Recommend not a total sliding scale, what is 47 vs 46?? Make it seven notches (choices), which is a known format for surveys (Liekert scale)
Starting to play with decentralized trust ranking in Spring v0.12.
You can estimate, adjust and publish trust scores for other users - these are estimated from your recent interactions.
https://void.cat/d/T5HriPK2C8QSd7cGsoJVL6.webp
nostr:npub1wmr34t36fy03m8hvgl96zl3znndyzyaqhwmwdtshwmtkg03fetaqhjg240 has been advocating the TrustNet as a web of trust implementation, useful for spam filtering etc.
The algorithm has two steps - first, each user publishes 'trust assignments' - that's trust scores your can now publish with Spring. These are published as 10629 replaceable events with a list of 'p' tags and a score, typical size will probably be ~100 pubkeys. We provide an estimate based on past interactions, but it can't be precise - you may and should adjust it to match your actual relationships.
The second step is that apps can download trust assignments of users close to your network (contacts, people you like/zap a lot etc) and run a calculation akin to PageRank, but it's not global - it's local to your network. The result will be several thousand pubkeys with non-zero trust ranks - a much wider network of users who could be trusted.
This way the trust ranking is a) based on everyone's actual relationships, because you can adjust the trust scores you're publishing, and b) efficient and can be used by any app - it just needs to download several hundred trust score lists and run the trustnet algo periodically and store results in local cache.
Spring only does step one at the moment. When enough people publish their trust assignments we will add the second step and let you calculate your own trust ranks. Spring will show the trust ranks under profiles, and will use it for spam filtering later. Other apps will probably find other uses for it.
More on TrustNet here: https://cblgh.org/trustnet/
Discussion
You can read the trustnet paper about the trust assignment ranges. I will look into Liekert scale
I like the 5 stars rating - well know system online and maps to the 1-5 likert scale.
No need for something like that. The Trustnet paper already provides human-meaningful labels.
Brugeman, any plans to implement those labels to make ranking easier?
Sure - looks like a 5 point scale
Trust weight (t) Semantic Human-meaningful label
----------------------------------------------------------
t == 0 no trust New person
0 < t < 0.25 low trust Acquaintance
0.25 >= t < 0.75 medium trust Friend
0.75 >= t < 1.0 high trust Peer
t == 1 complete trust Partner
nostr:npub1xdtducdnjerex88gkg2qk2atsdlqsyxqaag4h05jmcpyspqt30wscmntxy check this out for social weight. I've already implemented it
https://github.com/ArcadeLabsInc/arcade/wiki/ArcadeSocial#social-graph-reputation
https://github.com/ArcadeLabsInc/arclib/blob/main/src/social.ts
It's there the Friend labels on the screenshot. Also on the 5-star ratings nostr:naddr1qq25gnzpveay5jns29z9xdrkdgehw5mvv46k6q3qarkn0xxxll4llgy9qxkrncn3vc4l69s0dz8ef3zadykcwe7ax3dqxpqqqp65wuucp8v
I see, but is it a drop-down menu i can choose from? Or does it just update the label when I slide the number? Could use more visual reflection in the bar too.
Seeing those better reflected would help me a lot. Maybe others too
Those update as you move the slider. Maybe #nostrdesign team has any ideas?
Which labels were you referring to?
There are labels to the right of the profile name - Friend/Peer/Partner/Acquaintance/New person, it changes if you're dragging the slider (on the screenshot above). People questioned whether it's intuitive... I'd say never mind, the issue is too minor without the actual use or feedback atm.
I’m thinking about actual use fwiw. I remember nostr:npub1g53mukxnjkcmr94fhryzkqutdz2ukq4ks0gvy5af25rgmwsl4ngq43drvk had this in Iris version 0.1 and it was overkill. So he switched to just “friend or not friend” and when you follow someone it just assigns the friend for you.
Maybe there is a happy middle ground though. Can I just select what they are without thinking about numbers? Remember MySpace “Top Friends”? Analysis paralysis haha
Remember these trust score lists are public. I am pretty sure everyone has at least one special person that will really appreciate that one extra point you can assing that takes them to the top.