| ---
|
| license: cc-by-4.0
|
| task_categories:
|
| - tabular-classification
|
| - time-series-forecasting
|
| language:
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| - en
|
| tags:
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| - internet-censorship
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| - network-measurement
|
| - internet-freedom
|
| - ooni
|
| - ioda
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| - censoredplanet
|
| pretty_name: Voidly Benchmark v1
|
| size_categories:
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| - 10K<n<100K
|
| ---
|
|
|
| # Voidly Benchmark v1
|
|
|
| A public benchmark for the **internet-censorship-forecasting** task.
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| 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
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| 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
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| block rates (1/2/3/7/14d), rolling mean/std over 7/14/30d, GDELT protest
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| article counts, IODA outage alerts, OONI anomaly counts, election/protest
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| 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 |
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| | Recall | 0.89 |
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| | Optimal threshold | 0.23 |
|
| | Label-shuffle AUC | 0.53 (baseline sanity check) |
|
|
|
| **Prod Sentinel model**, same algorithm but with the full GDELT +
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| 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
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| GDELT + social-signals + probe metrics carry noise that the cleaner
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| 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,
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| tor, http_invalid_request_line, http_header_field_manipulation — rows
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| with `anomaly = 1`.
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| - **IODA**: ASN-level outage events with score above the IODA alert
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| threshold.
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| - **CensoredPlanet**: Satellite (DNS) and Hyperquack (HTTP/HTTPS) blocking
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| events with `blocked = True`.
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| - **Incident formation**: clustering of evidence by (country, week, domain
|
| family) with a z-score spike detector over 30-day baselines. Status
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| `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,
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| 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.
|
|
|
