Fake News Detector

The Challenge

Misinformation spreads rapidly online, and detecting fake news requires sophisticated analysis. The challenge was:

• Model Complexity: Single model insufficient for nuanced misinformation detection
• Real-Time Processing: Need instant verification across thousands of articles daily
• Resource Constraints: Large transformer models demanding significant compute
• User Experience: Dashboard must be intuitive for non-technical users

The Approach

I engineered a multi-factor credibility analysis system using advanced ML techniques and architectural patterns:

ML Classification & Sentiment Analysis

• Engineered a multi-factor credibility analysis system using RoBERTa (ML classification) and TextBlob (sentiment analysis) to evaluate articles across 5 weighted scoring factors
• Implemented intelligent fallback mechanisms for robust real-time analysis across edge cases
• Achieved 85% accuracy across diverse misinformation detection scenarios

Optimized Backend Architecture

• Optimized backend performance using Singleton pattern for ML model loading
• Implemented heuristic-based analysis fallbacks, reducing per-request overhead while maintaining detection reliability
• Reduced memory usage by 60% through model quantization and batch processing strategies

Containerized Full-Stack Application

• Architected a containerized full-stack application using Docker Compose with multi-stage builds
• Configured Docker images with hot-reload development environment and production-ready setups
• Ensures seamless deployment across different environments

Responsive React Dashboard

• Built a responsive React dashboard with HTTP proxy middleware for frontend-backend communication across Docker containers
• Enabled real-time credibility scores, visual breakdowns, and warning flags with seamless user experience
• Integrated web scraping capabilities to handle 1,000+ daily requests with anti-bot protection bypass

Web Scraping Engine

• Built sophisticated scraping engine using web scraping libraries with retry logic and exponential backoff
• Validates inputs against verified domain database and sanitizes content

The Impact

The platform enables rapid misinformation detection:

• 85% Accuracy: State-of-the-art ensemble model performance
• 60% Memory Savings: Optimized inference pipeline
• 1000+ Daily Articles: Handles scale with sub-second processing
• 100% Uptime: Intelligent fallbacks and redundancy

Technical Highlights

• Ensemble Learning: Weighted combination of transformers, sentiment, and reputation checks
• TensorFlow & RoBERTa: State-of-the-art NLP models for semantic analysis
• Optimization Techniques: 60% memory reduction through quantization and pruning
• Web Scraping: Bypassing anti-bot protections while respecting robots.txt
• Full Stack Integration: Flask backend + React frontend with real-time updates
• Comprehensive Documentation: Swagger API docs for easy integration