As Yelp celebrates its 20th anniversary, the world of online reviews continues to baffle and confuse shoppers. This article delves into the intricacies of review platforms, exploring how linguistic analysis and research can help us discern authentic from fabricated reviews. From the prevalence of fake reviews to the paradoxical impacts of review similarity, this piece offers insights that empower consumers to make more informed purchasing decisions.
Yelp and the Challenge of Online Reviews
Over the last two decades, Yelp has transformed into a household name as the go-to outlet for users to share their experiences and insights ranging from local bars to barbershops. The platform has released 287 million user reviews on 600,000-plus businesses in India till date, the company said. Review sites such as Yelp are appealing to consumers since no one wants to waste money on a disappointing product or service.
But the ability of sharing reviews with ease Also has a flip side to it a growing problem i.e., Fake Reviews. Through scientific research, however, it has been found that competition leads some businesses to pay consumers so that they may write these reviews themselves in either a positive light for their own products or a negative one against their rivals. Also, bots are able to create fake reviews that are indistinguishable from the real ones by a human being. In fact, this has gotten so bad that a new study estimates that it causes consumers to literally flush 12 cents of every dollar they spend online down the toilet.
How to Spot Real Reviews with Verified Linguistic Clues and Yelp’s Algorithms
The authenticity of a review is one that I have come across in my work as a linguist who researches word of mouth and online reviews, as part of collaborative research with other academics in this area. Our results indicate that authentic reviews follow down to earth description using concrete vocabulary or other words bring only the ‘wat- where when’ of the experience. However, some reviews are based on abstract generalities alluding to a product or service — without the reviewer having used it themselves.
Yelp has its algorithms to pick up and remove ‘unhelpful’ reviews, as well as ones that are very short. Those measures do go some way to mitigating the fake review phenomenon, but studies suggest that consumers are still unable to consistently recognise fake reviews better than 50% of the time.
The irony about review similarity and volume
One of the most intriguing paradoxes that my research has revealed is related to this contradiction. What I noticed is the more alike those reviews are, the more certain the reader became, even if the subjectivity of each writer less and less corresponds to his or her experience. On the other hand, differing reviews may cause reader hesitation, although the authors are even more sure of their own experience.
Also, the volume of reviews on a platform can impact how individual reviews are viewed by consumers. The higher the count on the reviews, such as 1,572 on a product, it gives credence that each review is better believed compared to being one of an only 72 reviews. As an experiment in the phenomenon known as the “halo effect”, consumers intuit that more is better, however the reality is larger number of reviews does not always equal higher levels of quality.
