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I think John S is on the right track.

Think of a "critically acclaimed" movie: a highly visible set of reviewers say it's great, but the vast majority think it's overrated. Or think of the restaurant that's just starting out in a nondescript strip mall: those who bother to stop in love it, but the vast majority of people simply have a bad impression when they drive by based on the wrong things (location) rather than what a restaurant would generally be rated on (food, service, atmosphere).

So you could look for restaurants that have a fanatical following, but flags of poor location, etc, that would cause many people to unfairly downrate it. Or look for restaurants which are highly rated by highly visible/influential people who are not representative of the general customer base.

If you're talking under/over-rated to a particular subgroup and you're a member of that subgroup speaking to that subgroup -- say a blogger -- you'd simply find places that were highly rated by your subgroup, but not by the general population. The consensus of being underrated is not a general consensus, but would be with "everybody" you know. Like the Yogism, "No one goes there anymore, it's too crowded."

Jonathan Baron

Perhaps Prelec's "Bayesian truth serum" would be useful here. It gives greater weight to opinions that are "surprisingly common" given the predictions of others' opinions. I'm sure it would show something, but I'm not sure that this would correspond to being "underrated".

Nick F.

If we define underrated as "not rated (often) enough" as opposed to "not rated highly enough." I think we get at a truer statistical definition. This metric might look something like a discrepency between average yelp user rating, and the amount of people who have reviewed it, after we adjust for both neighborhood traffic and restaurant capacity.


Thanks for all your comments. I have summarized them in a new post here with some further thoughts.

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