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country
string
date
string
anomaly_rate
float64
measurement_count
int64
spike_magnitude
float64
label
int64
event
string
confidence
float64
AE
2018-01-03
0.358209
67
2.259626
0
null
0.7
AE
2018-11-09
0.303797
79
1.724287
0
null
0.7
AE
2019-04-21
0.294118
51
1.62905
0
null
0.7
AE
2019-05-23
0.315789
57
1.842273
0
null
0.7
AE
2019-09-14
0.353846
65
2.216702
0
null
0.7
AE
2021-01-25
0.31746
126
1.858712
0
null
0.7
AE
2021-03-17
0.293103
58
1.619071
0
null
0.7
AE
2021-10-06
0.352941
51
2.207798
0
null
0.7
AE
2022-04-18
0.285714
224
1.546371
0
null
0.7
AE
2022-05-01
0.373333
75
2.408431
0
null
0.7
AE
2022-08-08
0.373984
123
2.41483
0
null
0.7
AE
2022-09-26
0.412261
2,610
2.791424
0
null
0.7
AE
2022-10-26
0.294927
1,163
1.637012
0
null
0.7
AE
2023-01-11
0.351542
1,135
2.19403
0
null
0.7
AE
2023-01-25
0.322388
335
1.907194
0
null
0.7
AE
2023-02-05
0.346405
612
2.143493
0
null
0.7
AE
2023-03-03
0.296296
648
1.650485
0
null
0.7
AE
2023-05-30
0.412
500
2.788861
0
null
0.7
AE
2023-05-31
0.37375
800
2.41253
0
null
0.7
AE
2023-06-02
0.31625
800
1.846804
0
null
0.7
AE
2023-06-14
0.285714
700
1.546371
0
null
0.7
AE
2023-06-15
0.33125
800
1.994385
0
null
0.7
AE
2023-07-31
0.94
50
7.983704
0
null
0.7
AE
2023-08-01
0.945455
55
8.03737
0
null
0.7
AE
2023-08-02
0.933333
60
7.918113
0
null
0.7
AE
2023-08-09
0.342039
2,795
2.100538
0
null
0.7
AE
2023-08-21
0.307619
2,100
1.761886
0
null
0.7
AE
2023-08-27
0.320423
3,873
1.887865
0
null
0.7
AE
2023-08-30
0.938462
65
7.968568
0
null
0.7
AE
2023-08-31
0.86
50
7.196607
0
null
0.7
AE
2023-09-04
0.8
60
6.606284
0
null
0.7
AE
2023-09-16
0.317446
2,224
1.858571
0
null
0.7
AE
2023-09-26
0.367656
4,749
2.352576
0
null
0.7
AE
2023-09-27
0.92
50
7.78693
0
null
0.7
AE
2023-10-11
0.289716
2,820
1.585746
0
null
0.7
AE
2023-10-20
0.419355
62
2.861223
0
null
0.7
AE
2023-10-21
0.381818
55
2.491911
0
null
0.7
AE
2023-10-21
0.375
56
2.424828
0
null
0.7
AE
2023-10-22
0.522727
88
3.878275
0
null
0.7
AE
2023-10-22
0.318182
88
1.86581
0
null
0.7
AE
2023-10-24
0.352941
51
2.207798
0
null
0.7
AE
2023-10-24
0.384615
52
2.519432
0
null
0.7
AE
2023-10-25
0.462687
67
3.287552
0
null
0.7
AE
2023-10-25
0.470588
68
3.365294
0
null
0.7
AE
2023-10-26
0.433333
60
2.998754
0
null
0.7
AE
2023-11-15
0.365306
1,470
2.329453
0
null
0.7
AE
2023-11-17
0.304682
1,303
1.732985
0
null
0.7
AE
2023-11-18
0.361111
1,152
2.28818
0
null
0.7
AE
2023-11-29
0.325548
3,511
1.938287
0
null
0.7
AE
2023-11-29
0.90566
53
7.645846
0
null
0.7
AE
2023-12-05
0.2861
741
1.550165
0
null
0.7
AE
2023-12-15
0.901961
51
7.609447
0
null
0.7
AE
2023-12-15
0.444444
54
3.108073
0
null
0.7
AE
2023-12-16
0.281637
3,469
1.50626
0
null
0.7
AE
2023-12-16
0.56
50
4.244991
0
null
0.7
AE
2023-12-18
0.846154
52
7.060378
0
null
0.7
AE
2023-12-18
0.37037
54
2.379279
0
null
0.7
AE
2023-12-19
0.888889
54
7.480836
0
null
0.7
AE
2023-12-19
0.574074
54
4.383462
0
null
0.7
AE
2023-12-20
0.779661
59
6.406174
0
null
0.7
AE
2023-12-20
0.807018
57
6.675327
0
null
0.7
AE
2024-02-10
0.328687
2,224
1.969168
0
null
0.7
AE
2024-02-27
0.282566
2,042
1.515397
0
null
0.7
AE
2024-03-10
0.335851
2,516
2.039648
0
null
0.7
AE
2024-03-15
0.30732
1,653
1.758944
0
null
0.7
AE
2024-03-25
0.314286
1,750
1.827478
0
null
0.7
AE
2024-03-30
0.288644
1,902
1.575192
0
null
0.7
AE
2024-04-02
0.440781
819
3.072034
0
null
0.7
AE
2024-04-18
0.33625
1,600
2.043578
0
null
0.7
AE
2024-04-19
0.351268
1,301
2.191339
0
null
0.7
AE
2024-04-27
0.320723
2,987
1.890814
0
null
0.7
AE
2024-04-28
0.349125
1,372
2.170255
0
null
0.7
AE
2024-05-01
0.360075
2,655
2.277989
0
null
0.7
AE
2024-05-02
0.368273
3,549
2.358641
0
null
0.7
AE
2024-05-07
0.334405
1,244
2.025427
0
null
0.7
AE
2024-05-18
0.471698
53
3.376214
0
null
0.7
AE
2024-05-18
0.377358
53
2.448033
0
null
0.7
AE
2024-05-20
0.313502
1,933
1.81977
0
null
0.7
AE
2024-05-21
0.288889
2,385
1.577606
0
null
0.7
AE
2024-05-22
0.290245
1,671
1.590951
0
null
0.7
AE
2024-05-29
0.381776
1,734
2.491498
0
null
0.7
AE
2024-06-01
0.294831
1,896
1.63607
0
null
0.7
AE
2024-06-04
0.34
1,400
2.080473
0
null
0.7
AE
2024-06-12
0.851852
54
7.116439
0
null
0.7
AE
2024-06-24
0.355956
3,551
2.237461
0
null
0.7
AE
2024-06-25
0.320204
2,158
1.885705
0
null
0.7
AE
2024-07-05
0.285375
4,205
1.543029
0
null
0.7
AE
2024-07-08
0.627451
51
4.908623
0
null
0.7
AE
2024-07-18
0.839286
56
6.992805
0
null
0.7
AE
2024-07-31
0.304622
3,808
1.732398
0
null
0.7
AE
2024-08-03
0.317988
2,107
1.8639
0
null
0.7
AE
2024-08-11
0.404605
608
2.716106
0
null
0.7
AE
2024-08-12
0.290105
1,334
1.58957
0
null
0.7
AE
2024-08-20
0.287583
4,816
1.564758
0
null
0.7
AE
2024-09-05
0.457627
59
3.237773
0
null
0.7
AE
2024-09-05
0.5
62
3.654668
0
null
0.7
AE
2025-07-19
0.335476
778
2.035959
0
null
0.7
AE
2025-10-14
0.792453
53
6.532029
0
null
0.7
AE
2025-11-18
0.538462
52
4.03308
0
null
0.7
AE
2025-12-21
0.288462
52
1.573401
0
null
0.7
End of preview. Expand in Data Studio

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

  • ๐Ÿ‡ฎ๐Ÿ‡ท Iran Mahsa Amini protests (2022)
  • ๐Ÿ‡ฒ๐Ÿ‡ฒ Myanmar military coup (2021)
  • ๐Ÿ‡ง๐Ÿ‡พ Belarus election shutdown (2020)
  • ๐Ÿ‡ท๐Ÿ‡บ Russia Ukraine invasion blocks (2022+)
  • ๐Ÿ‡ฐ๐Ÿ‡ฟ Kazakhstan January protests (2022)
  • ๐Ÿ‡ธ๐Ÿ‡ฉ Sudan military coup (2021)
  • ๐Ÿ‡จ๐Ÿ‡บ Cuba July protests (2021)
  • ๐Ÿ‡บ๐Ÿ‡ฌ 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

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