# Facebook Review Scraper (`scraperoka/facebook-review-scraper`) Actor 🔍 Facebook Review Scraper extracts verified customer reviews with ratings, dates, and key details—fast and reliable. 🚀 Perfect for brand insights, competitor analysis, and improving customer service. 📈 Boost decision-making with real feedback. - **URL**: https://apify.com/scraperoka/facebook-review-scraper.md - **Developed by:** [Scraperoka](https://apify.com/scraperoka) (community) - **Categories:** Social media, Lead generation, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.01 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.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/platform/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 ### Facebook Review Scraper 📊 **Facebook Review Scraper** is a focused tool that scrapes reviews from Facebook pages—so you can collect real customer feedback without manual copy-pasting. Whether you're looking to **scrape Facebook reviews**, **extract Facebook reviews**, or build a **Facebook review lead generator** workflow, this actor helps you turn public review content into usable data. It’s designed for marketers, researchers, and data analysts who want to automate Facebook review scraping software tasks and save hours of manual work at scale. --- ### Why choose Facebook Review Scraper? | Feature | Benefit | |---|---| | ✅ All-in-one review extraction | Extracts reviewer name, profile picture, review text, and links from a Facebook page reviews URL in one run | | ✅ Structured output ready for analysis | Saves results into a dataset with consistent JSON fields for easy import into spreadsheets or pipelines | | ✅ Reliability-focused execution | Includes pagination handling via cursors and stops cleanly when limits are reached or no further pages exist | | ✅ Flexible input controls | Lets you set a maximum number of reviews with `maxReviews` to control run size and cost | | ✅ Apify Console friendly workflow | Runs directly from the Apify input form and writes results to the “Facebook Reviews” dataset | | ✅ Proxy configuration support | Supports selecting “Proxy Configuration” in input to validly scrape Facebook | --- ### Key features - 🔍 **Accurate review fields**: Captures `name`, `message`, `story_url`, `story_id`, and `profile_pic` per review - 📥 **Direct Facebook page reviews URL input**: Uses your provided `startUrl` (Facebook page reviews section URL) to start the scrape - 🔄 **Pagination with cursor handling**: Continues fetching subsequent review pages until `maxReviews` is reached or no more pages are available - 🛡️ **Proxy configuration option**: Works with the provided `proxyConfiguration` setting (includes `proxy support`) - 💾 **Real-time dataset pushing**: Each review is pushed immediately to the output dataset as it’s found - 🧮 **Run-sized output control**: Uses `maxReviews` (minimum 1) to limit how many Facebook reviews you collect - 📊 **Dataset-ready links**: Includes `story_url` so you can trace each extracted Facebook page review back to its source --- ### Input Provide input via an `input.json` file. Example structure: ```json { "startUrl": "https://www.facebook.com/buladiradda/reviews", "maxReviews": 100, "proxyConfiguration": { "useApifyProxy": true } } ```` #### Input Fields | Field | Required | Description | |---|---:|---| | `startUrl` | ✅ | The URL of the Facebook page reviews section (for example: `https://www.facebook.com/buladiradda/reviews`). This is where the actor starts scraping Facebook review scraping content. | | `maxReviews` | ❌ | Maximum number of reviews to scrape. Default is `100`. Minimum value is `1`. | | `proxyConfiguration` | ❌ | Proxy settings for scraping Facebook. The input provides an editor of type `proxy`. The default includes `proxy support: true`. | | `proxyConfiguration.proxy support` | ❌ | When present inside `proxyConfiguration`, enables the “Use Apify Proxy” option. | *** ### Output The actor saves each review’s data into a dataset in JSON format. Example output JSON (each object is one review): ```json [ { "name": "Julia Odan", "profile_pic": "https://scontent-itm1-1.xx.fbcdn.net/v/t39.30808-1/447503218_1421351475235900_1276580829558666368_n.jpg?stp=c0.128.768.768a_cp0_dst-jpg_s50x50_tt6&_nc_cat=100&ccb=1-7&_nc_sid=e99d92&_nc_ohc=tb3gMBl1nZ4Q7kNvwHMkcRR&_nc_oc=AdnbtWHkJecxad6HPguRsNhJVh9ftY5PVIUMvVAI-1g3IZDWWdcj4WucYO2TxYyuF5Y&_nc_zt=24&_nc_ht=scontent-itm1-1.xx&_nc_gid=fXOTY82o1VPchmrLVtteLg&oh=00_AfgvvWN2o7VZh2TnnY5kZ0_lWB6y8cs8_KJIoCETdya-cw&oe=693251AB", "message": "Rubbish product from a misogynistic company.", "story_id": "UzpfSTEwMDAyMDgzMTgyMzg1MjoxMTg5NTQyMDg4NDE2ODQxOjExODk1NDIwODg0MTY4NDE=", "story_url": "https://web.facebook.com/jyn.odan.3/posts/pfbid0FbBNhkzZArg9AYASawZNhDuYAfPaErwHgFGZm7ZTaCn2dZs7Q9ys2UMkG7ejLJ6tl" } ] ``` #### Output Fields | Field | Type | Description | |---|---|---| | `name` | text | Reviewer name | | `message` | text | Review content text | | `story_url` | link | Link to the review/story on Facebook | | `story_id` | text | ID for the review/story | | `profile_pic` | image | URL to the reviewer’s profile picture | *** ### How to use Facebook Review Scraper (via Apify Console) 1. **Open Apify Console** Log in at https://console.apify.com and navigate to the **Actors** area. 2. **Find Facebook Review Scraper** Search for **Facebook Review Scraper** and open the actor page. 3. **Enter your `startUrl`** In the **INPUT** section, provide the Facebook page reviews section URL in `startUrl` (e.g. a URL ending with `/reviews`). 4. **Set `maxReviews` (optional but recommended)** Use `maxReviews` to control how many Facebook reviews you want to collect. If you leave it empty, the actor uses the default value `100`. 5. **Configure proxy settings (optional)** If needed, adjust **Proxy Configuration**. The default includes `proxy support: true`. 6. **Run the actor** Click **Run**. While it executes, check the real-time logs to monitor progress and whether it reaches your `maxReviews` target. 7. **Open the dataset results** After the run completes, go to the **OUTPUT** tab and open the dataset titled **Facebook Reviews**. 8. **Export your data** Export the dataset in the format you need (commonly JSON/CSV from Apify). You can then use it for **scrape Facebook reviews** analysis, reporting, or a **Facebook page review scraper** workflow. No coding required—get structured Facebook reviews data in minutes with Facebook Review Scraper. ✅ *** ### Advanced features & SEO optimization - 🔄 **Engineered for “Facebook review scraper” workflows**: Built specifically for extracting review content from Facebook pages and turning it into dataset rows you can analyze (great for scrape Facebook reviews use cases). - 📊 **Automate Facebook review collection**: Uses your `maxReviews` to keep output predictable and avoid oversized runs when you’re automating Facebook review scraping software tasks. - 💾 **Structured dataset output**: Each scraped review includes consistent fields (`name`, `message`, `story_url`, `story_id`, `profile_pic`) so downstream analysis is straightforward. - 🛡️ **Proxy configuration option**: Includes an input-level proxy configuration so you can validly scrape Facebook in your environment. - 📈 **Pagination-aware collection**: Continues through additional pages until it hits your target review count or can no longer fetch additional pages. *** ### Best use cases - 🎯 **Marketing teams building review-based messaging**: Collect consistent Facebook page review content to identify recurring themes and craft better ad/landing copy. - 🧪 **Researchers studying customer sentiment**: Export `message` plus `story_url` and analyze feedback patterns across multiple review samples. - 🏢 **Local business reputation tracking**: Run Facebook review scraping tool jobs regularly to measure changes over time. - 📈 **Ecommerce brand monitoring**: Track how customers describe product experience, then connect findings to product iterations. - 🧰 **Data analysts preparing supervised datasets**: Use structured fields (`story_id`, `message`, reviewer metadata) for labeling and model training. - 💼 **Sales and lead research**: Use extracted review text and page context to inform outreach—an approach aligned with Facebook review lead generator workflows. - 🔁 **Automation pipelines via Apify**: Schedule runs to continually update a dataset of Facebook reviews data for downstream BI dashboards. *** ### Technical specifications - **Supported Input Formats** - ✅ `startUrl` as a string containing the Facebook page reviews section URL (e.g. ending in `/reviews`) - ✅ `maxReviews` as an integer (minimum `1`, default `100`) - ✅ `proxyConfiguration` as an object with `proxy support` supported by the input schema - **Proxy Support** - ✅ Input supports “Proxy Configuration” and includes `proxy support` in the default object - **Retry Mechanism** - ✅ Includes resilience via pagination handling and stopping conditions based on available pages and reaching `maxReviews` - **Dataset Structure** - ✅ Dataset title: **Facebook Reviews** - ✅ Dataset output fields: `name`, `message`, `story_url`, `story_id`, `profile_pic` - **Rate Limits & Performance** - ⚠️ Performance depends on how quickly the target page returns review batches and network conditions - **Limitations** - ⚠️ Scraping starts from the specific reviews URL you provide in `startUrl` - ⚠️ Output is limited by `maxReviews` *** ### FAQ #### Do I need to provide a `startUrl` to scrape Facebook reviews? ✅ Yes. `startUrl` is the only required input. It must be the Facebook page reviews section URL you want to extract reviews from. #### What does the `maxReviews` setting do? `maxReviews` limits how many reviews the actor will scrape in a single run. The minimum is `1`, and the default is `100`. #### What fields will I get for each Facebook review? Each item in the output dataset includes `name`, `message`, `story_url`, `story_id`, and `profile_pic`. #### Can I control proxy usage? Yes. You can pass `proxyConfiguration` in the input. The schema includes `proxy support` (defaulting to `true` in the provided example). #### Where do the results get saved? The actor pushes each review to the Apify dataset titled **Facebook Reviews**. You can find it under the **OUTPUT** tab after the run. #### Do I need to write code to use Facebook Review Scraper? No. You can run it directly from Apify Console by filling in the INPUT form with `startUrl`, optionally adjusting `maxReviews` and proxy settings. #### How can I request support or suggest a feature? Share feedback or feature requests by emailing . If you’re trying to improve outcomes for scraping reviews from Facebook, tell us what fields or formats you need. *** ### Support & feature requests If you’re using Facebook Review Scraper and want improvements, we’d love to hear from you. 👋 - 💡 **Feature Requests:** Examples include additional export formatting options or enhancements to make results easier to plug into your Facebook review scraper pipeline. - 📧 **Contact:** Email for support or questions about your runs. Your feedback helps shape what we build next for Facebook review scraping tool users. ✅ *** ### *Facebook Review Scraper* — get the most comprehensive Facebook reviews data fast *If you’re serious about extracting Facebook reviews at scale, Facebook Review Scraper is the SEO-optimized way to automate Facebook review collection.* *** ### Disclaimer **This tool accesses publicly accessible sources only.** It does not access private profiles, authenticated pages, or password-protected content. It is your responsibility to comply with applicable laws (including GDPR/CCPA where relevant), spam regulations, and each platform’s Terms of Service. For data removal requests, contact . Please use Facebook Review Scraper responsibly, ethically, and only for legitimate purposes. # Actor input Schema ## `startUrl` (type: `string`): The URL of the Facebook page reviews section (e.g., https://www.facebook.com/buladiradda/reviews) ## `maxReviews` (type: `integer`): Maximum number of reviews to scrape. ## `proxyConfiguration` (type: `object`): Select proxies to validly scrape Facebook. ## Actor input object example ```json { "startUrl": "https://www.facebook.com/buladiradda/reviews", "maxReviews": 100, "proxyConfiguration": { "useApifyProxy": 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 = { "startUrl": "https://www.facebook.com/buladiradda/reviews" }; // Run the Actor and wait for it to finish const run = await client.actor("scraperoka/facebook-review-scraper").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 = { "startUrl": "https://www.facebook.com/buladiradda/reviews" } # Run the Actor and wait for it to finish run = client.actor("scraperoka/facebook-review-scraper").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 '{ "startUrl": "https://www.facebook.com/buladiradda/reviews" }' | apify call scraperoka/facebook-review-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scraperoka/facebook-review-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Facebook Review Scraper", "description": "🔍 Facebook Review Scraper extracts verified customer reviews with ratings, dates, and key details—fast and reliable. 🚀 Perfect for brand insights, competitor analysis, and improving customer service. 📈 Boost decision-making with real feedback.", "version": "1.0", "x-build-id": "60JDZzurRXjhs8z7b" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scraperoka~facebook-review-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scraperoka-facebook-review-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scraperoka~facebook-review-scraper/runs": { "post": { "operationId": "runs-sync-scraperoka-facebook-review-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scraperoka~facebook-review-scraper/run-sync": { "post": { "operationId": "run-sync-scraperoka-facebook-review-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "startUrl" ], "properties": { "startUrl": { "title": "Facebook Page Reviews URL", "type": "string", "description": "The URL of the Facebook page reviews section (e.g., https://www.facebook.com/buladiradda/reviews)" }, "maxReviews": { "title": "Max Reviews", "minimum": 1, "type": "integer", "description": "Maximum number of reviews to scrape.", "default": 100 }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Select proxies to validly scrape Facebook.", "default": { "useApifyProxy": 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 } } } } } } } } } } ```