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What Does The World Think?

Inspiration

The inspiration for "What Does The World Think?" came from the realization that in our increasingly connected yet polarized world, the same claim can be interpreted vastly differently across countries and cultures. We wanted to create a tool that could visualize these global differences in perspective, highlighting how cultural context shapes truth and consensus.

What it does

"What Does The World Think?" is an AI-powered global opinion analyzer that takes any claim or statement and provides a detailed breakdown of how it would likely be received across 192 countries worldwide. The application:

Analyzes claims against cultural, political, and social contexts of different nations
Calculates a "Global Truth Index" indicating how universally agreed-upon a claim is
Visualizes agreement levels on an interactive world map
Provides country-by-country breakdowns with cultural context
Offers insights into why different regions might agree or disagree with the claim

How we built it

The application was built using a modern tech stack:

React with TypeScript for the frontend framework
Tailwind CSS and shadcn-ui for responsive, elegant UI components
Perplexity AI API for sophisticated global perspective analysis
Leaflet for interactive map visualization
Custom data processing workflows to transform AI-generated responses into meaningful visualizations

Challenges we ran into

Ensuring balanced representation of all global perspectives
Accurately mapping country names to geographical data on the map
Optimizing the prompts to Perplexity AI to produce consistent, well-structured responses
Designing an intuitive UI that could convey complex geopolitical nuance
Handling the significant computational demands of analyzing 192 countries simultaneously

Accomplishments that we're proud of

Creating an accessible tool that helps people understand the subjectivity of "truth"
Developing a responsive map visualization that works across devices
Building a robust data processing pipeline that handles AI-generated responses reliably
Designing an elegant user experience that makes complex global data approachable
Achieving an intuitive way to represent agreement levels visually and statistically

What we learned

The intricacies of working with large language models for structured data generation
Techniques for visualizing geographical data effectively
The complexity of representing global perspectives fairly and accurately
How to optimize prompts to guide AI toward consistent output formats
The importance of data validation when working with AI-generated content