According to researchers from the Aalto University in Finland, nine out of ten people read these peer reviews and trust what they see.
In fact, up to 40 per cent of users decide to make a purchase based on only a couple of reviews, and great reviews make people spend 30 per cent more on their purchases.
Fake reviews based on algorithms are nowadays easy, accurate and fast to generate. Most of the time, people are unable to tell the difference between genuine and machine-generated fake reviews, Mika Juuti, doctoral student at the varsity, said in a statement.
"Misbehaving companies can either try to boost their sales by creating a positive brand image artificially or by generating fake negative reviews about a competitor.
"The motivation is, of course, money: online reviews are a big business for travel destinations, hotels, service providers and consumer products," Juuti said.
Juuti and her team based her work on a machine learning model, developed by researchers from the University of Chicago in 2017. The model faced a hard time staying on one topic.
They used a technique called neural machine translation to give the model a sense of context. Using a text sequence of "review rating, restaurant name, city, state, and food tags", they started to obtain believable results.
The team then devised a classifier that would be able to spot the fakes. The classifier turned out to perform well, particularly in cases where human evaluators had the most difficulties in telling whether a review is real or not.
(This story has not been edited by Social News XYZ staff and is auto-generated from a syndicated feed.)
