Real-Time Fake News Detection System

Overview

This project is a web application designed to detect and analyze fake news in real-time. It utilizes machine learning algorithms and web scraping techniques to classify news articles as real or fake, providing a user-friendly interface for efficient analysis.

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Features

• Real-Time Detection: Analyze news articles instantly.
• Machine Learning Integration: Leverages advanced algorithms for accurate predictions.
• Web Scraping: Automatically extracts and processes news articles from websites.
• Responsive Design: Optimized for various devices using Bootstrap.

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Technology Stack

Frontend

• HTML/CSS/JavaScript
• Bootstrap: Version 4 or later for responsive design.

Backend

• PHP
• Apache Web Server (via XAMPP)

Database

• MySQL: To store user data, news articles, and classification results.
• phpMyAdmin: For managing the MySQL database.

Machine Learning

• Python: Version 3.6 or later.
• Libraries:
  • Pandas
  • NumPy
  • Scikit-learn
  • NLTK
  • GoogleNews (for fetching articles)

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Algorithms

The project employs multiple machine learning models for classification. The final implementation utilizes:

• Random Forest Classifier:
  • Handles high-dimensional data efficiently.
  • Robust and reliable for real-time text classification tasks.

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Development Environment

• Code Editor: Visual Studio Code, Sublime Text, or PHPStorm.
• Operating System: Compatible with Windows, macOS, or Linux.
• Web Browser: Any modern browser.

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Installation and Setup

1. Clone the Repository:

   git clone [repository_url]

Install Requirements: Python libraries: bash Copy code pip install pandas numpy scikit-learn nltk PHP and MySQL setup via XAMPP. Configure the Database: Set up the MySQL database using the provided schema. Use phpMyAdmin for easier management. Run the Application: Start the Apache and MySQL servers using XAMPP. Open the project in a browser. Ethical Guidelines for Web Scraping Ensure compliance with the website’s terms of service. Avoid unethical practices to prevent potential bans. Contributors Eesha Pai (4CB21AI011) Makwin (4CB21AI020) Mazeen A. Shaikh (4CB21AI023) Raksha Prabhu (4CB21AI030) Future Scope Extend functionality to include sentiment analysis. Implement advanced NLP techniques for better accuracy. Add multilingual support for detecting fake news in various languages.