How the LOVE Index works. Live data. Verify everything yourself.
The LOVE Index is a weighted composite score ranging from 0 to 1000. It is computed entirely on-chain by the SentimentOracle smart contract using this formula:
LOVE = Σ (Scorei × Weighti) / 10,000
Each of the 6 subindices has a score between 0 and 1000 (where 500 = neutral, 0 = extremely negative, 1000 = extremely positive). Weights are expressed in basis points and sum to 10,000.
Subindices 0–4 are updated by the oracle pipeline every 6 hours, pulling real-world data from news APIs and running sentiment analysis. Subindex 5 (Good Spend) updates automatically on-chain when LefCoin is transferred to verified good destinations.
Armed conflicts, peace treaties, diplomatic progress, and global security sentiment.
Global donation volumes, humanitarian aid activity, and charitable sentiment across news and community discourse.
Overall tone of public discourse across social media, news, and happiness research.
Environmental progress: air quality, carbon intensity, biodiversity (including pollinator health 🐝), deforestation, and conservation sentiment.
Public health outcomes, disease burden, life expectancy, maternal health, and community wellbeing indicators.
On-chain activity: LefCoin transfers to verified good destinations in the GoodSpend Registry.
updateGoodSpend()All smart contracts are deployed on Base Sepolia and readable on BaseScan. The LOVE Index calculation is entirely on-chain and deterministic.
Read Oracle Contract ↗Anyone can call getLoveIndex() on the oracle contract directly. No API key needed. No middleman. Just Ethereum.
The oracle pipeline pulls from 18 open data sources — most require zero API keys. Clone the repo and run it yourself to verify our sentiment analysis.
View Source Code ↗We believe the biggest risk in "positive impact" crypto is love-washing — making vague claims about doing good without the infrastructure to back them up. Here's what we want to be clear about:
We publish everything: source code, contract addresses, methodology, data sources, and known limitations. If you find something we've missed, open an issue.