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Datasets:
emperor-mew
/
ooni-censorship-historical

Tasks:
Tabular Classification
Time Series Forecasting
Modalities:
Tabular
Text
Formats:
parquet
Languages:
English
Size:
1M - 10M
Tags:
censorship
internet-freedom
ooni
human-rights
network-measurement
geopolitics
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
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metadata 
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 Global Censorship Index
size_categories:
  - 1M<n<10M

Voidly Global Censorship Index

The most comprehensive open dataset for internet censorship research and ML.

Dataset Description

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

  • 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

Files

File Description Rows
ooni-historical.parquet Daily measurements by country/test 1.6M
censorship-incidents.parquet Labeled anomaly spikes 37K
known-events.json Major censorship events 25+

Usage 

from datasets import load_dataset

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

# Load labeled incidents (for ML training)
incidents = load_dataset("emperor-mew/global-censorship-index", data_files="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...

Model

We provide a trained GradientBoosting classifier:

  • F1 Score: 99.8%
  • ROC AUC: 1.000
  • Available via API: https://api.voidly.ai/hydra/v1/detect

Citation 

@dataset{voidly_censorship_index_2026,
  author = {Voidly Research},
  title = {Global Censorship Index: 10 Years of Internet Measurement Data},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/emperor-mew/global-censorship-index}
}

Links

License

CC BY 4.0 - Attribution required