3. Love the idea of using algorithms for moderation/mediation. Seems to be wildly successful on reddit at its aim (of deprioritizing controversial), and very successful on HN.
As as engineer at heart, one thing that would help me get more enthusiastic about this is showing 30 or so example controversial or disputed claims (e.g. who won the election, whatever) and how this algorithm would score various "provided contexts" about them. Of course as a biased individual I can't expect 100% agreement, but it would be nice to see if any wild or surprising results come out of this technique.
Author here. Thank you. And good idea, it would definitely improve the article to pull out actually examples. I could show examples of righ-wing+helpful, left-wing+helpful, right-wing+not-helpful, neutral+helpful, etc. I have looked at many examples myself and they are really quite interesting and not too surprising.
1. Very interesting concept
2. Good explanation
3. Love the idea of using algorithms for moderation/mediation. Seems to be wildly successful on reddit at its aim (of deprioritizing controversial), and very successful on HN.
As as engineer at heart, one thing that would help me get more enthusiastic about this is showing 30 or so example controversial or disputed claims (e.g. who won the election, whatever) and how this algorithm would score various "provided contexts" about them. Of course as a biased individual I can't expect 100% agreement, but it would be nice to see if any wild or surprising results come out of this technique.