How to scrape Facebook group posts and comments with 5 tools: 1. BrowserAct 2. Facebook Graph API 3. facebook-scraper (Python) 4. BeautifulSoup + requests 5. Selenium --- Ever wondered how market researchers track thousands of Facebook group discussions without spending hours scrolling? Or how brands monitor what customers say about their products in private communities? Facebook Groups host some of the most valuable conversations on the internet. With over 1.8 billion people using Groups every

Method 1: BrowserAct — The No-Code Facebook Group Scraper with Human Login (Best for Closed Groups)

BrowserAct is a no-code browser automation platform that solves Facebook Groups' biggest scraping challenge: access. Most Facebook Groups require membership to view posts, and Facebook aggressively blocks automated login attempts. BrowserAct's human login feature lets you authenticate manually in a real browser session, then automatically extracts posts, comments, and member data using AI-powered extraction.

What sets BrowserAct apart is its real-browser infrastructure with stealth browsing, built-in residential proxies, and CAPTCHA handling. Instead of sending detectable HTTP requests, BrowserAct runs actual browser sessions that look like real users. The AI-powered extraction engine identifies post cards, comment threads, and engagement metrics automatically—you describe what you need in plain English, and the platform handles the rest. When Facebook changes its page structure (which happens frequently), the AI adapts without requiring selector updates.

Steps to Scrape Facebook Groups with BrowserAct

Follow this quick start guide to scrape Facebook group posts and comments:

1. Sign up and create a workflow: Create a free account at BrowserAct. Click "Workflows" → "Create New" and name it (e.g., "Facebook Group Scraper"). Describe your goal in plain English: "Visit a Facebook group page, scroll through posts, and extract post text, author name, timestamp, reaction count, comment count, and share count for each post."

1. Configure input parameters: Set up input variables for your workflow. Add group_url (e.g., https://www.facebook.com/groups/fitnessprofessionals) as the target. Add max_posts to control how many posts to extract (e.g., 50 or 100). If you want comments too, add extract_comments: true and max_comments_per_post: 10.

1. Run and handle login: Click "Start" to execute. When Facebook's login page appears, BrowserAct pauses and opens a live browser session. Log in manually with your Facebook account, complete any 2FA or security checkpoint, and navigate to the group if membership is required. Once you're in, click "Resume" and BrowserAct takes over—scrolling through posts and extracting data automatically.

1. Review and export data: Once the run completes, review extracted post data (post text, author name, timestamp, reaction count, comment count, share count, media URLs) and comment data (comment text, author name, timestamp, reaction count). Export as CSV, JSON, Google Sheets, or via Webhook.

1. Automate with scheduling and session persistence: Set up recurring runs to monitor group activity over time. Use BrowserAct's session persistence to maintain your Facebook login across runs—no need to re-authenticate every time. Connect to Make or n8n to auto-save data to your CRM or database, and set up Slack notifications for new posts matching specific keywords.

For advanced builds, you can add Loop nodes for infinite scrolling, conditional nodes that filter posts by keyword or date range, and Extract Data nodes with custom prompts like "Extract only posts that mention 'supplement' or 'protein powder' along with all replies to those posts."

What Data Can You Extract?

BrowserAct's AI-powered extraction can pull any publicly visible data from Facebook group pages:

• Group Posts: Post text/content, author name and profile URL, timestamp, reaction count (total likes, loves, etc.), comment count, share count, media attachments (images, videos, links), and post permalink.
• Comments and Replies: Comment text, author name and profile URL, timestamp, reaction count, reply count, and parent post reference. Threaded replies are preserved with their parent comment relationships.
• Group Metadata: Group name, group URL, member count, group description, privacy setting (Public/Closed/Private), and group category.

Outputs are available in formats like JSON, CSV, XML, or Markdown, making it easy to integrate scraped data into sentiment analysis pipelines, community management dashboards, or market research reports.

Pros

• No coding required—describe what you need in plain English
• Human login feature handles Facebook's auth wall and group membership checks safely
• Real browser sessions with stealth browsing and residential proxies
• AI-powered extraction adapts to Facebook's frequent page structure changes
• Session persistence for maintaining login across runs
• Flexible export formats and integrations with Make, n8n, Zapier

Cons

• Requires manual login for first run (but session persistence reduces frequency)
• Credit-based pricing may add up for very large-scale extraction
• Cloud-based—no offline execution

Ready to try it? Sign up for BrowserAct's free trial and start scraping Facebook groups today!

Method 2: Facebook Graph API (Official and Compliant)

The Facebook Graph API is Meta's official way to access Facebook data programmatically. For Groups specifically, the API allows group admins to access posts and comments in their own groups—but you cannot scrape groups you don't admin.

Steps to Scrape Facebook Groups with the Graph API

1. Create a Meta Developer app: Go to developers.facebook.com and create a new app. Add the Facebook Login and Graph API products.

1. Request Group Access permissions: You'll need groups_access_member_info and pages_read_engagement permissions. These require App Review approval for production use, which can take several weeks.

1. Connect your group: Use the Graph API Explorer to get your User Access Token, then query the /me/groups endpoint to list groups where you're an admin. Note the group ID for the group you want to scrape.

1. Query group feed: Use the /{group-id}/feed endpoint to retrieve posts. Each post object includes message, created_time, from (author), reactions, comments, and permalink. Paginate using cursor-based pagination.

1. Retrieve comments for each post: For each post ID, query /{post-id}/comments to get comment text, author, timestamp, and reaction counts. Handle nested replies with /{comment-id}/comments.

import requests
import json
 
ACCESS_TOKEN = "YOUR_ACCESS_TOKEN"
GROUP_ID = "YOUR_GROUP_ID"  # Must be a group where you're an admin
 
# Get group feed (posts)
def get_group_posts(group_id, limit=25):
    url = f"https://graph.facebook.com/v18.0/{group_id}/feed"
    params = {
        "fields": "message,created_time,from,permalink_url,reactions.summary(true),comments.summary(true),shares",
        "limit": limit,
        "access_token": ACCESS_TOKEN,
    }
 
    all_posts = []
    response = requests.get(url, params=params)
    data = response.json()
 
    while True:
        for post in data.get("data", []):
            post_data = {
                "post_id": post.get("id"),
                "message": post.get("message", ""),
                "author": post.get("from", {}).get("name"),
                "author_id": post.get("from", {}).get("id"),
                "created_time": post.get("created_time"),
                "permalink": post.get("permalink_url"),
                "reactions": post.get("reactions", {}).get("summary", {}).get("total_count", 0),
                "comments_count": post.get("comments", {}).get("summary", {}).get("total_count", 0),
                "shares": post.get("shares", {}).get("count", 0),
            }
            all_posts.append(post_data)
            print(f"[{post_data['created_time']}] {post_data['author']}: {post_data['message'][:80]}...")
 
        # Paginate
        paging = data.get("paging", {})
        next_url = paging.get("next")
        if not next_url:
            break
        response = requests.get(next_url)
        data = response.json()
 
    return all_posts
 
# Get comments for a specific post
def get_post_comments(post_id):
    url = f"https://graph.facebook.com/v18.0/{post_id}/comments"
    params = {
        "fields": "message,created_time,from,reactions.summary(true),comment_count",
        "limit": 50,
        "access_token": ACCESS_TOKEN,
    }
 
    response = requests.get(url, params=params)
    comments = response.json().get("data", [])
 
    for comment in comments:
        print(f"  💬 {comment.get('from', {}).get('name')}: {comment.get('message', '')[:60]}...")
        print(f"     Reactions: {comment.get('reactions', {}).get('summary', {}).get('total_count', 0)}")
 
    return comments
 
# Scrape posts and their comments
posts = get_group_posts(GROUP_ID, limit=25)
print(f"\nExtracted {len(posts)} posts\n")
 
for post in posts[:5]:  # Get comments for first 5 posts
    print(f"\nComments for post by {post['author']}:")
    get_post_comments(post["post_id"])

Pros

• Fully compliant with Facebook's terms of service
• Official, stable API with comprehensive documentation
• No risk of IP bans or account suspensions
• Access to structured, reliable JSON data
• Supports webhooks for real-time post notifications

Cons

• Only works for groups where you are an admin—you cannot scrape other groups
• Requires App Review approval for production access (weeks-long process)
• Rate limits: 200 requests per hour per user per app
• Cannot access reactions breakdown (like, love, haha separately) without additional permissions
• Member data is limited to name and ID—no profile details

Method 3: facebook-scraper Python Library (Open-Source)

The facebook-scraper library is an unofficial Python tool that extracts posts from Facebook groups and pages by parsing the mobile web version of Facebook. It's one of the most popular open-source options for Facebook group scraping because it handles login, pagination, and post parsing out of the box.

Steps to Scrape Facebook Groups with facebook-scraper

1. Install the library: Run pip install facebook-scraper to install the package. You'll also need a valid Facebook account for authentication.

1. Log in with cookies: The library uses Facebook cookies for authentication. Export your Facebook session cookies using a browser extension like "EditThisCookie" and pass them to the scraper, or use the built-in login function.

1. Scrape group posts: Use the get_posts() function with the group name or ID. Configure parameters like pages (number of pages to scrape), extra_info (include reaction breakdowns), and comments (extract comments for each post).

1. Filter and process data: Each post is returned as a dictionary with fields like post_id, text, timestamp, author, reactions, comments, shares, image URLs, and post URL. Filter by date, keyword, or engagement metrics as needed.

1. Handle rate limiting and errors: The library includes built-in rate limiting, but you should add custom delays between requests, rotate cookies/accounts for large-scale scraping, and implement retry logic for connection errors.

from facebook_scraper import get_posts
import json
import time
 
# Scrape posts from a Facebook group
# Note: Use the group name from the URL (facebook.com/groups/GROUP_NAME)
group_name = "FitnessProfessionalsForum"
all_posts = []
 
for post in get_posts(
    group=group_name,
    pages=10,              # Scrape 10 pages of posts (~100-200 posts)
    extra_info=True,       # Include reaction breakdowns
    comments=True,         # Extract comments for each post
    cookies="cookies.json", # Path to your exported Facebook cookies
    options={
        "comments": 20,     # Max comments per post
        "reactors": False,  # Don't extract individual reactor profiles
    }
):
    post_data = {
        "post_id": post.get("post_id"),
        "text": post.get("text", ""),
        "post_url": post.get("post_url"),
        "timestamp": str(post.get("timestamp")),
        "author": post.get("username"),
        "likes": post.get("likes"),
        "comments_count": post.get("comments"),
        "shares": post.get("shares"),
        "reactions": post.get("reactions", {}),
        "image_ids": post.get("image_ids", []),
        "video_id": post.get("video_id"),
        "images": post.get("images", []),
    }
 
    all_posts.append(post_data)
    print(f"[{post_data['timestamp']}] {post_data['author']}: {post_data['text'][:80]}...")
    print(f"  Reactions: {post_data['likes']} | Comments: {post_data['comments_count']} | Shares: {post_data['shares']}")
 
    # Extract comments
    if post.get("comments_full"):
        for comment in post["comments_full"]:
            print(f"  💬 {comment.get('commenter_name')}: {comment.get('comment_text', '')[:60]}...")
 
    time.sleep(2)  # Rate limiting between posts
 
# Save to file
with open(f"{group_name}_posts.json", "w", encoding="utf-8") as f:
    json.dump(all_posts, f, ensure_ascii=False, indent=2)
 
print(f"\nTotal posts extracted: {len(all_posts)}")

Pros

• Free and open-source
• Handles Facebook login, pagination, and post parsing automatically
• Can extract both posts and comments in a single run
• Returns structured data with reaction breakdowns
• Supports group, page, and profile scraping
• Active community with regular updates

Cons

• Frequently breaks when Facebook updates its page structure
• Requires exporting and managing Facebook cookies manually
• Risk of account ban if used too aggressively
• Can be slow—each post with comments takes 5-15 seconds
• Cookie expiration requires re-exporting session cookies periodically

Method 4: BeautifulSoup + requests (Lightweight Post Extraction)

Using requests and BeautifulSoup to scrape Facebook groups is the most challenging lightweight approach because Facebook's web pages are heavily JavaScript-rendered and aggressively protected. This method works only for public groups and requires careful handling of authentication cookies.

Steps to Scrape Facebook Groups with BeautifulSoup

1. Install dependencies: Run pip install requests beautifulsoup4 lxml to install the required packages.

1. Export Facebook cookies: Use a browser extension to export your Facebook session cookies as a JSON file. These cookies are required to access any Facebook content, even public groups.

1. Load cookies into requests session: Create a requests.Session and populate it with your Facebook cookies. This simulates an authenticated browser session.

1. Fetch group page and parse HTML: Request the group's page URL using the mobile version (mbasic.facebook.com) which returns simpler, more parseable HTML. Extract post elements using CSS selectors.

1. Handle pagination: Facebook uses cursor-based pagination for group feeds. Parse the "See More" link to get the next page URL and implement a pagination loop.

import requests
from bs4 import BeautifulSoup
import json
import time
 
def scrape_facebook_group(group_name, cookies_file="cookies.json", max_posts=50):
    # Load cookies
    with open(cookies_file, "r") as f:
        cookies_list = json.load(f)
 
    session = requests.Session()
    for cookie in cookies_list:
        session.cookies.set(
            cookie["name"],
            cookie["value"],
            domain=cookie.get("domain", ".facebook.com"),
        )
 
    headers = {
        "User-Agent": "Mozilla/5.0 (iPhone; CPU iPhone OS 16_0 like Mac OS X) AppleWebKit/605.1.15",
        "Accept-Language": "en-US,en;q=0.9",
    }
 
    # Use mbasic.facebook.com for simpler HTML
    url = f"https://mbasic.facebook.com/groups/{group_name}"
    all_posts = []
    next_url = url
 
    while next_url and len(all_posts) < max_posts:
        response = session.get(next_url, headers=headers)
 
        if response.status_code != 200:
            print(f"Failed: HTTP {response.status_code}")
            break
 
        soup = BeautifulSoup(response.text, "html.parser")
 
        # Find post containers (mbasic uses simpler structure)
        posts = soup.find_all("div", class_="dw")
 
        for post in posts:
            # Extract post text
            text_elem = post.find("div", class_="cq")
            text = text_elem.get_text(strip=True) if text_elem else ""
 
            # Extract author
            author_elem = post.find("h3")
            author = author_elem.get_text(strip=True) if author_elem else "Unknown"
 
            # Extract timestamp
            timestamp_elem = post.find("abbr")
            timestamp = timestamp_elem.get_text(strip=True) if timestamp_elem else ""
 
            # Extract reaction/comment counts (varies by page structure)
            footer = post.find("div", class_="cl")
            footer_text = footer.get_text(strip=True) if footer else ""
 
            post_data = {
                "author": author,
                "text": text[:500],
                "timestamp": timestamp,
                "footer": footer_text,
            }
 
            all_posts.append(post_data)
            print(f"[{timestamp}] {author}: {text[:80]}...")
 
        # Find "See More" link for pagination
        see_more = soup.find("a", string=lambda s: s and "See More" in s)
        if see_more:
            next_url = "https://mbasic.facebook.com" + see_more.get("href", "")
        else:
            next_url = None
 
        time.sleep(3)  # Rate limiting
 
    print(f"\nExtracted {len(all_posts)} posts")
    return all_posts
 
# Scrape a public group
posts = scrape_facebook_group("FitnessProfessionalsForum", max_posts=30)

Pros

• Full control over extraction logic and data processing
• No third-party dependencies beyond requests and BeautifulSoup
• Very fast for single-page requests
• Works with mbasic.facebook.com which has simpler HTML
• Easy to customize for specific data fields

Cons

• Only works for public groups (closed groups require membership and JavaScript rendering)
• Extremely fragile—Facebook's HTML structure changes frequently
• mbasic.facebook.com may be deprecated or restricted at any time
• No access to reaction breakdowns, image URLs, or comment threads
• Cookie management is manual and error-prone
• High risk of being blocked even with valid cookies

Method 5: Selenium (Full Browser Automation for Closed Groups)

Selenium provides complete browser control, making it the most capable method for scraping Facebook groups—especially closed groups that require membership and JavaScript rendering. It can handle login, group navigation, infinite scrolling, comment expansion, and reaction data extraction. However, it's also the slowest and most resource-intensive approach.

Steps to Scrape Facebook Groups with Selenium

1. Install Selenium and undetected-chromedriver: Run pip install selenium undetected-chromedriver. The undetected-chromedriver package is essential for Facebook, which has aggressive bot detection.

1. Log in to Facebook: Navigate to Facebook's login page and authenticate manually or via Selenium. For safety, use a test account and implement 2FA handling. Handle security checkpoints (email/phone verification) gracefully.

1. Navigate to group and scroll: Go to the target group URL. If the group is closed, ensure your account is a member. Implement a scroll loop to load posts via infinite scrolling. Wait for post elements to appear using explicit waits.

1. Extract post data: Use CSS selectors to locate post containers, then extract post text, author name, timestamp, reaction count, comment count, and share count. Expand "See more" links to get full post text.

1. Extract comments: Click on individual posts to open them in full view, then scroll through the comment section. Extract comment text, author, timestamp, and reaction count for each comment and reply.

from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.common.keys import Keys
import undetected_chromedriver as uc
import time
import json
import random
 
def scrape_facebook_group(group_url, max_posts=30, extract_comments=True):
    options = uc.ChromeOptions()
    options.add_argument("--disable-popup-blocking")
    options.add_argument("--lang=en-US")
 
    driver = uc.Chrome(options=options)
 
    try:
        # Step 1: Log in to Facebook
        driver.get("https://www.facebook.com/login.php")
        time.sleep(3)
 
        email_input = WebDriverWait(driver, 10).until(
            EC.presence_of_element_located((By.ID, "email"))
        )
        password_input = driver.find_element(By.ID, "pass")
        email_input.send_keys("your_test_account@email.com")
        password_input.send_keys("your_password")
        password_input.send_keys(Keys.RETURN)
        time.sleep(8)  # Wait for login + potential security checks
 
        # Step 2: Navigate to group
        driver.get(group_url)
        time.sleep(5)
 
        # Step 3: Scroll to load posts
        last_height = driver.execute_script("return document.body.scrollHeight")
        scroll_count = 0
        max_scrolls = 15
 
        while scroll_count < max_scrolls:
            driver.execute_script("window.scrollTo(0, document.body.scrollHeight)")
            time.sleep(random.uniform(2, 4))
            new_height = driver.execute_script("return document.body.scrollHeight")
            if new_height == last_height:
                break
            last_height = new_height
            scroll_count += 1
 
        # Step 4: Extract posts
        post_elements = driver.find_elements(By.CSS_SELECTOR, "[data-ad-comet-preview='']")
        if not post_elements:
            # Fallback selector
            post_elements = driver.find_elements(By.CSS_SELECTOR, "div[role='article']")
 
        extracted_posts = []
        for post in post_elements[:max_posts]:
            try:
                # Expand "See more" if present
                see_more = post.find_elements(By.CSS_SELECTOR, "div[role='button']")
                for btn in see_more:
                    if "more" in btn.text.lower():
                        driver.execute_script("arguments[0].click()", btn)
                        time.sleep(0.5)
 
                # Extract post text
                text_elems = post.find_elements(By.CSS_SELECTOR, "[data-ad-preview='message'], div[dir='auto']")
                post_text = " ".join([e.text for e in text_elems if e.text])
 
                # Extract author
                author_elems = post.find_elements(By.CSS_SELECTOR, "h2 a, h3 a, strong a")
                author = author_elems[0].text if author_elems else "Unknown"
 
                # Extract timestamp
                time_elems = post.find_elements(By.CSS_SELECTOR, "abbr, span[id*='timestamp']")
                timestamp = time_elems[0].get_attribute("title") if time_elems else ""
                if not timestamp and time_elems:
                    timestamp = time_elems[0].text
 
                # Extract reaction/comment/share counts
                reaction_elems = post.find_elements(By.CSS_SELECTOR, "[aria-label*='reaction'], span[aria-label*='reaction']")
                comment_elems = post.find_elements(By.CSS_SELECTOR, "a[href*='comment']")
                share_elems = post.find_elements(By.CSS_SELECTOR, "a[href*='share']")
 
                post_data = {
                    "author": author,
                    "text": post_text[:500],
                    "timestamp": timestamp,
                    "reactions": reaction_elems[0].text if reaction_elems else "0",
                    "comments_link": comment_elems[0].get_attribute("href") if comment_elems else "",
                }
 
                extracted_posts.append(post_data)
                print(f"[{timestamp}] {author}: {post_text[:80]}...")
 
            except Exception as e:
                continue
 
        # Step 5: Extract comments (optional)
        if extract_comments and extracted_posts:
            for post in extracted_posts[:5]:  # Comments for first 5 posts
                if post.get("comments_link"):
                    driver.get(post["comments_link"])
                    time.sleep(3)
 
                    # Scroll to load comments
                    for _ in range(3):
                        driver.execute_script("window.scrollTo(0, document.body.scrollHeight)")
                        time.sleep(2)
 
                    comment_elements = driver.find_elements(By.CSS_SELECTOR, "div[role='article']")
                    print(f"\n  Comments for post by {post['author']}:")
                    for comment in comment_elements[:10]:
                        try:
                            comment_text = comment.find_element(By.CSS_SELECTOR, "div[dir='auto']").text
                            comment_author = comment.find_element(By.CSS_SELECTOR, "h3 a, h2 a").text
                            print(f"    💬 {comment_author}: {comment_text[:60]}...")
                        except:
                            continue
 
        print(f"\nExtracted {len(extracted_posts)} posts")
 
    finally:
        driver.quit()
 
# Scrape a Facebook group
scrape_facebook_group(
    "https://www.facebook.com/groups/fitnessprofessionals",
    max_posts=30,
    extract_comments=True
)

Pros

• Handles JavaScript rendering, infinite scrolling, and "See more" expansions
• Can access closed groups (if your account is a member)
• Full DOM access for extracting posts, comments, reactions, and media
• undetected-chromedriver reduces anti-bot detection
• Can expand comment threads and extract nested replies

Cons

• Very slow—30 posts with comments can take 15-30 minutes
• High risk of Facebook account ban or "You're temporarily blocked" errors
• Resource-intensive (each browser instance uses 200-500MB RAM)
• Extremely fragile—Facebook updates its DOM structure frequently
• Login automation violates Facebook's ToS and risks permanent account suspension
• Security checkpoints (2FA, email verification) can interrupt automated sessions

Comparison of 5 Effective Facebook Group Scraping Methods

For most users, no-code tools like BrowserAct offer the best balance of safety, data coverage, and reliability—especially because its human login feature safely handles Facebook's auth wall and group membership checks without risking account bans.

Conclusion

We've covered 5 powerful methods for how to scrape Facebook group posts and comments, from the official Graph API to no-code automation, each unlocking the platform's community conversations for sentiment analysis, customer feedback mining, and market research. Pick the one that best fits your skills and goals to get started efficiently.

• For marketers and researchers: Go with BrowserAct—it's no-code, its human login feature safely handles Facebook's auth wall and closed group access, and it can extract both posts and comments without risking your account.
• For group admins: Use the Facebook Graph API to access your own group's posts and comments programmatically—fully compliant, official, and webhook-supported for real-time monitoring.
• For developers and data analysts: The facebook-scraper library gives you the most Facebook-specific features for free, including comment extraction and reaction breakdowns—but be prepared for maintenance when Facebook updates its page structure.
• For quick post text extraction: BeautifulSoup + requests with mbasic.facebook.com can pull basic post text from public groups, but don't expect comments, reactions, or media URLs.
• For complex scraping of closed groups: Selenium provides full browser control for accessing closed groups and extracting comment threads, but expect slow performance, high resource usage, and significant ban risk.

For most users, starting with BrowserAct or the facebook-scraper library provides a strong balance of ease, data coverage, and account safety.

Whether you're a community manager monitoring customer sentiment or a market researcher tracking industry discussions, mastering how to scrape Facebook groups with the right Facebook scraper can supercharge your projects.

Try BrowserAct's no-code Facebook group scraper today—human login keeps your account safe while extracting posts and comments in minutes!