Hype Lense

A hype intelligence layer for Polymarket that uses Reddit and news sentiment to quantify emotional buzz and identify markets where prices may be driven by hype rather than information.

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Hype Lense

Inspiration

Prediction markets like Polymarket are powerful tools for aggregating information, but prices aren’t driven by information alone. Social media and news cycles create hype, fear, and momentum that can distort market behavior, especially these volatile prediction markets. We wanted to make that invisible force measurable.

What it does

Hype Lense is a hype intelligence layer for Polymarket. It analyzes Reddit discussions and news articles related to markets, extracts sentiment and emotion, and converts them into a normalized Hype Score. This score helps identify markets where prices may be driven by emotional buzz rather than fundamentals. With this information, as well as historical data of the market, we are able to provide the user with detailed insights such as market prediction, P&L charts, and much more.

How we built it

We collected Reddit posts and news article snippets tied to market-related keywords. Using Hugging Face NLP models, we extracted sentiment and emotional intensity from each text. These signals were normalized across markets to generate hype scores, which we combined with historical market data from the Polymarket CLOB API and categories to support predictive analysis. The frontend presents this data in a Polymarket-style UI in react js with a backend in Python FastAPI.

Challenges we ran into

We faced strict rate limits and API access issues, especially with Twitter/X and live news ingestion. To adapt, we focused on building a robust, extensible pipeline using curated Reddit and news datasets that still demonstrated the full system’s capabilities.

Accomplishments that we're proud of

  • Designed a clear, interpretable hype scoring system using real-world data
  • Successfully integrated sentiment and emotion analysis into a unified metric
  • Built a clean, intuitive UI that contextualizes Polymarket markets
  • Turned limited data access into a strong proof of concept

What we learned

We learned how much market behavior is influenced by emotion and attention, not just information. We also gained experience building NLP-driven analytics under real-world constraints like rate limits, incomplete data, and time pressure.

What's next for Hype Lense

Next, we plan to expand real-time data ingestion, add Twitter/X signals, and introduce alerts for hype spikes. Long-term, Hype Lense could become a behavioral analytics layer across multiple prediction and financial markets.

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