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The Roomah Restaurant

checked Jul 2026
A5-G2, 01, Jln Dutamas 1, Solaris Dutamas, 50480 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia

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F43/100

manipulation detected

4.71★ claimed4.34★per established accounts · 1,247 reviews

24% of all reviews (304 of 1247) landed on 9 burst days. A surge this large reads as a review campaign even when most accounts look real, such as an in-person solicitation program or sophisticated review farm.

2 reviewers independently report the business rewards customers for reviews: "the high online ratings might be influenced by discounts offered in exchange for positive reviews" · "free ice cream for review"

14.3% of all reviews (178 of 1247) mention one of 3 recurring staff first names (Waqas, Faysol, Wakas). Those reviews are 93% five-star (4.92 stars vs 4.68 stars from everyone else). This matches staff asking happy customers for reviews on the spot; the reviews are real, but the crowd is hand-picked, so the headline rating reads high.

base score
every place starts at 100
100
rating inflation
headline 4.71★ is 0.37 above the 4.34★ that established accounts (30+ lifetime reviews) give
-22
coordinated review injection
108 reviews (8.66% of all) from low-history, non-Local-Guide accounts, across 12 statistically-impossible days. The worst, 2026-07-19, drew 23 reviews (about 1 in 1022), 14 of them injected; 14 of them predate the 24-month window and weigh a third
-18
review solicitation
2 reviewers independently report the business rewards customers for reviews, e.g. "the high online ratings might be influenced by discounts offered in exchange for positive reviews", "free ice cream for review"
-12
solicited review program
14.3% of reviews name one of 3 recurring staff members and run 4.92 stars, the signature of reviews requested on the spot from happy customers
-5
final trust score43 / 100
reviews analyzed1247
headline rating4.71★
credible reviewers257 · 4.46★
established accts (30+)111 · 4.36★
inflation gap+0.37
local guides (all)323 · 4.49★
single-review accts13%
flagged reviews108 (8.66% all · 8.73% recent)
worst spike23 · 1 in 1022
review bombingnone
burstiness3.36 (organic≈1)
velocity1.1/day on 44% of days
cross-posted reviews0 (0%)

how it works

Step 1: your agent collects the reviews

It opens the listing in its own browser, sorts by newest and collects them: all of them, or the 500 most recent at a big listing. We don't scrape Google.

Step 2: we run the stats

Burst days, throwaway accounts, cross-posted text, solicited reviews, rating drift. Statistics, not vibes.

Step 3: you get a grade

A to F, plus the rating credible reviewers actually give, with the evidence one click away. The place then appears here for everyone.

Google usually asks the browser to be signed in before it will sort or load more reviews. The agent reads Maps through your signed-in browser, much like scrolling the listing yourself. Heavy use could trip Google's rate limits; if you plan to check many places, a secondary Google account is a sensible choice.

No agent? Scripts can POST the same review JSON to /api/submissions; the format is at /api/sources/google-maps. Everything required is visible on the review card; review ids and exact timestamps are optional. Results from a single unconfirmed submitter are labeled as such until a second source agrees.

methodology

what signals do we look for?

We read a place's full review history and weigh the signals that are costly for a review farm to fake:

  • Burst campaigns: clusters of 5 star reviews dumped on a single day by low-history accounts. A genuine surge keeps a place's normal share of Local Guides, so it isn't counted against it.
  • Drip-feed farms: reviews arriving nearly every day at a pace no organic business sustains, a paid stream spread thin to dodge obvious spikes.
  • Cross-posted text: the same review copied word-for-word onto other businesses, a farm seeding a whole chain.
  • Throwaway accounts: single-review profiles padding the average up.
  • Review solicitation: reviewers themselves reporting that the business rewards, pressures, or steers reviews (“free dessert if you leave 5 stars”). An LLM reads every such mention; only firsthand reports of policy-violating practices count, and the penalty deepens with each independent witness. Politely asking for a review is allowed by Google and never counted.
  • Too-perfect records: a spotless, unanimous 5 star history often signals curated or incentivized reviews, so it can't earn a top grade, even when we can't prove fraud.
what's the “adjusted rating”?

What hard-to-fake reviewers actually give, vs the headline number. A gap means the rating is inflated. Two groups qualify:

  • Credible reviewers are Local Guides who also wrote a substantive review. The badge alone is gameable (farms run Local Guide accounts that drop bare 5 star ratings), so we only count ones that put real words behind the stars.
  • Established accounts have 30+ lifetime reviews, regardless of badge or text. Farms pad accounts with a handful of reviews to look plausible, but aging thousands of accounts past 30 is the expensive part.

Each place is anchored to whichever group gives the more conservative rating. When a place corrects the headline number, the “per credible reviewers” / “per established accounts” label tells you which.

where does the review data come from?

From the agents and scripts that submit it. Every submission is checked for structure and consistency (Google's own review and profile ids, timestamps against displayed dates, the rating average against the displayed rating, the count against the listed count, overlap with what we already hold), and a place submitted by one unconfirmed source carries an unverified source note until a second, independent submission agrees. Places we pulled ourselves before October 2026 need no such note.

what does a high score mean?

It means we found no evidence the business is manipulating or inflating its own reviews. It does not promise the rating is accurate: a clean place can still be mis-rated by too few reviews, reviewers who self-select, or honest reviews that just don't match your experience. We measure whether the number looks gamed, not whether it's right.