Online reviews can make or break your decision to try out a new hotel, check out a restaurant or even influence your choice of medical care. Unfortunately, companies desperate to be perceived as awesome sometimes try to sneak in their own positive reviews, posing as fellow consumers. Good for us, then, as science has come up with yet another way to help sniff out the fake reviews.

Researchers from the State University of New York, Stony Brook are using statistical methods to detect if a company has been posting bogus reviews online, says Technology Review. The method can’t root out individual fraudulent reviews, but it can see where fake reviews are distorting the statistical distribution of say, a hotel’s scores. Basically, the method can tell you when something’s fishy.

Here’s how it works, in a nutshell: Review scores for products are plotted on a graph, and in most cases, would form a shape that looks sort of like a “J.” Usually on a scale of one to five, a product or service will have a pretty high amount of one-star reviews, followed by a smattering of twos, threes and fours and then a bunch of five-star ratings, say the researchers.

This is because people have a tendency to buy things they like, and then in turn, rate those things highly because they already like them. Usually those of us who are just satisfied but not blown away or horribly disappointed don’t feel the inclination to post a review at all, thus, the smaller amount of twos, threes and fours.

Fake reviews mess up this whole “J” shape idea, say researchers. They compared ratings of reviewers they believed to be reliable (having written at least 10 reviews more than a day or two apart) to single-time reviewers, to see if those single-timers gave out a weirdly high number of five-star reviews.

Researchers labeled hotels with large discrepancies between those two sets of reviewers as more suspicious.

Using earlier work with an algorithm that spotted textual clues to find fakes, researchers had a computer measure the effect that the known fake reviews had on the shape of the distribution and compared them, finding fraudulent activity 72% of the time.

One of the researchers says fake reviewers “might think that it was a perfect crime, but the truth is, they distorted the shape of the review scores of their own hotels, and that leaves a footprint of the deceptive activity, and the more they do it, the stronger it becomes.”

This isn’t the first time researchers have tackled the bogus online review problem: In April we looked at another study that was able to identify groups of spam reviewers better than individual fake reviews, by how often the posted reviews with the five stars. Thanks for staying on the case, scientists!

Statistics Unmask Phony Online Reviews [Technology Review]

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