Earlier quoted context omitted.
That's a good question. I'm not sure what the other poster had in mind, and I suspect it's not the same, but RT has a bunch of rating problems that would probably become far more egregious for news than they are for movies. (Leaving aside the tricky question of how to translate the formats: is a movie like a news story, or a news source? Does news really have an existing aggregation-worthy source like movie reviews?)…
> BeerAdvocate, of all things, takes on this problem with interesting meta-info like "reviewer's average distance from consensus". There's a lot to be said for intelligently dealing with ratings and their metadata in ways like this. For example, the Kappa statistic: https://en.wikipedia.org/wiki/Inter-rater_reliability Presenting crowdsourced ratings as mere averages -- or really any collapse onto a single scalar --…
Histograms are a screamingly obvious way of distinguishing "mediocre" from "some good some bad", which is one of the most common needs with things like Amazon products. But beyond that, there's so much more to be done. You can weight or shift scores by reviewer's average, reviewer's average distance from consensus, or a dozen other things. A one-star review from someone who uses Yelp exclusively to call out bad experiences is relevant, but a one-star review from someone who often gives 4-5 is far more interesting.
Maybe the weirdest thing is that a lot of this is done to catch fake/paid reviewers, but it's not extended to providing clearer info overall. Even the fight against fake reviews would be much achievable if it was shifted from a binary "take down or don't" to a more flexible approach to maximizing review value.