# Google Review Fraud & Fake Review Detector (`inexhaustible_glass/google-review-fraud-detector`) Actor

- **URL**: https://apify.com/inexhaustible_glass/google-review-fraud-detector.md
- **Developed by:** [Hitman studio](https://apify.com/inexhaustible_glass) (community)
- **Stats:** 3 total users, 2 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $4.00 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## 🕵️ Google Review Fraud & Fake Review Detector

Scores every Google Maps review on a business for fake/paid-review risk —
not by guessing from the text, but from the one thing a fake review can't
fake: the **reviewer's own account history**, which Google renders on the
page for every single review.

> Keywords: fake review detector, google review checker, review fraud
> detection, local guide review analysis, google maps review scraper, fake
> reviews, paid reviews, review bombing detection, local business reputation.

### 🌟 Why this one

- **Reads the signal Google itself exposes, not sentiment** — every review
  on Google Maps shows the reviewer's full history right next to it, e.g.
  *"Local Guide · 82 reviews · 12 photos"* or just *"2 reviews"* for a
  brand-new account — confirmed by direct inspection of real review blocks.
  A brand-new account's first-ever review being an extreme 1★ or 5★ with
  no real text is the single strongest, most reproducible fake-review
  signal there is — far more reliable than trying to judge review text.
- **Transparent scoring, not a black box** — every suspicion score comes
  with the exact reasons behind it (`thin_account`,
  `extreme_rating_from_thin_account`, `extreme_rating_with_little_or_no_text`,
  `reviewer_has_never_reviewed_anything_else`). Nothing is hidden behind an
  unexplained "fraud probability."
- **Catches review bursts** — flags clusters of 3+ reviews all posted in
  the same relative time window (a classic sign of a bought review batch).
- **No login, no API key, no paid review-monitoring subscription.**

### ⚙️ How it works

1. Opens the business on Google Maps and switches to its Reviews tab.
2. Scrolls Google's own lazy-loaded review list to pull as many recent
   reviews as requested.
3. For each review, extracts the reviewer's name, Local Guide status,
   total lifetime review/photo count, star rating, relative post date,
   full review text, and a link to the reviewer's own profile.
4. Scores each review with a transparent rule set weighted toward
   reviewer account history, and rolls everything up into a per-business
   fraud-risk summary.

### 🚀 Quick start

```json
{ "businessQueries": ["Dominos Pizza Times Square New York"], "maxReviewsPerBusiness": 30 }
```

Or via the API, once published:

```bash
curl "https://api.apify.com/v2/acts/google-review-fraud-detector/run-sync-get-dataset-items?token=<YOUR_TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{"businessQueries": ["Starbucks Times Square New York"], "maxReviewsPerBusiness": 25}'
```

### 📥 Input

| Field | Type | Default | Description |
|---|---|---|---|
| `businessQueries` | array | `["Dominos Pizza Times Square New York"]` | Business name + city, as you'd type it into Google Maps search — one per line |
| `maxReviewsPerBusiness` | integer | `30` | How many recent reviews to fetch and score per business (5–150) |

### 📤 Output

Two record types in the same dataset, one per review plus one per business:

**`record_type: "review"`**

| Field | Description |
|---|---|
| `reviewer_name`, `reviewer_profile_url` | Who posted it, and a link to their own reviewer profile |
| `reviewer_is_local_guide`, `reviewer_total_reviews`, `reviewer_total_photos` | The reviewer's own account history — the core fraud signal |
| `rating`, `relative_date`, `review_text` | The review itself |
| `suspicion_score` | 0–100, higher = more suspicious |
| `suspicion_flags` | The exact, named reasons behind the score — never unexplained |

**`record_type: "business_summary"`**

| Field | Description |
|---|---|
| `reviews_analyzed`, `average_rating` | What was analyzed |
| `suspicious_review_count`, `suspicious_review_pct` | How many reviews scored ≥40 |
| `thin_account_review_count` | Reviews from accounts with ≤3 lifetime reviews, not a Local Guide |
| `burst_periods` | Relative-date windows where 3+ reviews landed at once |
| `fraud_risk_level` | `low` / `medium` / `high`, based on the suspicious-review ratio |

```json
{
  "record_type": "review",
  "place_name": "Domino's Pizza",
  "reviewer_name": "Lydia Lugo",
  "reviewer_is_local_guide": false,
  "reviewer_total_reviews": 2,
  "rating": 1,
  "relative_date": "5 months ago",
  "review_text": "Worst service ever",
  "suspicion_score": 70,
  "suspicion_flags": ["thin_account (only 2 review(s) ever, not a Local Guide)", "extreme_rating_from_thin_account"]
}
```

A business that can't be matched or fails outright produces one
`record_type: "error"` row with a real error message — never a silent gap.

### 🎯 Use cases

- **Reputation management** — spot a wave of suspicious reviews (real or
  competitor-driven) as soon as it starts
- **Due diligence** — sanity-check a business's rating before trusting it
  (franchise buyers, local SEO agencies, review-monitoring services)
- **Competitor monitoring** — check whether a rival's glowing reviews hold
  up to account-history scrutiny

### 🔌 Integration examples

**Python (apify-client):**

```python
from apify_client import ApifyClient
client = ApifyClient("<YOUR_TOKEN>")
run = client.actor("google-review-fraud-detector").call(
    run_input={"businessQueries": ["Dominos Pizza Times Square New York"]}
)
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    if item["record_type"] == "business_summary":
        print(item["place_name"], item["fraud_risk_level"])
```

**JavaScript (apify-client):**

```javascript
import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: '<YOUR_TOKEN>' });
const run = await client.actor('google-review-fraud-detector').call({
    businessQueries: ['Starbucks Times Square New York'],
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
```

### 📊 Performance & reliability

| Metric | Value |
|---|---|
| Proxy required | No — confirmed by direct testing with a free, open stealth browser |
| CAPTCHA-solving service required | No |
| Login / API key required | No |
| Data source | Google Maps' own rendered review panel — the same data any visitor sees |
| Challenge handling | Up to 4 automatic retries per business, each with a fresh browser session; if a chain search's top result lands on a location with no usable tab strip, the next few results are tried before retrying; a sustained failure surfaces as one clear error row, never a silent gap |
| Cost per business (real cloud runs) | ~$0.015–0.035/business for 20 reviews, depending on retries needed |

### ❓ FAQ

**Is this a certainty that a review is fake?**
No — it's a transparent, rule-based risk signal from the reviewer's own
account history, not proof. Always spot-check flagged reviews (every
output row includes a direct link to the reviewer's profile) before
taking any action against a business or reviewer.

**Does this need a Google account or API key?**
No — it reads the same public review panel any visitor sees on
google.com/maps.

**Why "Local Guide" status specifically?**
Local Guide status is Google's own badge for an established, verified
reviewer — the single cheapest, most reliable signal to tell a real
long-time contributor apart from a disposable throwaway account.

### ⚖️ Notes

Reads only Google Maps' own public, already-posted review data — no
login, no private account data, no scraping of anything behind a
sign-in wall.

# Actor input Schema

## `businessQueries` (type: `array`):

The business as you'd type it into Google Maps search (name + city works best, e.g. "Dominos Pizza Times Square New York"). One per line — each is analyzed separately.

## `maxReviewsPerBusiness` (type: `integer`):

How many of the most-recent reviews to fetch and score per business (5-150).

## Actor input object example

```json
{
  "businessQueries": [
    "Dominos Pizza Times Square New York",
    "Starbucks Times Square New York"
  ],
  "maxReviewsPerBusiness": 30
}
```

# Actor output Schema

## `reviews` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "businessQueries": [
        "Dominos Pizza Times Square New York"
    ],
    "maxReviewsPerBusiness": 30
};

// Run the Actor and wait for it to finish
const run = await client.actor("inexhaustible_glass/google-review-fraud-detector").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "businessQueries": ["Dominos Pizza Times Square New York"],
    "maxReviewsPerBusiness": 30,
}

# Run the Actor and wait for it to finish
run = client.actor("inexhaustible_glass/google-review-fraud-detector").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "businessQueries": [
    "Dominos Pizza Times Square New York"
  ],
  "maxReviewsPerBusiness": 30
}' |
apify call inexhaustible_glass/google-review-fraud-detector --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,inexhaustible_glass/google-review-fraud-detector"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/NoQV1Ur9Wnyhit79f/builds/1LZ75HFAxjICpM9sh/openapi.json