SpotLiar - Fake News Detection

AI-Powered Truth Verification with Machine Learning

Combat misinformation with cutting-edge NLP and Machine Learning

Quick Start • Features • API • Android App • Documentation

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🌟 Features

🤖 Machine Learning Models

• 🎯 5 Algorithms: Naive Bayes, Logistic Regression, SVM, SGD, Random Forest
• 📊 70%+ Accuracy on LIAR benchmark dataset
• 🔍 Advanced NLP: TF-IDF, N-grams (1-5), Bag of Words
• ⚡ Real-time predictions with confidence scores
• 📈 Model comparison with learning curves

🚀 Production Ready

• 🐳 Docker containerized for instant deployment
• 🌐 REST API with Flask backend
• 📱 Native Android app with Jetpack Compose UI
• 🔒 Network security and HTTPS ready
• 📊 Batch prediction support

📊 Data & Analysis

• 📝 LIAR Dataset: 10,000+ labeled statements
• 🧹 Preprocessing pipeline: Tokenization, stemming, stopword removal
• 📈 EDA included: Distribution plots, quality checks
• 🎨 Visualizations: Confusion matrices, precision-recall curves

🛠️ Developer Experience

• 📖 Complete documentation with guides
• 🧪 30+ unit tests for reliability
• 🔧 One-command setup automation
• 💻 Multiple interfaces: CLI, API, Mobile
• 🎯 Type hints and docstrings

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🚀 Quick Start

🌐 Live Demo (No Installation Required!)

The API is already hosted and running:

https://spotliar.onrender.com

Test it instantly:

curl https://spotliar.onrender.com/api/health

📱 Android App

Download and install the APK to start verifying news immediately!

• Works on any network (WiFi/Mobile data)
• No setup required
• Instant predictions

⚡ Local Installation

git clone <your-repo-url>
cd Fake_News_Detection-master

chmod +x install.sh && ./install.sh
source .venv/bin/activate
python api_server.py

🐳 Docker

docker-compose up -d
curl http://localhost:5000/api/health

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💻 Usage

🖥️ Command Line

python prediction.py

Enter news text: Scientists discover new planet
Result: Real News
Confidence: 87.3%

🌐 REST API

import requests

# Use the live API
response = requests.post('https://spotliar.onrender.com/api/predict',
    json={'text': 'Breaking news: New discovery announced'})

print(response.json())

{
  "success": true,
  "prediction": "Real News",
  "is_fake": false,
  "confidence": 0.873,
  "confidence_percentage": 87.3
}

Live API Base URL: https://spotliar.onrender.com

📱 Android App

1. Open android/ in Android Studio
2. Build and run on device
3. Enter news text and get instant verification

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🔌 API Endpoints

Base URL: https://spotliar.onrender.com

Endpoint	Method	Description
/	GET	Root endpoint (200 OK)
/api/health	GET	Health check
/api/predict	POST	Predict single news article
/api/batch-predict	POST	Predict multiple articles
/api/info	GET	API information

Example Request

# Live API
curl -X POST https://spotliar.onrender.com/api/predict \
  -H "Content-Type: application/json" \
  -d '{"text": "Your news article here"}'

# Local development
curl -X POST http://localhost:5000/api/predict \
  -H "Content-Type: application/json" \
  -d '{"text": "Your news article here"}'

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📱 Android App

Features

• ✨ Modern Material Design 3 UI
• 🎨 Jetpack Compose interface
• 🔄 Real-time predictions
• 📊 Confidence visualization
• 🌐 WiFi and USB connectivity

Setup

The app is pre-configured to use the live API at https://spotliar.onrender.com

Build and Install:

cd android
./gradlew assembleDebug
adb install app/build/outputs/apk/debug/app-debug.apk

For local development, update RetrofitClient.kt:

private const val BASE_URL = "http://localhost:5000/"  // or your local IP

See Android Setup Guide and Wireless Setup for details.

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🧠 How It Works

1️⃣ Data Preprocessing

Raw Text → Tokenization → Stopword Removal → Stemming → Clean Text

2️⃣ Feature Extraction

Clean Text → TF-IDF Vectorization → N-grams (1-5) → Feature Matrix

3️⃣ Model Training

Feature Matrix → Multiple ML Models → Grid Search → Best Model Selection

4️⃣ Prediction

New Text → Preprocessing → Feature Extraction → Model → Prediction + Confidence

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📊 Model Performance

Logistic Regression (Best Model)

• Accuracy: 70.2%
• F1 Score: 0.701
• Precision: 0.68
• Recall: 0.72

Model Comparison

Model	Accuracy	F1 Score	Training Time
Logistic Regression	70.2%	0.701	Fast ⚡
Random Forest	66.5%	0.666	Slow 🐌
Naive Bayes	72.3%	0.723	Fastest ⚡⚡
Linear SVM	67.9%	0.679	Medium 🚶
SGD	71.8%	0.719	Fast ⚡

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📁 Project Structure

Fake_News_Detection-master/
├── 📄 Core ML Modules
│   ├── DataPrep.py              # Data preprocessing
│   ├── FeatureSelection.py      # Feature extraction
│   ├── classifier.py            # Model training
│   ├── model_trainer.py         # Training utilities
│   ├── prediction.py            # CLI prediction
│   └── api_server.py            # REST API
│
├── 📦 Models & Data
│   ├── final_model.sav          # Trained model
│   ├── train.csv                # Training data
│   ├── test.csv                 # Test data
│   └── liar_dataset/            # Original dataset
│
├── 🐳 Deployment
│   ├── Dockerfile               # Container definition
│   ├── docker-compose.yml       # Orchestration
│   └── .env.example             # Configuration
│
├── 📱 Android App
│   └── android/                 # Jetpack Compose app
│
├── 🧪 Testing
│   └── tests/                   # 30+ unit tests
│
└── 📚 Documentation
    ├── README.md                # This file
    ├── QUICKSTART.md            # Quick start guide
    ├── DEPLOYMENT.md            # Deployment guide
    └── CONTRIBUTING.md          # Contribution guide

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🛠️ Tech Stack

Backend

• Python 3.6+: Core language
• scikit-learn: ML algorithms
• NLTK: Natural language processing
• Flask: REST API framework
• pandas/numpy: Data manipulation

Frontend

• Kotlin: Android development
• Jetpack Compose: Modern UI toolkit
• Retrofit: HTTP client
• Material Design 3: UI components

DevOps

• Docker: Containerization
• pytest: Testing framework
• GitHub Actions: CI/CD (optional)

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📖 Documentation

• 📘 Quick Start Guide - Get running in 5 minutes
• 📗 Deployment Guide - Production deployment
• 📙 API Documentation - Complete API reference
• 📕 Android Setup - Mobile app guide
• 📓 Contributing Guide - How to contribute
• 📔 Wireless Setup - WiFi configuration

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🚀 Deployment

☁️ Production (Live)

Currently deployed on Render.com:

• URL: https://spotliar.onrender.com
• Status: ✅ Running
• Uptime: 24/7 (free tier may sleep after inactivity)

Local Development

./start-everything.sh

Deploy Your Own

Render.com (Recommended - Free):

1. Fork this repository
2. Connect to Render.com
3. Deploy with one click
4. See CLOUD_DEPLOYMENT.md for details

Other Platforms:

• 🌊 Railway: $5 credit/month
• ⚡ Fly.io: Free tier available
• 🔥 Heroku: $5/month
• ☁️ AWS/GCP: Full control

See CLOUD_DEPLOYMENT.md for complete deployment guides.

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🧪 Testing

pytest
pytest --cov=. --cov-report=html
make test-coverage

Test Coverage: 30+ unit tests covering:

• ✅ API endpoints
• ✅ Data preprocessing
• ✅ Model predictions
• ✅ Feature extraction

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📊 Dataset

LIAR Dataset

• Source: ACL 2017 Paper by William Yang Wang
• Size: 12,836 labeled statements
• Classes: Binary (True/False)
• Features: Statement text, speaker, context

Data Distribution

• Training: 10,240 samples
• Testing: 1,267 samples
• Validation: 1,284 samples

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🎯 Roadmap

Current Version (v1.0)

• ✅ 5 ML models implemented
• ✅ REST API with Flask
• ✅ Android app with Jetpack Compose
• ✅ Docker containerization
• ✅ Comprehensive documentation

Future Enhancements

• 🔮 Deep learning models (BERT, GPT)
• 🌍 Multi-language support
• 🎨 Web interface
• 📊 Real-time dashboard
• 🔐 User authentication
• 📈 Model explainability (LIME, SHAP)
• 🚀 Kubernetes deployment
• 📱 iOS app

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🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Quick Contribution Steps

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

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📜 License

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

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👥 Authors

• Original Author: nishitpatel01
• Contributors: See contributors

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🙏 Acknowledgments

• LIAR Dataset: William Yang Wang, ACL 2017
• scikit-learn: Machine learning library
• NLTK: Natural language toolkit
• Flask: Web framework
• Jetpack Compose: Android UI toolkit

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📞 Support

• 📧 Email: kpsingh2004@outlook.com
• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions
• 📖 Docs: Full Documentation

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⭐ Star History

If you find this project useful, please consider giving it a star! ⭐

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🎯 Made with ❤️ for fighting misinformation

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