRank is something we call interpretation: you pick your signal, write a script to convert the signal into a format ready to be ingested by the GrapeRank algo, and then use it in contextual GrapeRank score calculations.

For now, for Brainstorm (a personal WoT relay under development), I’m interpreting follows, mutes, and reports into a baseline GrapeRank score, the purpose of which is to filter out bots and spam. At some point I’m thinking I may interpret zaps, reactions, and replies and use them to create one or more “engagement” GrapeRank scores that can be used in a variety of ways. Eventually you and your community will have the tools to pick your own signals, create new interpretation scripts, and create new GrapeRank scores. All of these scores will be made readily available to any nostr client that wants to use them.

sounds excellent! in my prototype (treebit) I made the decision to also use WoT score to render threads in a novel new way. the way it’s normally done is to render the root and then the replies below the root. i made a slight change to this, so that the rooT was rendered first, then the highest scored replyA to the rooT and then the highest scored reply to that replyA (instead of next replyB to rooT). thus when scrolling vertically through the notes tree, the path taken through the tree would be the one where the highest WoT scored path. this way really high value notes deep down would still get a lot of attention and thus get a wider spread in the network.