Fake Amazon reviews are everywhere—here’s how to spot them

Like most people with hair, we're aware of the Dyson Supersonic hair dryer, which is famously expensive. We went looking for a cheaper alternative and found one that looked promising. It wasn't a brand we recognized, but the reviews were good—excellent, even. As we read further, though, we started to notice something off about them. Review after review used almost the exact same phrasing to describe this hair dryer. Of course, that doesn't guarantee they're fake, but how do you know for sure?

Not all the reviews on Amazon are genuine—that's well documented. Spotting the fakes, though, can be tricky. For a long time, shoppers could turn to tools like Fakespot and ReviewMeta, but both are gone. So, how do you tell the difference between a fake review and a real one that just copied the homework of every other reviewer before it? There's a lot to consider.

Why fake reviews never go away

Amazon's marketplace runs on third-party sellers competing for the same few slots at the top of a search page, and a higher star rating pushes a product up. This pressure gives sellers a direct financial incentive to buy positive reviews or pay for negative ones that bury a competitor. Amazon's own community guidelines have banned paid and incentivized reviews since 2016, so none of this happens in the open. Most of it happens off Amazon, in private Facebook groups and encrypted messaging apps where brokers recruit ordinary shoppers, so the company's filters can't see the deal being struck. Amazon has said it blocked more than 200 million suspected fake reviews in a single year and sued thousands of broker group administrators—and the company admits its own AI tools can't solve the problem alone.

One government-commissioned study in the UK put a number to the trend. Researchers estimated that 11% to 15% of reviews in categories like consumer electronics, home and kitchen, and sports and outdoors were fake, and that fake review text alone caused between £50 million and £312 million in consumer harm each year.

What a fake review looks like in 2026

The crude fakes are still the easiest to catch. Watch for a cluster of five-star reviews posted within a day or two of each other, especially right after a product launches. Look for repeated phrasing across different reviewers, generic praise that never names a specific feature, and a run of reviews that all read at the same polished level.

A reviewer's history tells you more than any single review does. Tap a reviewer's name and check whether they've rated dozens of unrelated products in a short window, or only ever posted five stars.

A Verified Purchase badge feels reassuring, but it's weaker than it looks. In a practice called brushing, a seller ships a product to a paid reviewer so the purchase registers as genuine, and the fake review inherits the badge. The way a person reviews over time catches more than the badge ever will.

The harder-to-spot fakes come from modern AI tools. LLMs now produce grammatically clean, specific-sounding reviews at scale, from accounts with a normal-looking purchase history. The same UK study also found that shoppers were more likely to buy a product with well-written fake reviews. The effect only grew for higher-priced items, where shoppers rely more heavily on reviews.

What tools can still identify fake reviews?

Fakespot, once the largest consumer tool, closed in July 2025 after Mozilla decided it couldn't sustain it. ReviewMeta, known for recalculating a product's star rating after stripping out suspicious reviews, went dark in early 2026 without a farewell post. The Review Index still runs but has moved toward a paid tier and a narrower focus on tech products, so it's a weaker free check than it used to be.

A crop of newer analyzers has appeared to fill the gap, including browser tools that assign a trust score or a plain buy-or-avoid verdict.

One popular option is FakeFind, which uses AI to analyze reviews and offer an authenticity score from 1 to 10. As with all review checkers, the score is a signal, not a guarantee. It gave our suspect hair dryer a 9 out of 10, which doesn't mean it's wrong. It could just be better at recognizing that a bunch of reviewers took the path of least resistance when describing a simple product.

Another new entrant, ReviewAI, takes a product link and returns a plain buy, avoid, or caution verdict in about 10 seconds, along with a trust score and the manipulation signals it flagged. It also needs a deep pool of reviews to work with. Our sample hair dryer had 1,503 reviews at the time of writing, and that was apparently still "insufficient data" for ReviewAI.

Amazon's AI assistant, Rufus, will summarize a product's reviews if you ask, and it can surface common complaints buried deep in the list. Remember who employs it before you lean on it, though. Rufus exists to help Amazon sell, so it's the wrong tool to ask whether a listing's reviews can be trusted.

Things you can do to spot fake reviews

There's no single solution to fake reviews, but there are a lot of little things you can do to keep from getting tricked:

• Be skeptical in categories where fakes cluster: cheap electronics, supplements, and obvious knockoffs
• Check reviewer profiles
• Check posting dates for clusters of reviews close together
• Study reviewer photos for AI artifacts
• Focus on the three- and four-star reviews for specific, checkable complaints only an owner would think to make

None of these checks takes more than a few minutes, but they make a big difference if you're ever unsure of a review's authenticity.