As online shopping has increases forcefully, there is one more thing that growing rapidly along with it –“fake review”. Most of the people read peer reviews and trust what they see without knowing that not all of them are legitimate.
Around 85% of consumers trust online reviews, positive reviews make 73% of consumers trust a local business, but this thing is also true that 79% of consumers have read a fake review and 84% can’t always spot them.
To combat this, an artificial intelligence (AI) system has been developed by the scientists that can identify machine-generated fake reviews on online e-commerce websites.
The AI system is developed by Juuti and team. The study was presented at the 2018 European Symposium on Research in Computer Security in September.
“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 his team used a technique called neural machine translation to help the system stay on the mark. It gives 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.
“In the user study we conducted, we showed participants real reviews written by humans and fake machine-generated reviews and asked them to identify the fakes,” said Juuti.
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. For a review of a Japanese restaurant in Las Vegas, the model could make references to an Italian restaurant in Baltimore. These kinds of errors are easily spotted by readers.
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, said Mika Juuti, a doctoral student at the varsity.
“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,” Juuti said.
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