MetaDataScraper 1.0.1

A module designed to automate the extraction of follower counts and post details from a public Facebook page.

MetaDataScraper

MetaDataScraper is a Python package designed to automate the extraction of information like follower counts, and post details & interactions from a public Facebook page, in the form of a list. It uses Selenium WebDriver for web automation and scraping.
The module provides two classes: LoginlessScraper and LoggedInScraper. The LoginlessScraper class does not require any authentication or API keys to scrape the data. However, it has a drawback of being unable to access some Facebook pages. The LoggedInScraper class overcomes this drawback by utilising the credentials of a Facebook account (of user) to login and scrape the data.

Installation

You can install MetaDataScraper using pip:

pip install MetaDataScraper

Make sure you have Python 3.x and pip installed.

Usage

To use MetaDataScraper, follow these steps:

1. Import the LoginlessScraper or the LoggedInScraper class:

   from MetaDataScraper import LoginlessScraper, LoggedInScraper
   

2. Initialize the scraper with the Facebook page ID:

   page_id = "your_target_page_id"
   scraper = LoginlessScraper(page_id)
   email = "your_facebook_email"
   password = "your_facebook_password"
   scraper = LoggedInScraper(page_id, email, password)
   

3. Scrape the Facebook page to retrieve information:

   result = scraper.scrape()
   

4. Access the scraped data from the result dictionary:

   print(f"Followers: {result['followers']}")
   print(f"Post Texts: {result['post_texts']}")
   print(f"Post Likes: {result['post_likes']}")
   print(f"Post Shares: {result['post_shares']}")
   print(f"Is Video: {result['is_video']}")
   print(f"Video Links: {result['video_links']}")
   

Features

• Automated Extraction: Automatically fetches follower counts, post texts, likes, shares, and video links from Facebook pages.
• Comprehensive Data Retrieval: Retrieves detailed information about each post, including text content, interaction metrics (likes, shares), and multimedia (e.g., video links).
• Flexible Handling: Adapts to diverse post structures and various types of multimedia content present on Facebook pages, like post texts or reels.
• Enhanced Access with Logged-In Scraper: Overcomes limitations faced by anonymous scraping (loginless) by utilizing Facebook account credentials for broader page access.
• Headless Operation: Executes scraping tasks in headless mode, ensuring seamless and non-intrusive data collection without displaying a browser interface.
• Scalability: Supports scaling to handle large volumes of data extraction efficiently, suitable for monitoring multiple Facebook pages simultaneously.
• Dependency Management: Utilizes Selenium WebDriver for robust web automation and scraping capabilities, compatible with Python 3.x environments.
• Ease of Use: Simplifies the process with straightforward initialization and method calls, facilitating quick integration into existing workflows.

Dependencies

• selenium
• webdriver_manager

License

This project is licensed under the Apache Software License Version 2.0 - see the LICENSE file for details.