How to Scrape Data from Facebook Legally Using Python?

Many industries and businesses implement Facebook data scraping as Facebook’s content consists of posts, comments, and other user-generated content, providing powerful insights they need for brand product tracking, new launch vigilance, sentiment analysis, competitor monitoring, reputation management, and influencer identification.

Facebook recognizes that multiple business sectors want to extract data from their enormous web platform, and therefore, they have implemented some highly sophisticated and strict anti-bot protections that block most automated crawlers & scripts. Moreover, the platform updates its internal APIs constantly and detects unauthorized requests aggressively. This makes Facebook data scraping quite challenging and requires specialized techniques to scrape data successfully.

From adhering to Facebook’s terms of service to deploying the right tools, this article details how to scrape Facebook data legally and effectively with Python in 2025.

What is Facebook Data Scraping?

Facebook data scraping means extracting data from Facebook’s platform. Scripts or bots extract publicly available information such as posts, profiles, pages, events, and other user-generated content from Facebook. Such activity can be split into two categories: authorized scraping (like web crawling by search engines) and unauthorized scraping that breaks their terms of service.

You need to know the legal scene before you actually deploy a Facebook scraper with Python. Facebook’s policies about automatic data gathering will affect what you can scrape and how you should go about it.

Is Scraping Facebook Data legal in 2025?

The legal status of Facebook Data Scraping became clearer when the Ninth Circuit Court of Appeals ruled in 2022 that scraping publicly available data does not violate the Computer Fraud and Abuse Act.

Now, the question is not about whether Facebook data scraping is legal or not, as it has already been granted legal status by the US court. The challenge lies in scraping itself, as Facebook makes scraping difficult.

Any crawler access to the Facebook platform is heavily scrutinized, and the platform makes every possible effort to detect, block, and stop scraping activities. Facebook also uses rate limits and algorithms to spot and block suspicious request patterns. This is why you either need to have extreme technical competence in scraping or hire a professional data scraping company for legally scraping Facebook data.

Understanding Facebook’s Terms of Service

Facebook’s terms of service prohibit data collection other than applicable permitted purposes without Meta’s written permission first.

The terms strictly forbid:

• Collecting data that isn’t publicly available
• Transferring, selling, or licensing collected data to third parties
• Getting around measures that prevent violations
• Breaking robot exclusion headers

What data is considered public?

Public information on Facebook has content that users choose to share as ‘Public’ through the audience selector. Note that content becomes available to anyone, even non-Facebook users, when users mark it as Public. Even when public, avoid scraping personal data and login info.

Step-by-Step Process to Extract Facebook Data Legally Using Python

Python is one of the most preferred data scraping tools that creates custom scripts for legally scraping Facebook datasets. Using Python, create a scraper that can handle Facebook’s anti-scraping measures properly.

Below is the complete process of how to do it:

Step 1: Installing Facebook-Page-Scraper

The Facebook-page-scraper Python library makes it easy to extract data from public Facebook pages. You can install this package by running:

Pip install facebook-page-scraper

This library helps you collect posts, comments, and reactions from public Facebook pages without login credentials. After installation, you can start a scraper instance by setting the page name, posts count, and browser:

from facebook_page_scraper import Facebook_scraper
scraper = Facebook_scraper("meta", 10, "firefox")
json_data = scraper.scrap_data()

The library’s best feature is how it handles Facebook’s dynamic content loading. You should start with small request volumes to test everything before scaling up.

Step 2: Using Selenium and Headless Browsers

Selenium is perfect for Facebook web scraping because it works with JavaScript-rendered content. Facebook loads data dynamically as users scroll, making Selenium invaluable. Start by installing Selenium and a webdriver:

pip install selenium
pip install webdriver-manager

You can set up Selenium to run in headless mode, which works without showing a browser window:

from selenium import webdriver
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.chrome.service import Service
from webdriver_manager.chrome import ChromeDriverManager

options = Options()
options.add_argument("--headless")
options.add_argument("--disable-gpu")
options.add_argument("--no-sandbox")
options.add_argument("--disable-dev-shm-usage")

driver = webdriver.Chrome(service=Service(ChromeDriverManager().install()), options=options)

Selenium shines at handling Facebook’s interface by acting like a real user. It scrolls through feeds, clicks buttons, and manages cookie consent dialogs. This approach helps avoid detection since your scraper behaves like a regular user.

Step 3: Setting up Proxies to Avoid IP bans

Facebook watches and blocks IPs that show unusual request patterns. Proxy rotation helps prevent your scraper from getting banned:

Install a proxy management library

pip install rotating-proxies

Add proxies to your Selenium setup

PROXY = "http://your-proxy-address:port"
options.add_argument(f'--proxy-server={PROXY}')

Here are some tips for effective proxy rotation:

• Residential proxies work better than datacenter proxies and rarely get blocked
• Switch proxies after specific requests or time periods
• Add random delays between requests to copy human browsing
• Track proxy performance and remove blocked ones

Services like iWeb Scraping, Web Screen Scraping, X-Byte Crawling, and 3i Data Scraping provide specialized social media scraping solutions with automatic IP rotation. You can also build your own rotation system:

from itertools import cycle
proxy_pool = cycle(['proxy1:port', 'proxy2:port', 'proxy3:port'])
proxy = next(proxy_pool)

Facebook data scraping takes time. Each request needs delays (3-10 seconds) between actions to stay under Facebook’s anti-bot radar. Good error handling lets your scraper recover from temporary blocks or connection issues.

These tools combined create a strong python-based facebook scraper that can collect data while respecting technical limits and legal boundaries.

Step 4: Preparing Python Script for Facebook Scraping

Setting up your environment with the right tools is the first step in preparing your Python script for Facebook scraping. Your code needs to navigate through Facebook’s complex structure smoothly.

Importing the scraper and setting variables

You’ll need to install specific libraries based on your project requirements. The simple Facebook scraper needs the Facebook-scraper package using pip:

pip install facebook-scraper

The latest version can be installed directly from the GitHub repository:

pip install git+https://github.com/kevinzg/facebook-scraper.git

After installation, import the required functions in your script:

from facebook_scraper import get_posts, get_profile

Your script needs several key variables to work properly:

# Target page to scrape
page_name = "nintendo"

# Number of pages to scrape (try more than 2)
pages_count = 10

# Timeout in seconds
timeout = 30

# Optional credentials if needed
credentials = None # Or (username, password)

# Cookies path if using authenticated scraping
cookies_path = "facebook_cookies.txt"

Facebook serves different information to logged-in and logged-out users. You’ll need browser cookies for authenticated scraping. These can be extracted after logging into Facebook using extensions like Cookie Quick Manager.

Step 5: Handling Cookie Prompts and Page Layout

Cookie consent pop-ups can block vital webpage sections and cause your scraper to miss data. Selenium helps handle these automatically:

from selenium import webdriver
from selenium.webdriver.common.by import By
import time

# Initialize WebDriver
driver = webdriver.Chrome()
driver.get("https://facebook.com/targetpage")

# Wait for cookie prompt and accept
time.sleep(2) # Adjust delay as needed
try:
accept_button = driver.find_element(By.XPATH, '//*[@id="cookie_consent"]/button')
accept_button.click()
except:
# Handle case where cookie prompt doesn't appear
pass

JavaScript can be executed directly for complex scenarios:

driver.execute_script("document.querySelector('#cookie_consent button').click();")

Facebook’s page layout requires locating key elements with appropriate selectors. BeautifulSoup works well with Selenium to parse HTML content:

from bs4 import BeautifulSoup

# Get page source after JavaScript rendering
html_content = driver.page_source
soup = BeautifulSoup(html_content, 'html.parser')

# Find elements using appropriate selectors
# Example: finding all divs with a specific class
elements = soup.find_all('div', class_='your-target-class')

Step 6: Scraping Multiple Pages with Proxy Rotation

Multiple Facebook pages can be scraped efficiently while avoiding detection through proxy rotation. This method changes your IP address after each scraping session:

page_list = ['nintendo', 'playstation', 'xbox']
proxy_port = 10001 # Starting port

for page in page_list:
# Configure proxy with authentication if needed
proxy = f'username:password@proxy.provider.com:{proxy_port}'

# Initialize scraper with current page and proxy
scraper = Facebook_scraper(page, posts_count=100, browser="firefox", 
proxy=proxy, timeout=600, headless=False)

# Export data to CSV
filename = page
directory = "facebook_data"
scraper.scrap_to_csv(filename, directory)

# Rotate proxy for next session
proxy_port += 1

The facebook-scraper library’s built-in functions offer simpler implementations:

from facebook_scraper import get_posts

# Iterate through pages
for page in page_list:
# Get posts from current page
for post in get_posts(page, pages=5, options={"comments": True}):
print(f"Post ID: {post['post_id']}")
print(f"Text: {post['text'][:50]}...")

# Implement delay between pages
time.sleep(10) # 10-second delay

These practices will help your Facebook scraper handle the platform’s complex structure while reducing the risk of blocks.

Step 7: Running the Scraper and Exporting Data

Let’s look at how to run your Facebook scraper Python code and handle the data you collect.

Scraping to JSON or CSV

Your scraper needs a way to save data. The facebook-page-scraper library gives you two quick ways to export your data:

# For JSON output

json_data = scraper.scrap_to_json()
print(json_data)

# For CSV output

filename = page
scraper.scrap_to_csv(filename, directory)

JSON works great for nested data like comments and reactions. CSV files are better when you need to work with spreadsheets. You can also save Facebook data in Excel, XML, or HTML formats based on what you plan to do with it.

Saving results to local folders

Start by creating a folder for your scraped data:

directory = "C:\facebook_scrape_results"

You can save each page’s data in separate files and use the page name as the filename: for page in page_list:

filename = page
scraper.scrap_to_csv(filename, directory)

This setup makes it easy to find and analyze specific pages later. JSONL (Newline Delimited JSON) is another good option that works well with large datasets.

Large-scale scraping operations need database storage:

• Relational databases (MySQL, PostgreSQL)
• NoSQL databases (MongoDB)
• SQLite

Conclusion

Data scraping Facebook offers powerful insights but comes with unique challenges, and therefore, your Facebook scraping toolkit needs specialized techniques, tools, libraries, and custom scripts for avoiding detection and IP bans by Facebook.

Successful Facebook scraping needs a balance between technical skills and ethical handling of the data scraping process.

iWeb Scraping Services can help if your Facebook data extraction needs go beyond your simple Python script solution. Our expertise helps tackle complex scraping challenges while meeting legal requirements.