What Share of Places Have Manipulated Reviews? Our Data, July 2026

As of July 2026, 33.5% of the 242 Google Maps places we've analyzed show at least one fake-review manipulation signal, and 12.4% score high enough to be labeled Likely Manipulated. That's based on 241,041 individual reviews run through our scoring process, of which 49,047 — 20.3% — were flagged for at least one suspicious pattern.

This is the first data release from our own catalog, and we'll update it periodically as the dataset grows. The numbers below are pulled directly from completed analyses, not survey estimates.

#How many analyzed places show manipulation signals?

Of 242 places with a completed analysis, the verdict split is:

Two-thirds of analyzed places come back Clean — most businesses' review histories look organic. But a third show at least a Suspicious level of signal clustering, and roughly one in eight cross the higher Likely Manipulated threshold. The average score across every analyzed place is 32.1 out of 100.

#What does the score distribution look like?

The score histogram isn't a smooth curve — it clusters. The largest single band is 10-19 (120 of 242 places, roughly half the dataset), consistent with most review histories carrying a light, ordinary background rate of low-activity reviewers and the occasional cluster that doesn't add up to a pattern. There's a second, smaller cluster in the 60-79 range (54 places combined) that separates cleanly from the bulk of the data — the group our verdict system calls Likely Manipulated.

That bimodal shape — a large low-score cluster and a distinct high-score cluster, with relatively few places in between — is what you'd expect if review manipulation is something a minority of businesses actively do rather than a background level every business drifts toward gradually. For more on what feeds each score, see how our fake-review score works.

#Which manipulation signal is most common?

Across every flagged review in our dataset, low-activity reviewer accounts are by far the most common pattern:

Low-activity accounts — reviewer profiles with only one or two lifetime reviews — outnumber the other two signals combined. That tracks with how reviews-for-hire operations actually work: varying the wording of a review is cheap, but building or maintaining reviewer accounts with a long, believable history is not, so the accounts themselves are the weakest link and the easiest signal to detect at scale. For the full walkthrough of what each signal looks like and how to check for it yourself, see how to spot fake Google reviews.

#What's driving review manipulation right now?

The data lands against a backdrop of active federal enforcement. The FTC's Consumer Review Rule — which bans buying or selling fake reviews and testimonials, including AI-generated ones, and suppressing genuine negative reviews — took effect in October 2024 and allows civil penalties of $51,744 per violation.

On December 22, 2025, the Federal Trade Commission took its first step in enforcing its August 2024 Consumer Review Rule by issuing warning letters to 10 unidentified companies alerting them of their potential violations of the Rule, and cautioning that continued noncompliance could lead to enforcement action and substantial civil penalties.

That first wave of warning letters, 14 months after the rule took effect, suggests enforcement is shifting from establishing the rule to acting on it — a signal worth watching for small businesses weighing whether a review-buying vendor's pitch is worth the risk.

#Which categories are represented in the dataset?

The current dataset skews toward food and hospitality, reflecting where analyses have been requested so far: restaurants are the largest single category (28 places), followed by hamburger restaurants (15), American restaurants (14), and pizza restaurants (10), with hotels, bakeries, and bars also represented. As the catalog grows across more categories and geographies, we'll revisit whether the manipulation rate holds steady or varies by business type — early signal only, not a categorical conclusion yet.

#Methodology note

Every figure above comes from our own completed analyses as of July 2026 — public Google review text, ratings, timestamps, and reviewer profile metadata, scored with the four-signal model described in our post on how the score works. Verdicts are computed from each place's most recent completed analysis, so a business analyzed more than once is counted once, using its latest result. These numbers will shift as the dataset grows; we'll date every future release the same way so the trend is trackable over time. This dataset currently covers Google Maps listings only — see how Google, Yelp, and Tripadvisor compare in their approach to catching fake reviews for the wider picture.

#Frequently asked questions

#Is one-third of all Google Maps listings manipulated?

No — this is 33.5% of the places in our specific analyzed catalog, which currently skews toward food and hospitality in the US. It shouldn't be read as a universal rate across all of Google Maps without a representative sample.

#Has the manipulation rate changed over time?

This is our first published dataset release, so there's no prior period to compare against yet. We'll track this figure over successive updates going forward.

#What counts as "Likely Manipulated" versus "Suspicious"?

Likely Manipulated is a combined score of 60 or higher out of 100; Suspicious is 30-59. Both reflect the same four weighted signals — bursts, low-activity accounts, duplicate text, rating-text mismatch — at different strengths.

#Why are low-activity accounts so much more common than duplicate text as a signal?

Because reusing accounts or standing up new ones with thin histories is structurally necessary for review-selling at scale, while varying review wording to avoid duplicate-text detection is comparatively cheap and easy to do.

You can check any specific Google Maps listing against this same scoring model with our free fake-review analyzer — the dataset behind these numbers grows with every analysis submitted.