The main differences between an Influence Score and WoT scores are that Influence Scores also keep track of context and degree of confidence in the score. If explicit attestations like you describe don’t exist, then we use whatever information is available. If follows and mutes and likes are the best we have, then for each one we infer a score, context, and confidence and feed that into the Influence Score calculation.

Example: if Alice “likes” an article by Bob in wikifreedia on Category X, we interpret that as a trust attestation with score: 100, context: Category X, confidence: 5%. 

I have some posts from a few hours ago saying this in more detail. 

 The main advantages of Influence Scores are that they’re contextual and they allow us to stop incentivizing people for being “influencers.” So you can find the needle in the haystack, that person who is many hops away from you and has only a few followers but is brilliant in some niche category you’re interested in.

The main disadvantage of Influence Scores is they are computationally burdensome, but there are ways to address that problem.

https://habla.news/a/naddr1qqxnzdes8q6rwv3hxs6rjvpeqgs98k45ww24g26dl8yatvefx3qrkaglp2yzu6dm3hv2vcxl822lqtgrqsqqqa28kn8wur 

 I used the Influence Score method rather than WoT scores in a desktop app to curate lists, as described below.

What I failed to appreciate when I built that desktop app was that the low quality but abundant proxy data (likes, follows, etc) can be merged with high quality but scarce contextual trust attestations via the method of “interpretation” I just described. Pooling them together is how we overcoming the bootstrapping problem.

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