Facebook Scraper 2026: How to Scrape Facebook Group with Python

Ever wondered how businesses tap into the goldmine of Facebook data to understand their audience better? The platform hosts billions of user posts, comments, and interactions daily—all potentially valuable for market research, sentiment analysis, or competitive intelligence. But here's the catch: actually getting your hands on that data isn't as simple as you might think.

Facebook's official API exists, but it's deliberately restrictive. The permissions are tight, the rate limits are brutal, and the data you can access is often too limited for any serious analysis. That leaves one alternative: web scraping. But before you dive in, you need to understand what you're up against.

Why Facebook Scraping Is Actually Harder Than You Think

Facebook isn't some small website running on a shoestring budget. We're talking about a tech giant with thousands of engineers dedicated to one mission: keeping bots out. Their anti-scraping system goes way beyond simple IP tracking. They use sophisticated browser fingerprinting, behavioral analysis, and machine learning models that can spot automated activity within seconds.

The company learned this lesson the hard way. Remember the Cambridge Analytica scandal? That massive data breach resulted in huge backlash and even bigger fines. Since then, Facebook has doubled down on security, making large-scale scraping increasingly difficult and expensive.

And there's another concern you can't ignore: the legal risks. Getting caught scraping Facebook could mean more than just getting your accounts banned. Depending on what you do with the data and how you collect it, you might face lawsuits or even criminal charges. Companies have been sued for less.

Still interested? Let's talk about how to actually do it.

The JavaScript Problem (And How to Work Around It)

Most modern scrapers rely on tools like Requests and BeautifulSoup for simple sites, or Selenium when JavaScript rendering is needed. But Facebook throws a curveball: the site is heavily JavaScript-dependent, yet using Selenium makes you an easy target.

Here's why: Facebook uses JavaScript not just to display content, but to fingerprint your browser and analyze your behavior. Selenium's automation signatures are easy to detect, and you'll get blocked after just a few requests.

👉 Build smarter web scrapers that can handle complex JavaScript-heavy sites without getting blocked

The workaround? Forget about the main Facebook website entirely. Instead, target the old mobile version at mobile.facebook.com. This legacy interface doesn't require JavaScript to function, which means you can scrape it with basic tools like Requests and BeautifulSoup. It's not as feature-rich, but it gets the job done for collecting posts, comments, and basic profile data.

Building a Basic Facebook Group Scraper with Python

Here's a straightforward Python script that scrapes text content from Facebook groups using the mobile site. This example focuses on simplicity—it extracts post text but doesn't grab images, videos, or author names. You'll also need to add proxy rotation and pagination handling for any real-world project.

First, install the required libraries:

pip install requests
pip install beautifulsoup4

Now here's the code:

python
import requests
from bs4 import BeautifulSoup

class FBGroupScraper:

def __init__(self, group_id):

    self.group_id = group_id

    self.page_url = "https://mobile.facebook.com/groups/" + self.group_id

    self.page_content = ""

def get_page_content(self):

    self.page_content = requests.get(self.page_url).text

def parse(self):

    soup = BeautifulSoup(self.page_content, "html.parser")

    feed_container = soup.find(id="m_group_stories_container").find_all("p")

    for i in feed_container:

        print(i.text)

group_id = "1463546523692520"
d = FBGroupScraper(group_id)
d.get_page_content()
d.parse()

Replace the group_id with any public Facebook group you want to scrape, and the script will extract the text from posts in that group. Keep in mind this is a bare-bones implementation—production use requires error handling, authentication, and most importantly, proxy rotation to avoid getting blocked.

When DIY Isn't Enough: Ready-Made Facebook Scrapers

Building your own scraper sounds appealing, but maintaining it is another story. Facebook constantly updates their anti-bot measures, which means your code breaks regularly. For most people, using a commercial scraping service makes more sense. These tools handle the hard parts—proxy rotation, browser fingerprinting evasion, and CAPTCHA solving—so you can focus on analyzing the data instead of fighting with the scraper.

👉 Skip the headache of building scrapers from scratch with enterprise-grade scraping infrastructure

Bright Data's Data Collector

Starting at $500 for 151K page loads, Bright Data offers arguably the most robust Facebook scraping solution available. Their Data Collector includes five specialized scrapers: profile scraper, post scraper, product scraper by keyword, organization scraper, and more. The tool is web-based and requires zero coding knowledge. You can request custom collectors if you need to scrape specific Facebook data not covered by their templates. Output comes in Excel format, and you can access a free trial before committing.

Proxycrawl Scraper API

This one's different—it's an API rather than a point-and-click tool. Pricing starts at $29 monthly for 50,000 credits, with the first 1,000 requests free. Proxycrawl returns data as JSON, making it perfect for developers who want to integrate Facebook scraping directly into their applications. You send an HTTP request specifying what you want, and the API handles all the complexity of bypassing Facebook's defenses. It's particularly good for extracting group feeds and their associated comments.

Apify Facebook Page Scraper

With plans starting at $49 per month for 100 Actor compute units (plus a free starter tier), Apify hosts a Facebook Pages Scraper that focuses on public profiles. It extracts posts, reviews, and comments from Facebook pages, returning everything as JSON through their API. The setup is straightforward: send HTTP requests to their endpoints, and responses come back as structured data you can immediately use in your projects.

Phantom Buster Facebook Group Extractor

At $30 monthly for one hour of scraping per day, Phantom Buster offers a cloud-based solution with a 14-day free trial. Their Facebook Group Extractor specializes in community and group data—member profiles, posts, and engagement metrics. The tool outputs data in CSV, Excel, or JSON formats. The trial gives you 10 minutes of scraping daily, which is actually enough for small projects or testing.

Octoparse

Octoparse is one of the most popular general-purpose scrapers, starting at $75 per month with a 14-day free trial. It comes with pre-built Facebook scraping templates, so you don't need to configure everything from scratch. The tool works both as cloud-based software and as a desktop application. It's fast and reliable, though Facebook templates aren't available on the free plan.

ScrapeStorm

Built by an ex-Google crawler team, ScrapeStorm starts at $49.99 monthly with a limited free plan. This desktop application uses intelligent data recognition to automatically identify the information you want to scrape. The visual point-and-click interface makes it beginner-friendly, while the underlying technology is sophisticated enough to handle Facebook's anti-bot measures. It exports to basically any format you need: TXT, CSV, Excel, JSON, MySQL, Google Sheets, and more.

The Bottom Line

Scraping Facebook at scale is expensive and technically demanding. The platform's defenses are constantly evolving, and building a scraper that works today doesn't guarantee it'll work tomorrow. For serious projects, commercial scraping tools are worth the investment—they absorb the maintenance burden and legal risk while delivering more reliable results.

Just remember: regardless of which approach you choose, be mindful of Facebook's terms of service and local data protection laws. The data might be publicly visible, but that doesn't automatically make scraping it legal or ethical in every context.