Facebook Reviews Scraper

Facebook Reviews Scraper

Facebook Reviews Scraper is a production-ready Apify actor that extracts public Facebook Page reviews at scale — a Facebook review scraper for sentiment analysis, CX, social proof, and competitor research. It helps you scrape Facebook page reviews with clean, structured outputs, solving the pain of manual copy-paste and unstable tools. Built for marketers, developers, data analysts, and researchers, this Facebook page reviews scraper normalizes inputs, paginates reliably, and exports ready-to-use datasets. Launch automated Facebook reviews scraping for ongoing monitoring and reporting.

What data / output can you get?

Below are the exact fields the actor collects and pushes to the Apify Dataset and aggregates into the Key-Value Store.

Data type	Description	Example value
facebookUrl	The Facebook page URL where the review was posted	https://www.facebook.com/mrbeast
id	Unique review identifier (Base64 encoded)	UzpfSTYxNTg0MzE3MjA0OTY4OjEyMjEwOTkzMzE4OTE0MzkwNjoxMjIxMDk5MzMxODkxNDM5MDY=
user.id	Reviewer’s Facebook ID	pfbid022DhB36PVT9anEH47J7WQu78JYMNhaEGeHHKAhyPXJVNjusrH3TCierbYBcaA7NTBl
user.name	Reviewer’s display name	Helen Head
user.profileUrl	Link to the reviewer’s profile	https://www.facebook.com/people/Helen-Head/pfbid022DhB36PVT9anEH47J7WQu78JYMNhaEGeHHKAhyPXJVNjusrH3TCierbYBcaA7NTBl/
user.profilePic	Profile picture URL (may be null)	null
date	Review creation date (ISO 8601, UTC)	2025-12-23T18:12:27.000Z
url	Direct link to the review post	https://www.facebook.com/permalink.php?...
isRecommended	Whether the reviewer recommended the page	true
text	Full text content of the review	"I'm at the homeless shelter..."
likesCount	Number of likes on the review	1
commentsCount	Number of comments on the review	0
facebookId	Facebook feedback ID	ZmVlZGJhY2s6MTIyMTA5OTMzMTg5MTQzOTA2
postFacebookId	Facebook post ID	122109933189143906
pageName	Name of the Facebook page being reviewed	MrBeast

Notes:

• Results are available in the Dataset (one item per review) and aggregated in the Key-Value Store under key OUTPUT (grouped by page name).
• You can export Facebook reviews to CSV, JSON, or Excel directly from the Apify platform.

Key features

• 🚀 Asynchronous concurrency Scrape multiple Facebook pages in parallel with a controlled concurrency limit for speed and stability.

• 🔄 Reliable cursor-based pagination Uses Facebook’s GraphQL feed cursor to fetch all available reviews in batches with automatic “next cursor” extraction.

• 🧹 Automatic deduplication Filters duplicate reviews by unique IDs during pagination to keep outputs clean and consistent.

• 🧭 Smart input normalization Accepts full URLs, usernames, or keywords and normalizes them to valid /reviews endpoints automatically.

• 🧠 Flexible sorting Sort scraped reviews by newest, oldest, or most_relevant (likes + comments) to prioritize insights.

• 🛡️ Proxy fallback & retries Three-stage anti-blocking: input/no proxy → datacenter → residential, with retries on empty batches for resilience.

• 📦 Structured outputs Pushes each review instantly to the Dataset and also saves aggregated output (grouped by page name) to the Key-Value Store.

• 🧑‍💻 Developer-friendly Works seamlessly with the Apify API and exports so you can build pipelines in your Facebook reviews scraper Python scripts or other integrations.

How to use Facebook Reviews Scraper - step by step

1. Create or log in to your Apify account

   • Visit https://console.apify.com and sign in.

2. Open the Facebook Reviews Scraper

   • Find the actor in your console (search “Facebook Reviews Scraper”).

3. Add input data

   • In the Input tab, paste one or more Facebook page URLs, usernames, or keywords into inputs. The actor auto-normalizes them to /reviews endpoints.

4. (Optional) Configure limits

   • Set maxItems to cap the number of reviews per page (default: 100).

5. (Optional) Configure proxies

   • In proxyConfiguration, you can leave the default {"useApifyProxy": false} or select Apify proxy groups in the editor. The actor includes automatic fallback logic.

6. Start the run

   • Click Start. Watch logs for pagination progress, batches, and proxy stage transitions.

7. Monitor and wait for completion

   • The actor pushes reviews to the Dataset as it scrapes. It also prepares an aggregated OUTPUT object at the end.

8. Download your data

   • Open the Dataset to browse individual reviews or export to CSV/JSON/Excel. Check the Key-Value Store for the aggregated OUTPUT JSON.

Pro Tip: Chain the Dataset or Key-Value Store with the Apify API or add it to a Make/n8n/Zapier workflow to automate “download Facebook reviews” jobs for reporting and alerts.

Use cases

Use case name	Description
Competitor analysis for marketers	Monitor Facebook page reviews across competitor pages to quantify sentiment and identify messaging gaps.
Brand reputation tracking	Track your page’s ratings and reviews over time and prioritize replies by engagement.
CX & support prioritization	Surface high-impact reviews (likes + comments) for faster customer service response and escalation.
Market research & benchmarking	Aggregate reviews across multiple brands or locations to compare themes and satisfaction.
Academic & social research	Collect Facebook reviews data for sentiment studies and social media research methodologies.
Automated reporting pipelines (API)	Integrate dataset exports with BI tools or scripts as a Facebook reviews API alternative.

Why choose Facebook Reviews Scraper?

This Facebook reviews scraping tool is built for precision, automation, and reliability at scale.

• ✅ Accurate, structured output: Clean JSON with consistent fields for analytics and enrichment.
• 🌍 Scales across pages: Concurrency-enabled to handle multiple Facebook business pages in one run.
• 🔌 Easy integrations: Export Facebook reviews to CSV/JSON/Excel or pull via the Apify API.
• 🧑‍💻 Developer-ready: Ideal for building an automated Facebook reviews scraping tool or pipeline in Python.
• 🔒 Robust anti-blocking: Tiered proxy fallback and retries for resilient extraction.
• 🧠 Sorting & limits: Control max items and sort results by recency or engagement.
• 🆚 Better than extensions: Avoid unstable browser add-ons with a production-ready Facebook reviews extractor.

In short, you get a reliable Facebook page reviews scraper that automates collection and delivers analytics-ready data.

Is it legal / ethical to use Facebook Reviews Scraper?

Yes — when used responsibly. This actor extracts data from publicly available Facebook pages only and does not access private or authenticated content.

Guidelines for compliant use:

• Collect public reviews from Facebook pages; avoid private profiles or gated content.
• Ensure your usage adheres to Facebook’s Terms of Service and Apify’s Terms of Service.
• Comply with data protection laws (e.g., GDPR, CCPA) and respect user privacy.
• Use the data ethically and avoid spam or misuse. Consult your legal team for edge cases.

Input parameters & output format

Example JSON input

{
  "inputs": [
    "https://www.facebook.com/mrbeast",
    "tesla"
  ],
  "maxItems": 100,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}

Input fields

• inputs (array, required): List of Facebook page URLs (e.g., https://www.facebook.com/mrbeast) or usernames (e.g., mrbeast). Keywords require resolvable page context. Default: none.
• maxItems (integer, optional): Maximum number of reviews to scrape per page. The actor will stop pagination once this limit is reached. Default: 100. Min: 1, Max: 10000.
• proxyConfiguration (object, optional): Choose which proxies to use. By default, no proxy is used. If Facebook rejects or blocks the request, it will automatically fallback to datacenter proxy, then residential proxy with retries. Default: {"useApifyProxy": false}.

Note: The actor’s code also recognizes an optional sortOrder parameter with values newest, oldest, or most_relevant. If not provided, it defaults to newest.

Dataset output (individual reviews)

{
  "facebookUrl": "https://www.facebook.com/mrbeast",
  "id": "UzpfSTYxNTg0MzE3MjA0OTY4OjEyMjEwOTkzMzE4OTE0MzkwNjoxMjIxMDk5MzMxODkxNDM5MDY=",
  "user": {
    "id": "pfbid022DhB36PVT9anEH47J7WQu78JYMNhaEGeHHKAhyPXJVNjusrH3TCierbYBcaA7NTBl",
    "name": "Helen Head",
    "profileUrl": "https://www.facebook.com/people/Helen-Head/pfbid022DhB36PVT9anEH47J7WQu78JYMNhaEGeHHKAhyPXJVNjusrH3TCierbYBcaA7NTBl/",
    "profilePic": null
  },
  "date": "2025-12-23T18:12:27.000Z",
  "url": "https://www.facebook.com/permalink.php?story_fbid=pfbid036zKdeTsZRHzcZspm8CNjzv3jSuSQFmbjNc6h4FPwLvsBmjYU3ibcHqX4HSukzQakl&id=61584317204968",
  "isRecommended": true,
  "text": "I'm at the homeless shelter in Alexandria Virginia Prince William county building and there's a lot of us that are homeless that should have a Christmas maybe someone should help us all out show people love...",
  "likesCount": 1,
  "commentsCount": 0,
  "facebookId": "ZmVlZGJhY2s6MTIyMTA5OTMzMTg5MTQzOTA2",
  "postFacebookId": "122109933189143906",
  "pageName": "MrBeast"
}

Key-Value Store output (aggregated under key OUTPUT)

{
  "MrBeast": [
    {
      "facebookUrl": "https://www.facebook.com/mrbeast",
      "id": "UzpfSTYxNTg0MzE3MjA0OTY4OjEyMjEwOTkzMzE4OTE0MzkwNjoxMjIxMDk5MzMxODkxNDM5MDY=",
      "user": {
        "id": "pfbid022DhB36PVT9anEH47J7WQu78JYMNhaEGeHHKAhyPXJVNjusrH3TCierbYBcaA7NTBl",
        "name": "Helen Head",
        "profileUrl": "https://www.facebook.com/people/Helen-Head/pfbid022DhB36PVT9anEH47J7WQu78JYMNhaEGeHHKAhyPXJVNjusrH3TCierbYBcaA7NTBl/",
        "profilePic": null
      },
      "date": "2025-12-23T18:12:27.000Z",
      "url": "https://www.facebook.com/permalink.php?story_fbid=pfbid036zKdeTsZRHzcZspm8CNjzv3jSuSQFmbjNc6h4FPwLvsBmjYU3ibcHqX4HSukzQakl&id=61584317204968",
      "isRecommended": true,
      "text": "I'm at the homeless shelter...",
      "likesCount": 1,
      "commentsCount": 0,
      "facebookId": "ZmVlZGJhY2s6MTIyMTA5OTMzMTg5MTQzOTA2",
      "postFacebookId": "122109933189143906",
      "pageName": "MrBeast"
    }
  ],
  "Tesla": []
}

Field notes:

• user.profilePic may be null if not available.
• pageName may fall back to “Unknown” when not detectable from the feed.
• Aggregated keys in the Key-Value Store are pageName strings.

FAQ

How many reviews can I scrape per page?

By default, the actor collects up to 100 reviews per page, controlled by the maxItems input. You can increase this limit up to 10,000 as needed.

Can I sort the reviews by date or relevance?

Yes. The actor supports sorting by newest, oldest, or most_relevant (likes + comments). If sortOrder isn’t provided, it defaults to newest.

Do I need to configure proxies?

No. Proxies are optional. The actor starts with your configuration (or none) and includes a 3-stage fallback: input/no proxy → datacenter → residential, with retries on empty batches.

Can I scrape multiple Facebook pages at once?

Yes. Add multiple URLs or usernames to inputs and the actor will process them concurrently (with a set concurrency limit).

What happens if a page has no reviews or access is restricted?

The actor logs a warning, skips that page, and continues processing others. In the aggregated output, missing pages simply won’t appear, and the Dataset won’t contain items for them.

Is this an alternative to a Facebook reviews API?

Yes. It acts as a Facebook reviews API alternative by exporting structured reviews to the Apify Dataset and Key-Value Store that you can fetch programmatically.

In what formats can I download the data?

You can export the Dataset and aggregated OUTPUT to JSON, CSV, or Excel directly from Apify.

Is login required to extract Facebook page reviews?

No. This Facebook reviews extractor targets publicly available reviews on Facebook pages and does not require authentication.

Closing CTA / Final thoughts

Facebook Reviews Scraper is built to extract structured, analytics-ready Facebook ratings and reviews fast and reliably. With smart pagination, deduplication, sorting, and proxy fallback, it helps marketers, developers, analysts, and researchers automate Facebook reviews data extraction without hassle. Export to CSV/JSON/Excel, integrate via the Apify API, or plug into your Facebook reviews scraper Python workflow. Start your next run today and automate how you extract Facebook reviews from pages at scale.