Free fake review checker

Paste a review that does not look right and get a transparent signal, with the reasons stated.

The patterns worth looking at

No single signal proves anything. Fake reviews are identified by clusters, and the honest version of this tool tells you which signals fired rather than handing you a verdict to wave at Google.

  • Rating and text disagree. A five-star rating attached to a lukewarm sentence, or a one-star attached to praise, usually means the rating was the point and the text was an afterthought.
  • No text at all. Weak on its own, meaningful in a burst. Genuine unhappy customers often leave a bare rating too.
  • Generic language. Reviews that could describe any business in any city, with no detail a real visit would produce.
  • Timing clusters. Several similar reviews arriving within days, particularly right after a dispute, a bad shift or a competitor opening nearby.
  • Cross-location footprints. The same reviewer appearing at several of your locations in places they are unlikely to have visited.

What to do about one

Report it through your Google Business Profile with the specific policy it breaks, and be precise: spam, conflict of interest, off-topic, or prohibited content. Then reply publicly anyway. State briefly that you have no record of the visit and invite direct contact. Removal may never come, but the reply is permanent and everyone reads it.

What matters more than any single review is the flow around it. A business collecting reviews steadily absorbs a bad one without flinching. A business with eleven reviews feels every single one, which is the real argument for asking every customer, every time.

Across every location, continuously

This page checks one review you paste in. Inside GBP Manager, the same credibility engine reads every review across every location you manage and flags the ones worth a second look as Trusted, Neutral or Suspicious, with the reasons attached. See review intelligence for how the signals are built.

These signals are indicative heuristics. They are not evidence, they do not prove intent, and they are never a reason to dismiss criticism that happens to be inconvenient.

Fake review questions

You cannot tell with certainty, and anybody claiming otherwise is overselling. What you can do is look for patterns: no text or generic text, a rating that contradicts what the review says, an account with no history, a burst of similar reviews in a short window, or a reviewer who has hit several of your locations.

Only Google can remove a review, and only if it violates their policies - spam, conflict of interest, off-topic content, or prohibited content. You can report it through your profile. A clear, specific report with evidence does better than a general complaint.

Reply publicly, calmly and briefly. Say you have no record of the visit, invite the person to contact you directly, and stop there. The reply is read by every future customer, and a measured one does more good than the removal you may never get.

No. Plenty of genuine customers leave a rating and nothing else, especially when they are annoyed. A textless one-star is a weak signal on its own and only becomes meaningful alongside others - several arriving together, from new accounts, right after a dispute.

No, and it is built not to. It returns a score and the specific reasons behind it so a person can decide whether to look closer. Treating a heuristic as proof leads businesses to dismiss genuine criticism, which is worse for the business than the occasional fake review.

Watch every review, at every location

Credibility signals with the reasons stated, across your whole estate. 90-day free trial.

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