Methodology

Overview

Composite score from three data sources, processed through federated ML pipeline, updates continuously based on live network measurements.

When a government blocks a new service, our data reflects the change within hours.

Data Sources

ML Model

Gradient boosting classifier trained on 37K labeled censorship incidents. Federated learning across 16 nodes ensures no raw user data leaves local systems.

Scoring System

0-100 scale. 0 = complete freedom. 100 = total censorship.

Limitations

• ⚠Scores are national averages — regional variations not captured
• ⚠VPN detection underreported in highly restricted environments
• ⚠Sample sizes vary by country — affects confidence levels
• ⚠Real-time events may take up to 24h to reflect in scores
• ⚠Content filtering and throttling harder to detect than blocking
• ⚠Self-censorship and legal restrictions not measured

Confidence Intervals

Each country score includes a confidence interval reflecting measurement certainty. Wider intervals indicate less data or greater variability.

Validation

Scores are validated against external benchmarks and known censorship events. Continuous evaluation ensures model accuracy over time.

Update Pipeline

Citation

Use this data in research? Please cite:

@misc{voidly_censorship_index,
  author = {Voidly Research},
  title = {Global Censorship Index},
  year = {2026},
  url = {https://voidly.ai/censorship-index}
}

License: CC BY 4.0 — Free to use with attribution

Data Access