---
license: cc-by-4.0
task_categories:
  - tabular-classification
  - time-series-forecasting
language:
  - en
tags:
  - internet-censorship
  - network-measurement
  - internet-freedom
  - ooni
  - ioda
  - censoredplanet
pretty_name: Voidly Benchmark v1
size_categories:
  - 10K<n<100K
---

# Voidly Benchmark v1

A public benchmark for the **internet-censorship-forecasting** task.
Labeled incidents, joined raw evidence, per-source reliability, and a
reproducibility script that lands the same F1 / AUC numbers the Voidly
Sentinel model publishes at `/v1/sentinel/accuracy`.

If your model claims to predict internet shutdowns, this is the dataset
to prove it on.

## What's in here

| File | Rows | Purpose |
|------|------|---------|
| `incidents.parquet` | 1,574 | Labeled censorship incidents with severity, type, duration, and citable readable IDs like `IR-2026-0142`. |
| `evidence.parquet` | 49,596 | Raw measurement signals from OONI (24,150), IODA (15,108), and CensoredPlanet (10,338) — country, ASN, domain, signal_type, observed_at, signal_value, source_url. |
| `incident_evidence.parquet` | 35,376 | Many-to-many links from evidence rows to incidents, with link_confidence and link_reason. |
| `source_reliability.json` | 3 | Per-source precision proxy (fraction of linked incidents that are high-confidence). |
| `reproduce.py` | — | End-to-end: loads data → builds features → trains a forecast → evaluates. Should land within ±1pp of the target numbers below. |

## The task

For each (country, date) pair, predict whether **at least one incident
will start within the following 7 days**. Features: lagged OONI-derived
block rates (1/2/3/7/14d), rolling mean/std over 7/14/30d, GDELT protest
article counts, IODA outage alerts, OONI anomaly counts, election/protest
proximity, historical incident density, and country risk tier.

Primary metrics: ROC AUC and F1 at the F1-optimal threshold.

**Targets on this released dataset** (what `reproduce.py` should hit
within ±2pp):

| Metric | Target |
|--------|--------|
| ROC AUC | 0.98 |
| F1 | 0.92 |
| Precision | 0.94 |
| Recall | 0.89 |
| Optimal threshold | 0.23 |
| Label-shuffle AUC | 0.53 (baseline sanity check) |

**Prod Sentinel model**, same algorithm but with the full GDELT +
social-signals feature set (not in this snapshot):

| Metric | Value |
|--------|-------|
| ROC AUC | 0.98 |
| F1 | 0.79 |
| Precision | 0.83 |
| Recall | 0.76 |
| Optimal threshold | 0.34 |

The benchmark F1 is *higher* than prod because the auxiliary features
GDELT + social-signals + probe metrics carry noise that the cleaner
OONI-only benchmark avoids. Published papers should state which feature
set they used. `reproduce.py` is deterministic; check seed + data SHA256
before claiming a delta.

## How the labels were built

- **OONI**: web_connectivity, signal, whatsapp, telegram, facebook_messenger,
  tor, http_invalid_request_line, http_header_field_manipulation — rows
  with `anomaly = 1`.
- **IODA**: ASN-level outage events with score above the IODA alert
  threshold.
- **CensoredPlanet**: Satellite (DNS) and Hyperquack (HTTP/HTTPS) blocking
  events with `blocked = True`.
- **Incident formation**: clustering of evidence by (country, week, domain
  family) with a z-score spike detector over 30-day baselines. Status
  `confirmed_censorship` = confidence ≥ 0.7 AND ≥ 2 sources agreeing.

Full pipeline: <https://github.com/voidly-ai/voidly/tree/main/scripts>
(scripts: `ingest-ooni-*.py`, `ingest-ioda.py`,
`ingest-censoredplanet-*.py`, `incident-labeler.py`, `dedup-incidents.py`).

## Per-source reliability

Reliability is measured as `confirmed_linked / linked_incidents` — the
fraction of incidents that a given source's evidence rows contribute to
that end up at confidence ≥ 0.7. Current values:

| Source | evidence rows | linked incidents | confirmed linked | precision proxy |
|--------|---------------|------------------|------------------|-----------------|
| IODA | 15,108 | 1,352 | 388 | 0.287 |
| CensoredPlanet | 10,338 | 559 | 122 | 0.218 |
| OONI | 24,150 | 931 | 34 | 0.037 |

IODA's high precision is an artifact of its alert-threshold cutoff —
IODA only emits when ASN-level reachability drops significantly, which
is rare and usually real. OONI's low precision reflects its breadth:
many measurements flag as anomalies for reasons unrelated to
censorship (test-server issues, client misconfig, transient outages).
Use these as instance weights in training if you want to down-weight
noisier sources.

## Known gaps (don't publish results that pretend these don't exist)

- **Positive base rate** is ~7%; splits need stratification or results
  are noise.
- **Temporal leakage**: splitting randomly inflates AUC. Use time-based
  splits (train on dates ≤ T, evaluate after T) for honest numbers.
- **Country effects dominate**: leave-country-out AUC drops from 0.98
  to ~0.55 with the reference features. The model is learning
  per-country base rates as much as temporal structure.
- **Ingestion gaps**: Apr 2026 has an OONI gap Mar 31 - Apr 10 on this
  dataset snapshot. Features computed over that window will have
  missing-as-zero artifacts.
- **Probes not yet in high-risk countries**: probe_metrics covers
  US/CA/DE/IN/FR/BR at this snapshot, not IR/CN/RU. Probe-derived
  features are zero in the published dataset.

## Licensing

Data: **CC BY 4.0** (attribution: "Voidly — https://voidly.ai").
Code in `reproduce.py`: **Apache 2.0**.

Derived sources retain their upstream licenses — OONI, IODA,
CensoredPlanet each publish their raw measurements under open terms;
this dataset aggregates and labels them. Upstream citations required
when publishing derived work:

- OONI: <https://ooni.org/> (MIT / CC BY-NC-SA for some aggregates)
- IODA: <https://ioda.inetintel.cc.gatech.edu/> (research use)
- CensoredPlanet: <https://censoredplanet.org/> (research use)

## Reproducibility

```bash
pip install pandas pyarrow scikit-learn xgboost
python reproduce.py

# Expected output (within ±2pp):
#   rows_used ≈ 14300
#   AUC      ≈ 0.98
#   F1       ≈ 0.92
#   precision ≈ 0.94
#   recall   ≈ 0.89
#   threshold ≈ 0.23
#   within_tolerance: True
```

Run time: ~30 minutes on 6 CPU cores. Memory: <2 GB.

Artifact hashes and seeds:

- `incidents.parquet` — sha256 will be stamped on HF upload
- `evidence.parquet` — sha256 will be stamped on HF upload
- `np.random.seed(42)` used throughout `reproduce.py`

## Citation

```bibtex
@dataset{voidly_bench_v1_2026,
  author    = {Voidly},
  title     = {Voidly Benchmark v1: Labeled Internet Censorship Incidents
               with Joined Evidence},
  year      = {2026},
  publisher = {HuggingFace},
  license   = {CC BY 4.0},
  url       = {https://huggingface.co/datasets/emperor-mew/voidly-bench-v1}
}
```

Companion paper in preparation — this card will be updated with the
NeurIPS Datasets & Benchmarks submission link.

## Changelog

- **v1** (2026-04-17): initial release. 1,574 incidents, 49,596 evidence
  rows, 3 sources. Reference model: GradientBoosting, AUC 0.98 / F1 0.79.