---
license: cc-by-4.0
task_categories:
  - tabular-classification
  - time-series-forecasting
language:
  - en
tags:
  - censorship
  - internet-freedom
  - ooni
  - human-rights
  - network-measurement
  - geopolitics
pretty_name: Voidly OONI Censorship Historical
size_categories:
  - 1M<n<10M
---

# Voidly OONI Censorship Historical

**A 10-year open archive for internet censorship research and ML.**

## Dataset Description

This dataset contains 10 years of global internet censorship measurements
from 120+ countries:

- **1.6M+ daily measurements** (2017-2026)
- **37K detected anomaly spikes**
- **4.5K confirmed censorship events** with labels
- **25+ known major incidents** (Mahsa Amini protests, Myanmar coup, etc.)

### Data Sources
- Primary: [OONI (Open Observatory of Network Interference)](https://ooni.org)
- Secondary: Voidly Research analysis and labeling

## Files

| File | Description | Rows |
|------|-------------|------|
| `data/ooni-historical.parquet` | Daily measurements by country/test | 1.6M |
| `data/censorship-incidents.parquet` | Labeled anomaly spikes | 37K |

## Usage

```python
from datasets import load_dataset

# Load historical measurements
ds = load_dataset("emperor-mew/ooni-censorship-historical",
                  data_files="data/ooni-historical.parquet")

# Load labeled incidents (for ML training)
incidents = load_dataset("emperor-mew/ooni-censorship-historical",
                         data_files="data/censorship-incidents.parquet")
```

## Schema

### ooni-historical
| Column | Type | Description |
|--------|------|-------------|
| country | string | ISO 3166-1 alpha-2 country code |
| test_name | string | OONI test type (web_connectivity, telegram, whatsapp) |
| date | date | Measurement date |
| measurement_count | int | Total measurements |
| anomaly_count | int | Measurements showing anomalies |
| confirmed_count | int | Confirmed blocked |
| anomaly_rate | float | Fraction showing anomalies (0-1) |

### censorship-incidents
| Column | Type | Description |
|--------|------|-------------|
| country | string | ISO 3166-1 alpha-2 country code |
| date | date | Incident date |
| anomaly_rate | float | Measured anomaly rate |
| measurement_count | int | Sample size |
| spike_magnitude | float | Z-score above baseline |
| label | int | 1=confirmed censorship, 0=not |
| event | string | Matched known event (if any) |
| confidence | float | Label confidence (0-1) |

## Known Events Covered

- Iran Mahsa Amini protests (2022)
- Myanmar military coup (2021)
- Belarus election shutdown (2020)
- Russia Ukraine invasion blocks (2022+)
- Kazakhstan January protests (2022)
- Sudan military coup (2021)
- Cuba July protests (2021)
- Uganda election shutdown (2021)
- And 17+ more

## Voidly Atlas ML Stack (2026-05-21)

This historical archive is the long-horizon training substrate for the
**Voidly Atlas** ML stack. The production stack is documented in dedicated
HuggingFace model cards under [`emperor-mew`](https://huggingface.co/emperor-mew):

- **Classifier v3.3** ([`emperor-mew/voidly-classifier-v3.3`](https://huggingface.co/emperor-mew/voidly-classifier-v3.3)) — country-day censorship classifier, GradientBoosting, regime-similarity-weighted contagion features. Honest cross-country generalization: **leave-one-country-out median F1 0.87, mean F1 0.71**. The fitted `.pkl` + per-country thresholds ship in that repo.
- **Multi-horizon forecast** ([`emperor-mew/voidly-forecast-v1-multi-horizon`](https://huggingface.co/emperor-mew/voidly-forecast-v1-multi-horizon)) — 1d/7d/30d XGBoost + isotonic, LOCO AUC 0.91 / 0.88 / 0.84.
- **Unsupervised anomaly** ([`emperor-mew/voidly-anomaly-dbscan-v1`](https://huggingface.co/emperor-mew/voidly-anomaly-dbscan-v1)) — CenDTect-style DBSCAN second-opinion signal.
- 12 more model cards — search `emperor-mew/voidly-` on the Hub.

> **Note on the older "F1 99.8% / AUC 1.000" claim:** that figure was a
> stratified-random-split number on a now-superseded v2 model. It does not
> reflect cross-country generalization. The current honest metric is the
> **LOCO** (leave-one-country-out) F1 reported above — random splits inflate
> apparent accuracy because the model learns per-country base rates.

For a clean held-out evaluation task, use the companion benchmark
[`emperor-mew/voidly-bench-v1`](https://huggingface.co/datasets/emperor-mew/voidly-bench-v1).

## Citation

```bibtex
@dataset{voidly_ooni_historical_2026,
  author = {Voidly Research},
  title = {Voidly OONI Censorship Historical: 10 Years of Internet Measurement Data},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/emperor-mew/ooni-censorship-historical}
}
```

## Links

- [Voidly Censorship Index](https://voidly.ai/censorship-index)
- [Real-time API](https://api.voidly.ai/data/censorship-index.json)
- [MCP Server](https://www.npmjs.com/package/@voidly/mcp-server)
- [Voidly Atlas ML model cards](https://huggingface.co/emperor-mew)
- [OONI (source)](https://ooni.org)

## License

CC BY 4.0 - Attribution required