Fake News Detector - AI-Powered News Verification System

🎯 Overview

An intelligent web application that uses machine learning to detect fake news articles in real-time. Built with React frontend, Express.js backend, and Python Flask ML API, this system provides accurate predictions with explanatory insights.

✨ Features

• Real-time Detection: Instant fake news classification
• Confidence Scoring: Probability-based predictions
• Explainable AI: Key indicators influencing decisions
• Similar Content: Matching with verified real news
• Responsive Design: Works on all devices
• Multi-dataset Training: Trained on diverse news datasets

🛠️ Technology Stack

Frontend

• React 18.2.0 - Modern UI framework
• Axios - HTTP client for API communication
• Custom CSS - Responsive styling

Backend

• Express.js 5.1.0 - Web application framework
• CORS - Cross-origin resource sharing
• Node.js - Runtime environment

Machine Learning

• Python Flask 3.0.3 - ML API framework
• Scikit-learn 1.5.2 - Machine learning library
• Multinomial Naive Bayes - Classification algorithm
• TF-IDF Vectorization - Text feature extraction
• NumPy & Pandas - Data processing

🏗️ Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   React App     │    │  Express.js     │    │   Flask ML      │
│   (Port 3000)   │───▶│   (Port 5000)   │───▶│   (Port 5001)   │
│                 │    │                 │    │                 │
│ • User Interface│    │ • API Gateway   │    │ • ML Processing │
│ • Visualizations│    │ • Request Proxy │    │ • Predictions   │
│ • Form Handling │    │ • CORS Handling │    │ • Explanations  │
└─────────────────┘    └─────────────────┘    └─────────────────┘

🚀 Quick Start

Prerequisites

• Node.js (v14+)
• Python (v3.8+)
• Git

Installation

1. Clone the repository

git clone https://github.com/nihal07g/fake-news-detector-ml.git
cd fake-news-detector-ml

2. Setup Frontend

cd frontend
npm install

3. Setup Backend

cd backend
npm install

4. Setup Python ML API

cd python_api
python -m venv venv
venv\Scripts\activate
pip install -r requirements.txt

5. Train the Model

python train_model.py

Running the Application

Start all three services in separate terminals:

# Terminal 1: ML API
cd python_api
python app.py

# Terminal 2: Backend
cd backend
npm start

# Terminal 3: Frontend
cd frontend
npm start

Visit http://localhost:3000 in your browser.

📊 Model Performance

• Algorithm: Multinomial Naive Bayes
• Features: 3,000 TF-IDF features with bi-grams
• Datasets: WELFake, Custom News, LIAR (optional)
• Accuracy: Optimized through cross-validation
• Response Time: < 2 seconds

🎮 Usage

1. Enter News Text: Paste or type news article content
2. Click Analyze: Press "Check Authenticity" button
3. View Results: See prediction with confidence scores
4. Understand Decision: Review key indicators
5. Compare Content: View similar real news (if fake detected)

📁 Project Structure

fake-news-detector-ml/
├── frontend/                 # React application
│   ├── src/
│   │   ├── App.js           # Main component
│   │   ├── App.css          # Styles
│   │   └── index.js         # Entry point
│   ├── public/              # Static assets
│   └── package.json         # Dependencies
├── backend/                 # Express server
│   ├── server.js           # API gateway
│   └── package.json        # Dependencies
├── python_api/             # ML processing
│   ├── app.py              # Flask API
│   ├── train_model.py      # Model training
│   ├── preprocess_*.py     # Data preprocessing
│   ├── requirements.txt    # Python deps
│   └── *.pkl               # Model artifacts
├── .gitignore              # Git ignore rules
└── README.md               # Documentation

🔧 Configuration

Environment Variables (Optional)

Create .env files for custom configurations:

# Backend (.env)
PORT=5000
PYTHON_API_URL=http://localhost:5001

# Python API (.env)
FLASK_PORT=5001
MODEL_PATH=./model.pkl

🤝 Contributing

1. Fork the repository
2. Create feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open Pull Request

📈 Future Enhancements

• Deep learning models (BERT, LSTM)
• Multi-language support
• User authentication
• Batch processing
• Advanced visualizations
• Mobile application
• Cloud deployment

🐛 Known Issues

• Large dataset files not included in repository
• Model retraining required for optimal performance
• CORS configuration for production deployment

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👨‍💻 Author

Nihal - GitHub

🙏 Acknowledgments

• WELFake Dataset contributors
• Scikit-learn community
• React development team
• Open source community

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