Free Fake Review
Checker
Paste any review and let AI spot the signals of fake, paid, or bot-written reviews. Get an authenticity score, a verdict for each review, and the exact red flags it found.
Paste the review(s)
One review, or several separated by a blank line
Tips for best results
- Paste the full review text, exactly as written
- Check several reviews together to reveal patterns
- Works with Google, Amazon, Yelp, Trustpilot, any platform
- Use the verdict as evidence to investigate, not final proof
Your authenticity report will appear here
Paste a review and click Check for Fake Reviews
Fake reviews are everywhere, and they cost real businesses real customers
Whether you are a shopper deciding where to spend, or a business owner being targeted by planted reviews, knowing which reviews to trust changes the decision.
30%
Of online reviews are estimated to be fake
Independent studies and consumer watchdogs estimate that up to a third of reviews across major platforms are not genuine. They are written by bots, paid writers, or the businesses themselves.
$152B
In yearly spend influenced by fake reviews
Fake reviews steer billions in consumer spending toward products and businesses that did not earn it, while honest businesses lose out on customers they should have won.
93%
Of shoppers say reviews affect their decisions
Reviews are one of the biggest trust signals in local and online buying. When they are fake, the whole decision is built on a lie. That is why catching them matters.
The 6 signals our AI looks for
No single flag proves a review is fake. But when several of these show up together, the odds shift sharply. That is what the checker measures.
Vague, generic praise
Fake reviews tend to be thin. Lots of enthusiasm but no real detail. No names, no dates, no description of what actually happened. Genuine reviews almost always include something specific.
Unnatural or AI-like language
Reviews written by bots, paid writers, or AI often read too smoothly, like marketing copy. Real customers write the way they speak, with small imperfections and natural phrasing.
Extreme rating, thin text
A 5-star or 1-star rating attached to a sentence or two of non-specific text is a classic flag. Strong opinions usually come with strong detail when they are real.
Repetitive or templated structure
When several reviews share the same sentence structure, opening, or phrasing, they were likely written by the same source. Authentic reviews vary widely in how they are written.
Keyword stuffing
Reviews that repeat the business name or service unnaturally are often planted to manipulate search rankings, not to describe a real experience.
Mismatched tone
A complaint framed as 5 stars, or glowing praise rated 1 star, signals something is off. Often it is a review left on the wrong listing, or one generated without much care.
4 ways people use the fake review checker
Business owners
Competitors and bad actors plant fake negative reviews to drag down your rating. Run them through the checker to build a case before you report them to Google or the platform.
Shoppers & consumers
Before you trust a 5-star average, paste a handful of the reviews here. If most of them look planted, that rating is not telling you the truth about the product or service.
Agencies & marketers
Auditing a client's profile? Screen their incoming reviews for authenticity so you are not optimising around a rating that has been inflated or attacked.
Researchers & journalists
Investigating review manipulation on a platform or brand? Use the per-review breakdown to spot coordinated patterns across a batch of suspicious reviews.
Understanding your authenticity report
Authenticity score (0–100)
70+ means the reviews read as genuine. 40–69 is a grey zone worth a closer look. Below 40 means strong fake signals are present across what you pasted.
Per-review verdict
Each review gets its own verdict and a fake-probability percentage, so a single planted review in a batch of genuine ones does not skew your read of the rest.
Red flags
The specific signals found, like vague language, templated structure, or mismatched tone. These are the concrete points you can cite if you report a review.
Probability, not proof
Only the platform can confirm a fake with account and purchase data. Treat a high fake-probability as strong evidence to investigate, never as a final ruling.
Patterns across a batch
Checking several reviews at once is the most powerful use. Repeated phrasing or structure across reviews is one of the clearest signs of coordinated fakery.
What to do next
The advice line tells you the most useful next move, whether that is reporting a review, drowning out fakes with genuine ones, or trusting the rating because it holds up.
What to do after you check a review
| If the result shows… | Do this first | Who |
|---|---|---|
| Likely fake negative review on your profile | Report it to the platform with the specific flags as evidence. Do not respond emotionally in public. | |
| Likely fake positive reviews on a competitor | Document them, but put your energy into collecting genuine reviews of your own. That is what wins over time. | |
| Most reviews on a product look planted | Discount the star rating heavily. Look for detailed, balanced reviews and weigh those instead. | |
| Suspicious but not conclusive | Check a few more reviews from the same source. Patterns across a batch are far more telling than one review. | |
| Reviews read as genuine | Trust the rating. Use the strengths in genuine reviews to inform your buying or marketing decision. |
beat fake reviews with real ones.
The best defence against fake reviews is a steady stream of genuine ones. Bragly collects real reviews from your real customers automatically, so your rating reflects the truth.
Frequently Asked Questions
Paste one or more reviews and our AI checks each one for the signals that platforms and researchers use to flag inauthentic reviews. That includes vague or generic language, unnatural fluency, extreme ratings with thin text, repetitive structure, keyword stuffing, and mismatched tone. You get back an authenticity score, a verdict for each review, the specific red flags it found, and advice on what to do next.