If a post gets downvoted, it could be a geinuenly awful post. But another post that gets downvoted, but is actually empiracially scientifically true, then it is treated equivolent as the other even though they are the same.
I don’t think this is the answer but one idea is to add points to people, or products, who are verified to be awesome. So that would be a scientist or compassionate politician gets more votes or a healthy product gets a subsidy.
With lemmy votes being public, one could do clustering of accounts on voting behaviour, and display upvote/downvote count per cohort.
ok and the outcome would be what
It’s a methodology for achieving what you describe, without manually having to assign social scores to each person.
can you define this? this seems like almost a semantic nightmare in practice. Like tagging all the the topics they up/downvote
That’s what happens: every post one up or downvotes is public. You can just download that data.
Uh huh… like web scraping? There seems to be some confusion-making
You don’t need to scrape. The votes made on one instance need to be propagated to the others. So the information “person X up/downvoted post Y” is openly transmitted as part of the defederation protocol.
The short answers are a bit infuriating for me. I am tempted to ask more questions, but this is becoming an unfun game for me. I guess one could see the votes, but the categorizing the types of topics per user and/or cluster of users sounds like a difficult, combinatorically complex job.
Ah ok, now I see the confusion. I thought you were enquiring how to gather the data.
There’s well known clustering algorithms for that. It’s a long time known and solved problem: https://en.wikipedia.org/wiki/Cluster_analysis
These techniques have the benefit that you don’t have to inject your own biases into the score assignment, you can just let the data speak for itself.