| --- |
| 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 |
| |
