Methodology
How we measure global internet censorship
Overview
Composite score from multiple measurement networks, processed through our 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
OONI Measurements
Sensor Network
External Sources
Multi-Source Correlation
No single measurement network captures the full picture of internet censorship. OONI provides active probing but has geographic gaps. CensoredPlanet provides remote measurement but lacks ground truth. IODA detects outages but not selective blocking.
Voidly operates its own 37-node network across 6 continents — testing VPN accessibility and censorship patterns every 5 minutes — then correlates these proprietary measurements with three external measurement networks (OONI, CensoredPlanet, IODA) to produce verified incidents with evidence chains. This turns ambiguous network anomalies into structured, citable censorship intelligence.
ML Model
Gradient boosting classifier trained on 37K labeled censorship incidents. Privacy-preserving training on aggregate data only — no raw user data is used.
Censorship Classifier
Shutdown Forecast Model
Feature Importance (Classifier)
Importance values from GradientBoosting model trained on 37K labeled incidents.
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.
Scores shown are illustrative examples from a point-in-time snapshot. Live scores update continuously on the Censorship Index.
Validation
Scores are validated against external benchmarks and known censorship events. Continuous evaluation ensures model accuracy over time.
Metrics are from internal 5-fold cross-validation. No independent third-party evaluation has been conducted. Published tools and data are available for independent replication.
Update Pipeline
Citation
Use this data in research? Please cite:
APA Format
Voidly Research. (2026). Global Censorship Index. https://voidly.ai/censorship-indexBibTeX
@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