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Scientists have developed an artificial intelligence (AI) system that can identify machine-generated fake reviews on online e-commerce websites.
Sites such as TripAdvisor, Yelp and Amazon display user reviews of products and services. Nine out of ten users read these peer reviews and trust what they see, researchers said.
However, not all reviews are legitimate. Fake reviews written by real people are common on sites, but the number of fake reviews generated by machines is likely to increase substantially. According to doctoral student Mika Juuti at Aalto University in the US, 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. “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,” said Juuti.
“The motivation is, of course, money: online reviews are a big business for travel destinations, hotels, service providers and consumer products,” he said.
Using neural network
In 2017, researchers from the University of Chicago in the US described a method for training a machine learning model, a deep neural network, using a dataset of three million real restaurant ratings on Yelp.
But there was a slight hiccup in the method, it had a hard time staying on 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.
To help the review generator stay on the mark, Juuti and his team 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.
“Up to 60 per cent of the fake reviews were mistakenly thought to be real,” he said.
Researchers then devised a classifier that will be able to spot the fakes.
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