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The problem with false reviews

Convincing yourself to visit a new place whether it’d be a restaurant, hotel, shopping mall, etc. can potentially be a battle. This is now solved with the use of Yelp, Google, Foursquare, and other review hosting websites. This article, “Yelp, Google and Urbanspoon targets for fake reviews” discusses and says that almost 15% of all reviews on websites are deemed as fake. Fake meaning posted by either employees or friends of owners that do not reflect actual experiences at these establishments or for particular products or people who are of competition that post negative reviews. About 19 companies have been charged of up to $350,000.00 in fines regarding false reviews and advertisement.

A professor at Cornell, Jeff Hancock, recently said, “Some data now show that a good majority of people in North America believe and trust online reviews more than they trust their friends’ opinions.” This shows that if someone has not been to a particular place or a has purchased a particular product, their opinions will solely depend on the review read. This said, these false reviews affect the type of information cascade that is being produced.

If the false reviews are negative, then a negative information cascade will initially form. Depending on whether or not customers who like the product/restaurant writes a positive review or not, then the cascade could remain or change. If enough people write positive reviews, indicating numerous people are passionate about the goodness of the product, the falsely written reviews will ultimately not result in an incorrect cascade. But if no one feels too great about it and not enough positive remarks are publicized, then an incorrect cascade will form due to the negative reviews. The chance of an incorrect cascade will be high cause less people will be tempted to test the product after negative reviews are publicized.

The vice versa will occur if someone posts a positive review, that in the end falsely promotes the product/venue. The biggest difference between the two types of reviews is that positive ones will try to get people to try these products/places but negative ones will just steer them away. This shows that it is easier to prove positive reviews will encourage participation/willingness to try but negative reviews will be harder to prove in general. The article, “Researchers developing algorithms to detect fake reviews,” show that there are ways to formulate how to detect reviews: through burst of posts, distortion, recent accounts, etc.

In the end, those people connected to a product/venue about their product/experience may increase  revenue initially. However, unless they meet the expectations given by these false reviews, the invalidity of the positive reviews will be revealed soon and the public will soon know the truthful value of the hotel.


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November 2014