Question 5: What (in broad terms) would a content filter spec look like, where anybody (client, relay, DVM, or individual) could “publish” a filter for end users to “subscribe” to? Such filters could take ANY event data on nostr (including “is trusted”) to produce “recommendations” for content, accounts, and other stuff. IMHO, this is where rubber meets road for WoT on Nostr, and is often overlooked in light of implementing “is trusted” trust attestations. 

Answer: The Grapevine is my answer to this question. It is a method to filter content based on explicit, context-based trust attestations. 

In a nutshell: the core function of the Grapevine is to calculate WEIGHTED AVERAGES over things like product ratings (just to use one example), where the weights are proportional to the Influence Score of the author of the rating, in whatever context that you consider to be most relevant. Example: If I am at a nostr e-commerce site, and I want to rank all widgets from top to bottom based on average rating, then the Grapevine calculates the average score for each widget in the same way that Amazon does, EXCEPT that not all ratings are treated equally. If my Grapevine tells me Alice is an expert in widgets, her ratings weight more heavily than a rating by Bob, who is not to be trusted when it comes to widgets, according to my Grapevine. How much weight to give to someone about whom my Grapevine knows nothing? Set a default Influence Score for that.

Weighted averages instead of just averages. Simple but effective.

I have a proof of concept to demonstrate how it works: the app: Curated Lists, which is a part of my desktop nostr client, Pretty Good Apps. It is open source and anyone is welcome to download it and give it a whirl (below). But my app is clunky and crashes and not the best UX, which is why @kinjo  is working to refactor it as a web app. In the meantime, I have screenshots to show how it works (link below).

https://github.com/wds4/pretty-good

https://github.com/wds4/pretty-good/blob/main/appDescriptions/curatedLists/overview.md