Facebook Scraper: How to Scrape Facebook in 2024

With over 2.96 billion monthly active users as of Q4 2022, Facebook offers a goldmine of valuable data for businesses, researchers, and analysts. [1] However, harvesting Facebook‘s data manually is hugely time-consuming and labor-intensive. This is where web scrapers optimized for Facebook come in.

Facebook scrapers are tools that enable the automated extraction of data from Facebook pages at scale. When used properly, Facebook scrapers allow for efficient, cost-effective, and ethical data collection from the platform.

In this comprehensive guide, we‘ll explore everything you need to know about scraping Facebook in 2024, including:

  • The top paid and open source Facebook scrapers
  • Licensed Facebook datasets as an alternative
  • Python libraries for DIY Facebook scraping
  • How Facebook prevents and detects scraping
  • What types of Facebook data can legally be scraped
  • Leveraging Facebook APIs for data access
  • Best practices for ethical data collection
  • Scraping alternatives like Instagram, Twitter and YouTube

Let‘s dive in and uncover how you can tap into Facebook‘s data riches.

The Top Facebook Scrapers for Efficient Data Extraction

Web scrapers provide a quick and scalable way to extract all types of data from Facebook. Here we compare some of the top paid and open source Facebook scrapers available today:

Bright Data

Bright Data Facebook Scraper

Overview: Bright Data offers a user-friendly web scraper optimized specifically for Facebook. It handles proxies, browsers, and CAPTCHAs automatically in the background.

Features: With Bright Data, you can extract data points like page info, posts, images, hashtags, comments, and more. It also handles pagination and infinite scrolling.

Ease of Use: No coding required. Simple setup via point-and-click interface.

Scalability: Built on auto-scaling infrastructure to handle large-scale Facebook scraping.

Starting Price: $500/month. 7-day free trial available.


Overview: Octoparse is a versatile no-code web scraping tool with built-in support for Facebook. It can extract posts, comments, images, profile data, and other public information.

Features: Handles proxies, CAPTCHAs, and data export. Web recorder to simplify configuration.

Ease of Use: No coding needed. Visual interface to build scrapers.

Scalability: Scales based on cloud infrastructure. Can monitor task load.

Starting Price: $99/month for the Basic plan. 7-day free trial.


Overview: ScrapingBee offers a web scraping API with data centers around the world. It can scrape Facebook by integrating headless Chrome rendering.

Features: Make API calls to extract data from Facebook in JSON format. Automatic IP rotation.

Ease of Use: No coding needed. API requests in the language of your choice.

Scalability: Scales to any volume through cloud-based API.

Starting Price: $99/month for 50,000 pages scraped. $0 free trial.

facebook-scraper (Open Source)

Overview: facebook-scraper is a popular open source Python library for scraping Facebook data.

Features: Can scrape posts, comments, reactions, page data, groups, and more. Handy for small projects.

Ease of Use: Requires Python coding skills. Tricky setup.

Scalability: Limited scalability since runs on a single machine.

Cost: Free and open source.

As you can see, paid tools like Bright Data generally provide greater usability, scalability, and maintenance. But open source libraries give more customization if you have in-house scraping expertise.

Licensed Facebook Datasets – An Alternative to Building Scrapers

As an alternative to investing time building your own specialized Facebook scraper, you can license datasets that have already been scraped from Facebook.

For example, Bright Data offers massive licensed datasets scraped ethically and legally from public Facebook pages and profiles. You can get historical Facebook data on followers, engagement, posts, comments, images, user profiles, and more.

The key benefits of licensed Facebook datasets include:

  • Skip scraping efforts – Avoid the hassle of creating and maintaining scrapers

  • Save money – Cheaper than building your own at scale

  • Get historical data – Datasets contain old historical records

  • Clean structured data – Ready for import and analysis

  • Focus on insights – Data engineers can focus on modeling rather than scrapping

So rather than reinventing the wheel, licensing Facebook datasets lets you skip the scraping process and directly access the data.

Scraping Facebook with Python for Custom Scrapers

While Facebook scrapers make data collection easy, you can also build custom scrapers from scratch using Python. There are several Python libraries that are useful for scraping Facebook:

  • requests – Makes HTTP requests to fetch page content
  • BeautifulSoup – Parses HTML content and extracts data
  • Selenium – Automates browser actions for dynamic page content
  • facebook-scraper – Scrapes posts, comments, reactions and more

Here is some sample code to scrape Facebook posts using these libraries:

import requests
from bs4 import BeautifulSoup 
from facebook_scraper import get_posts

# Fetch page HTML
url = ‘https://www.facebook.com/nasa‘
response = requests.get(url) 
html = response.text

# Parse HTML  
soup = BeautifulSoup(html, ‘html.parser‘)

# Extract posts
for post in get_posts(url, pages=1):

This prints out the text from each post on NASA‘s public Facebook page. The facebook-scraper library handles pagination automatically.

So Python provides the flexibility to build custom Facebook scrapers tailored to your specific use case. You get more control compared to turnkey scraper tools.

How Facebook Detects and Prevents Scraping

Facebook employs advanced technical countermeasures to detect and block scrapers and bots from accessing its platform. Here are some of the key scraping prevention techniques used:

  • Rate limiting – Limits how often you can make requests to prevent abuse.

  • IP blocking – Bans IP addresses that engage in scraping or bot-like behavior.

  • CAPTCHAs – Uses CAPTCHAs to distinguish humans from bots.

  • Pattern recognition – Leverages machine learning to identify patterns indicative of scrapers. Such as repeatedly accessing pages too quickly.

  • Legal action – Facebook‘s security team monitors for ToS violations and issues takedown notices. They even file lawsuits in some severe cases.

Facebook‘s scraping prevention measures pose challenges to building your own robust scraper. Leveraging commercial tools and residential proxies helps bypass these defenses by mimicking natural human web browsing behavior.

What Types of Facebook Data Can Be Scraped Legally?

Facebook‘s terms of service only allow scraping of public data on the platform. Scraping private user data is unethical and illegal.

Here are some examples of public Facebook data types that can legally be scraped:

  • Page profile information – Such as page name, category, description, follower counts.

  • Posts and metadata – Post text, images, videos, views, shares, reactions, etc.

  • Comments – Comment text, timestamps, reactions, engagement.

  • Ads – Ad images, text, targeting criteria, performance stats.

  • Events – Event name, location, date, attendee list.

  • Hashtags – Posts containing a hashtag and related engagement data.

However, you cannot scrape private personal data like email addresses, phone numbers, private messages, chat logs, or hidden friend lists.

When scraping public Facebook data, it‘s crucial to carefully de-identify any personal information related to individual users. This maintains privacy and prevents abuse.

Leveraging Facebook APIs for Authorized Data Access

For authorized access to Facebook data beyond what‘s publicly visible, developers can leverage Facebook‘s APIs. Key Facebook APIs include:

Graph API – Provides access to data like profiles, posts, photos, events, groups and more. Some permissions require review/approval.

Marketing API – Used to manage and analyze Facebook ads and your ad account.

Business SDKs – Allows interactions with business tools like Pages, Messenger, Instagram.

To use Facebook APIs, developers must register as a Facebook developer and comply with all API guidelines. Advantages of APIs include:

  • Access to more data types, including some non-public data requiring permission.
  • Much higher rate limits compared to web scraping.
  • More robust – Less likely to be blocked than scrapers.
  • Guaranteed compliance with Facebook‘s policies.

So leveraging Facebook‘s APIs enables gathering valuable insights through controlled, authorized data access. The tradeoff is that API access requires more technical expertise compared to turnkey scraper tools.

Best Practices for Ethical Facebook Scraping

When scraping any website, including Facebook, it‘s crucial to follow best practices to stay compliant and respect user privacy:

  • Check robots.txt – Review Facebook‘s robots.txt file for guidelines.

  • Use proxies – Rotate IPs via proxies to distribute requests and prevent blocks.

  • Limit frequency – Use reasonable delays between requests to mimic human behavior.

  • De-identify data – Remove any private user information from scraped data.

  • Minimize ad blockers – Disable any ad blockers when scraping to avoid bot detection.

  • Seeking consent – If possible, inform users their public data may be scraped.

  • Minimal data collection – Only scrape the essential data needed for your purpose.

  • Review policy changes – Monitor Facebook‘s Developer Platform Policy for changes.

Scraping ethics boil down to collecting only what you need, getting user consent where possible, not misusing data, and respecting site policies. This allows gathering data responsibly.

Scraping Alternatives: Instagram, Twitter, YouTube

If Facebook doesn‘t fully meet your analysis needs, here are some other major social media sites you can scrape for social data:

Scraping Instagram

With over 2 billion monthly active users, Instagram is a top social media platform. [2] Key data points that can be extracted include:

  • Posts – Images, captions, hashtags, location, user tags
  • Stories – Photos and videos visible for 24 hours
  • Comments – Comment text and metadata
  • Profiles – Bio, followers/following counts, etc.

Scraping Twitter

Twitter has over 300 million monthly active users that generate mountains of valuable data. [3] Examples of Twitter data that can be scraped:

  • Tweets – Tweet text, hashtags, links, media, tweet volume
  • Profiles – Handle, name, bio, location, follower counts
  • Trends – Trending topics by region with tweet volume

Scraping YouTube

YouTube receives over 2 billion logged-in monthly users, who watch over 1 billion hours daily. [4] YouTube scrapers can extract data like:

  • Video metadata – Title, description, tags, view count
  • Comments – Comment text, user, timestamps, sentiment
  • Transcripts – Auto-generated transcripts of video audio
  • Thumbnails – Video preview images in multiple sizes

Each platform requires tailored techniques, but provides a wealth of public data for analysis.


When done properly using tools like Bright Data, scraping publicly available data from Facebook provides efficient, scalable data extraction. Facebook scrapers enable gathering social insights legally and ethically.

To stay compliant, be sure to follow best practices around privacy, proxies, rate limits, and Facebook‘s frequently updated terms. For expanded data access, leverage Facebook‘s developer APIs.

With the right approach, Facebook‘s data can be tapped to understand audiences, trends, and competitors to gain an advantage. Scraping delivers the valuable public Facebook data your business needs to succeed.