Online retail giant Amazon is flooded with fake five star reviews for products for unfamiliar brands, consumer group Which? has found.
The inauthentic reviews mean that household names are largely absent from the top rated items in categories such as headphones, smart watches and fitness trackers.
Thousands of the reviews were found to be unverified, which means there is no evidence that the reviewer actually bought the product. Amazon said it was using automated technology to weed out and remove fake reviews.
A common problem
The Which? research suggested fake reviews were a common problem.
When it searched for headphones, it found all the products on the first page of results were from unknown brands. These are defined as ones Which? experts have never heard of, while known brands, are defined as household names.
Of 12,000 reviews for these unknown headphones, 87 per cent were from unverified purchases.
One example, a set of headphones by an unknown brand called Celebrat, had 439 reviews. These were all five-star, unverified and were posted on the same day, suggesting they had been automated.
How to spot a fake review and what to do
The consumer group has identified a few key ways to ensure that you do not fall foul of fake five star reviews. Here they are:
- Many of the fake reviews simply give ratings rather than any kind of written feedback. Don’t just rely on the ratings. Delve deeper and read the reviews
- It is important to check the dates of the posted reviews. Fake reviews are often posted in a short space of time. If this is the case, they are likely to have been computer generated
- All of the fake reviews will be unverified. You can filter out unverified reviews on the Amazon website
- Any products with very large numbers of reviews – sometimes in the hundreds or thousands – should be treated with caution
A statement from Amazon said: “Amazon invests significant resources to protect the integrity of reviews in our store because we know customers value the insights and experiences shared by fellow shoppers. Even one inauthentic review is one too many.
“We use a combination of teams of investigators and automated technology to prevent and detect inauthentic reviews at scale, and to take action against the bad actors behind the abuse.
“We estimate more than 90% of inauthentic reviews are computer generated, and we use machine learning technology to analyse all incoming and existing reviews 24/7 and block or remove inauthentic reviews. “
This article originally appeared on our sister site, Yorkshire Evening Post