# Trustpilot Review Monitor & Fraud Detector (`miraculous_permission/trustpilot-review-monitor-fraud-detector`) Actor Scrapes Trustpilot reviews, classifies negative reviews by complaint category and urgency using keyword rules (no AI cost), and detects suspicious fake-review patterns. - **URL**: https://apify.com/miraculous\_permission/trustpilot-review-monitor-fraud-detector.md - **Developed by:** [Luis](https://apify.com/miraculous_permission) (community) - **Categories:** E-commerce, Automation, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 review classifieds 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/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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. Actors are written with capital "A". ## 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. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Review Intelligence Monitor Stop reading raw review exports. This Actor scrapes Trustpilot reviews and gives you back a **prioritized, classified complaint report** plus a **fake-review risk signal** — not just a CSV of text. ### What it does 1. **Scrapes** recent reviews from any public Trustpilot company page. 2. **Classifies** every negative review (configurable threshold) using keyword-based rules into: - Complaint category (shipping, product quality, customer service, billing, etc.) - Urgency (low / medium / high — flags legal threats, regulator mentions, viral-complaint risk) - A short summary (the review's own title/text, since classification is rule-based, not AI-generated) 3. **Detects suspicious review patterns**: volume spikes, near-duplicate review text, and thin/new reviewer accounts — surfaced as a risk score (0-100), not a binary accusation. ### No AI API costs Classification and fraud detection both run on local keyword rules and statistics — no LLM calls, no external API key required, no per-review AI cost. You only pay for this Actor's own Apify compute usage, same as any scraper. **Trade-off to know about**: keyword rules catch obvious cases well but won't understand sarcasm, implicit complaints, or unusual phrasing as well as an AI model would. Treat the categorization as a strong first pass, not a perfect one. ### Why this instead of a plain review scraper Plain scrapers give you rows of text. You still have to read 500 reviews to find the 5 that need a response today. This Actor does that triage for you and tells you *which* reviews are urgent and *whether* your review set shows signs of manipulation. ### Input | Field | Required | Description | |---|---|---| | `trustpilotUrl` | Yes | Trustpilot company URL or bare domain | | `maxReviews` | No | Cap on reviews fetched (default 100) | | `negativeRatingThreshold` | No | Ratings at or below this are classified (default 3) | | `runFraudDetection` | No | Toggle the fraud-signal analysis (default true) | ### Output Three record types in the dataset: - `summary` — company name, trust score, counts, top-level risk label - `complaint` — one row per classified negative review, sorted by urgency - `fraud_report` — volume spikes, duplicate-text pairs, thin-account flags, overall risk score ### Important disclaimers - Fraud detection is a **statistical signal**, not proof. Legitimate review-request campaigns can also trigger volume spikes. - Classification is keyword-based, not AI-based — accuracy depends on how closely review wording matches the built-in keyword lists (currently English-only). - This is an independent tool, not affiliated with or endorsed by Trustpilot. # Actor input Schema ## `trustpilotUrl` (type: `string`): Example: https://www.trustpilot.com/review/example.com OR just example.com ## `maxReviews` (type: `integer`): Limit how many recent reviews to pull and analyze (controls cost). ## `negativeRatingThreshold` (type: `integer`): Reviews with a star rating at or below this value are treated as 'negative' and sent for classification. ## `runFraudDetection` (type: `boolean`): Analyzes timing spikes and text similarity across all reviews (not just negative ones). ## Actor input object example ```json { "trustpilotUrl": "https://www.trustpilot.com/review/example.com", "maxReviews": 100, "negativeRatingThreshold": 3, "runFraudDetection": true } ```` # 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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "trustpilotUrl": "https://www.trustpilot.com/review/example.com" }; // Run the Actor and wait for it to finish const run = await client.actor("miraculous_permission/trustpilot-review-monitor-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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "trustpilotUrl": "https://www.trustpilot.com/review/example.com" } # Run the Actor and wait for it to finish run = client.actor("miraculous_permission/trustpilot-review-monitor-fraud-detector").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "trustpilotUrl": "https://www.trustpilot.com/review/example.com" }' | apify call miraculous_permission/trustpilot-review-monitor-fraud-detector --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=miraculous_permission/trustpilot-review-monitor-fraud-detector", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Trustpilot Review Monitor & Fraud Detector", "description": "Scrapes Trustpilot reviews, classifies negative reviews by complaint category and urgency using keyword rules (no AI cost), and detects suspicious fake-review patterns.", "version": "0.0", "x-build-id": "Jevgt0TRBiVdpsSkM" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/miraculous_permission~trustpilot-review-monitor-fraud-detector/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-miraculous_permission-trustpilot-review-monitor-fraud-detector", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/miraculous_permission~trustpilot-review-monitor-fraud-detector/runs": { "post": { "operationId": "runs-sync-miraculous_permission-trustpilot-review-monitor-fraud-detector", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/miraculous_permission~trustpilot-review-monitor-fraud-detector/run-sync": { "post": { "operationId": "run-sync-miraculous_permission-trustpilot-review-monitor-fraud-detector", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "trustpilotUrl" ], "properties": { "trustpilotUrl": { "title": "Trustpilot company URL or domain", "type": "string", "description": "Example: https://www.trustpilot.com/review/example.com OR just example.com" }, "maxReviews": { "title": "Max reviews to fetch", "minimum": 10, "maximum": 2000, "type": "integer", "description": "Limit how many recent reviews to pull and analyze (controls cost).", "default": 100 }, "negativeRatingThreshold": { "title": "Negative rating threshold", "minimum": 1, "maximum": 5, "type": "integer", "description": "Reviews with a star rating at or below this value are treated as 'negative' and sent for classification.", "default": 3 }, "runFraudDetection": { "title": "Run fraud/fake-review detection", "type": "boolean", "description": "Analyzes timing spikes and text similarity across all reviews (not just negative ones).", "default": true } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```