Apologies if this has been suggested before. I'm curious if something like the BCS ranking system in college football could work for online communities. The sp

Apologies if this has been suggested before. I'm curious if something like the BCS ranking system in college football could work for online communities. The specifics don't translate, but the general idea is that you use a weighted combination of human and machine generated rankings. This can be seen as maintaining the user-driven voting system but tempered with an impartial community spirit moderator in the form of a machine learning algorithm. How could this be applied to HN? Let's leave the standard karma/voting system as it exists, as that seems to generally work. Next, determine the general distribution of votes per comment. This will allow for things like z-scores to be determined that can notice if a particular comment has received significantly more votes than usual. Next, perform a machine learning algorithm on a corpus of comments. Something as straightforward as Bayesian filters can work, though self-organizing maps also have potential. This is effectively doing the same thing as spam filtering, but instead of simply flagging something as spam, it would provide its own +/- vote. The initial training would start with a baseline of existing comments, and then periodically, say once a night, be updated with recently added comments and votes. Additional information, such as the karma of the commenter, can also be incorporated. The final ranking then, which here would be how high up on the page it appears, would be a weighted combination of user votes, z-score scaling, and machine votes.