In reverse order:
What you're describing goes beyond the scope of what a web of trust system does. You're describing an analytical engine that helps a human spot patterns.
Yes. My interest is in building systems as you likely know by now. A system can either learn or be adjusted based on the output it receives or its output parameters can indirectly adjust user behavior. This was done back when my post highlighter ran for the SSG community. Some users started formatting their posts to be picked up by the bot which looked for a few 'quality' measures.
And here's where the serious difficulty comes in: in order for any of the information being reported by these accounts to be taken seriously by a person's local rating system, no matter how many sock puppets the identity thief controls, they have to convince other people in your trusted network that these are all real people.
This just gave me an idea. One of the ID scammers that I alluded to here and mentioned privately was confirmed to be fraudulent by an individual geographically local to them. The individual was another long-time user from the same hometown who confirmed that its impossible for them to have the credentials they claim. Geographical coordinates of supporting or refuting parties, where they choose to provide them (such as in profile) or knowledge of a particular area, may be incorporated in the weighing algorithm.
Yes, when we start looking at using a web of trust to help analyze truth in various domains, one of the more "advanced" topics can be to look at how information in related areas can impact opinions about a specific truth (in the example you mention, proximity when verifying identity). To solve problems like this in a general way is a very challenging problem, but coding for the use of specific, logically related information to help rate other information is clearly possible.