I see where you included metrics as a starting point like how long an article takes to read. Maybe like a word count approach as a starting point.
Thinking about understanding the content of an article and seeing who's most likely to be referencing that content in their post is more of what I had in mind.
Using page rank as suggested by OP, would provide weird incentives like frequent comments, or higher volume, could rank a person higher than if they had occasional comments further down the thread. If OP is interested in the most influential commenters, people who write frequently and have a posse, then PR would be a good way to do it. What would happen to a helpful comment from a throwaway account?
If there's more of a ranking algorithm, identifying who is most likely to have actually read the article could be neat.
What happens on a slightly different task where domain experts have tried to create a set of topics, not all domain experts talk to each other, and so we instead need a way to merge existing topics? I continue to see benchmarks where human expertise significantly outperforms AI on common sense reasoning tasks (most recently https://arxiv.org/abs/2112.11446).
What about an approach using directed acyclic graphs and entities?
https://atd.readthedocs.io/en/latest/atdgen.html