📢 Hybrid Fake News Detection System

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📝 Description

This project aims to detect whether a given news article is fake or reliable using a hybrid machine learning approach. The system combines the strengths of:

• 🔮 Deep Learning LSTM Classifier
• 🔍 K-means Clustering Model
• 🧠 Naive Bayes Classifier for merging predictions.

The model is deployed using Shiny for Python, allowing users to:

• Input news articles 📰.
• Receive predictions on authenticity ✅/❌.
• View LIME explanations for interpretability ✨.

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✨ Features

• Hybrid System:
  • 🔮 LSTM Classifier for advanced text classification.
  • 📊 K-means Clustering for article grouping.
  • 🧠 Naive Bayes Classifier for combining model predictions.
• Interactive Interface: Built with Shiny for Python.
• Detailed Predictions:
  • 🔵 Probabilities for "Real" vs "Fake."
  • 🔦 LIME explanations highlighting key phrases influencing predictions.

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🛠️ Technologies Used

• Shiny for Python: Interactive user interface.
• Deep Learning (LSTM): Text classification backbone.
• K-means Clustering: For unsupervised grouping.
• Naive Bayes Classifier: For merging results.
• LIME: To explain model decisions.

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🚀 Installation

1. Clone the repository:

   git clone https://github.com/FestusNzuma/hybrid-fake-news-detection.git

2. Install Python dependencies:

   pip install tensorflow shiny lime

3. Run the Shiny app:

   shiny run --app app.py

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💻 Usage

1. Launch the app using the command above.
2. Paste or type a news article in the input box 🖊️.
3. Review the output:
   • Prediction: Real or Fake 🟢/🔴.
   • Probabilities: Confidence levels 📈.
   • LIME Explanation: Highlights the words contributing to the prediction 🔦.

⚠️ Note

• Predictions for long articles may take more time.
• The model was trained on articles approximately 200 words long, so similar lengths are recommended for optimal performance.

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🌐 Live App

The app is live and can be accessed here: Hybrid Fake News Detection App.

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🤝 Contributing

We welcome contributions! Here's how to get started:

1. Fork the Repository 📤.
2. Create a feature branch:

   git checkout -b feature-name

3. Commit your changes:

   git commit -m "Add new feature"

4. Push to the branch:

   git push origin feature-name

5. Submit a Pull Request 📬.

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📜 License

This project is licensed under the MIT License.

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🙏 Acknowledgments

Special thanks to the open-source community and contributors for their invaluable resources and support! 🌟

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