Faizan Raza

I love HN authors, publishing, and talking incessantly about AI, Tech,Startup,Blockchain & etc.

How AI Helps in Distinguishing Fake Reviews from Genuine Ones

January 28th 2019
Photo by Owen Beard on Unsplash

Approximately 97% of the consumers read online reviews before making a buying decision in 2017. This is the power of the reviews! If a business has 4 or 4.5 stars out of 5, 63% of the consumers will trust it and make the purchase. According to the research conducted by Chatmeter in 2017, it is believed by most of the experts that even Google and other search engines base their rankings on reviews.

How much do you rely on online reviews depends on your age. It is unfortunate that as much as millennials trust online reviews, they have encountered the need to differentiate the fake from genuine ones.

Efforts are being made to spot the fake reviews and a number of organizations and universities are looking for solutions through AI. A study was conducted by Aalto University in collaboration with Japanese researchers from Waseda University that proved that AI detects fake reviews better than the human eye.

The Trend of Faking Success

Even 77% of the patients rely on reviews to choose a new doctor and as many as 47% of them would visit a doctor out of context if he has favorable reviews. It is not necessary that all of these reviews are authentic or generated from reliable sources like 24Option Review. Hence, it is essential to have a system which has the ability to treat all the text with the same level of scrutiny.

The overly ambitious or misbehaving companies try to enhance their sales by boasting a positive brand image artificially. They can even generate fake negative reviews for a competitor to undermine their brand value in the market. The motivation is definitely money and revenue but the damage is directed towards both the competing company and the target audience.

How AI Detecting Fake Reviews

Through powerful AI, one can develop such a system which employs the language processing method to detect unusual patterns of text, writing style, and formatting. There is an internal scoring system to rate the reviews and any suspicious similarity can raise red flags and make the team pursue its authenticity. The fake reviews algorithm need time and training before they start working flawlessly. It is the certain level of sophistication in autonomously generated reviews that helps in differentiating them.

The Aalto team had already worked on the generation of fake reviews and were successful in convincing the audience which couldn’t detect 60% of them. Now, they are dedicated to counter the problem of fake reviews by introducing such machine learning system which can detect them. In 2017, a few researchers belonging to the University of Chicago also came up with a machine learning system, which was a deep neural network, and relied on the dataset of three million real restaurant reviews on Yelp.

The Bottom Line

Even the consumers can detect fake reviews with the help of minute details like the frequent use of ‘I’ and ‘me’, overuse of a particular phrase and review timings but this is not enough. 90% of the audience knows about fake reviews but only 61% is truly concerned.

Nonetheless, a number of companies and organizations are working towards the development of such AI which will help in detecting the fraudulent reviews and help the consumers in making the right decisions.

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