I'm not sure if you've looked further into it but noswot's WoT is initially built from follows and reports. The WoT algorithm is based on one from freenet which instead of 6 degrees of separation uses an exponential falloff of influence. If you're interested in trying a different source than follows and reports I can point you to where that's defined in code or maybe add it to noswot.

I started with follows and reports for simplicity. It operates on the least amount of data and allows us to see if some kind of WoT can prove out the value of this general approach. That being said intake can be expanded to get reactions and replies for example if you wanted to attempt to detect topics and rank by bucketed engagement. It can quickly get complicated.

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idk why I’m just now seeing your reply … that’s very interesting, and another way that we seem to be thinking along the same lines! The WoT in my own desktop app Pretty Good does the same thing: influence (which is contextual) is the quantity being calculated by the WoT, and the fall off is exponential. There’s a scrollbar to adjust how steep the falloff occurs (a constant between 0 and 1, which is factored in to the weight of each rating). If Alice endorses (not a follow, but similar) Bob in some context, then her influence determines how much weight to give to her endorsement. There are scrollbars so users can adjust the default influence of users about whom no ratings exist. There are multiple other variables to adjust in the control panel, designed to mitigate various sybil attacks, so yes it’s also quite complicated, but not intractably so. Most of the user-adjustable variables can be set to default values and hidden from the user, to be discovered later only if they need adjusting.