The Credibility Compass

A news credibility analysis tool developed for Bath Hack 2025 that evaluates news articles for factual accuracy, political bias, source reliability, and sentiment.

🌟 Inspiration

In an era of information overload and increasing concerns about misinformation, we were inspired to create a tool that helps people navigate the complex media landscape. The Credibility Compass was born from our desire to empower readers with AI-assisted insights into the credibility and bias of the news they consume.

Our goal was to build a solution that:

• Promotes media literacy and critical thinking
• Provides transparent assessments of news content
• Helps users recognize political bias and sentiment in reporting
• Reduces the spread of misinformation by highlighting factual inaccuracies

🔍 What It Does

The Credibility Compass allows users to:

1. Enter any news article URL for comprehensive analysis
2. View factuality assessments with confidence scores, ratings, and supporting sources
3. Understand the reliability of the news source with detailed reasoning
4. Identify political leaning of both the article and its source on a spectrum from Far Left to Far Right
5. Analyze sentiment patterns throughout the article, with entity-specific sentiment highlighting

🛠️ How We Built It

This project is built with a modern web stack:

• Frontend: Next.js with the App Router, Tailwind CSS, and shadcn/ui components
• Backend: Next.js API routes handling content processing and analysis
• AI Integration:
  • Google's Gemini API for comprehensive content analysis
  • Perplexity API for search-based factual verification and source credibility assessment
• Data Processing: Custom algorithms for entity extraction, sentiment analysis, and bias detection

🧠 Challenges We Faced

Building The Credibility Compass came with several challenges:

• Content Extraction: Developing robust methods to extract clean article text from diverse news websites
• Bias Detection: Creating a nuanced system for identifying political bias without introducing our own biases
• Performance Optimization: Balancing comprehensive analysis with reasonable response times
• Source Credibility: Establishing reliable metrics to evaluate the trustworthiness of news sources
• UI/UX Design: Creating an intuitive interface that presents complex information clearly

🏆 Accomplishments

We're proud of creating:

• A fully functional tool that provides multi-faceted analysis of news content
• An intuitive user interface that makes complex information accessible
• A system that balances detailed analysis with user-friendly presentation
• Integration with advanced AI models to provide sophisticated insights

📚 What We Learned

Through this project, we gained expertise in:

• Prompt engineering for specialized AI analysis
• Content extraction techniques for web articles
• Political bias detection methodologies
• Sentiment analysis implementation
• Building responsive UIs for data-heavy applications
• Optimizing API calls to external AI services

🔮 What's Next

Future enhancements we're considering:

• Browser extension for instant analysis while browsing
• Expanded historical context for news sources
• Comparison feature to analyze multiple articles on the same topic
• Community feedback integration to improve analysis accuracy
• Mobile application development

🚀 Getting Started

Installation

# Clone the repository
git clone https://github.com/CDE90/BathHack2025.git
cd BathHack2025

# Install dependencies
pnpm install

Development

# Start the development server
pnpm dev

Building for Production

# Build the application
pnpm build

# Start the production server
pnpm start

🧪 Testing and Quality Assurance

# Run linting
pnpm lint

# Fix linting issues
pnpm lint:fix

# Run type checking
pnpm typecheck

# Check formatting
pnpm format:check

# Fix formatting
pnpm format:write

👥 Contributors

• Ethan Coward
• Tom Watts

📄 License

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