6 dated technical articles. These pages record the methods and claims as published; follow each article's source links and update notes for its current scope.
How Voidly uses uncertainty sampling, Cohen's kappa inter-annotator agreement, and weekly model retrains to grow its censorship anomaly training set from 127K bootstrap labels to 275K — 500 examples/week annotated by 3 researchers each, with DVC data versioning and PSI drift detection.
Machine learning and OSINT · Censorship and information control
How Voidly constructs a labeled training dataset for the anomaly classifier from 200M+ OONI measurements: weak supervision with Snorkel-style label functions across DNS/TCP/TLS/HTTP layers, class imbalance handling with SMOTE and log-weighting, time-based train/val/test splits to prevent leakage, per-country Platt scaling calibration, and the continuous retraining pipeline.
Censorship and information control · Machine learning and OSINT · Engineering and infrastructure
How the quality filter pipeline decides which raw measurements are fit for ML training: boolean checks for control_failure (1.9% drop rate — ISP blocks on control server IPs in CN/IR/RU), missing_fields (0.8%), old probe version pre-2.5.0 (0.3%), and duplicates (0.2%), totalling 3.2% dropped. Includes the quality_filter() Python function, the to_feature_input() schema transformation, and why rejected measurements go to quarantine not discard.
Censorship and information control · Machine learning and OSINT · Engineering and infrastructure
How Voidly normalizes 200M+ OONI measurements across five web_connectivity schema versions (v0.2 to v0.6) into a single ML-ready format: a detect_web_connectivity_version() function using field-presence inference, AnomalyType and ConfidenceTier enums, the OoniMeasurementNormalized dataclass, FLAG_* bitmask constants for DNS/TCP/TLS/HTTP anomaly encoding, side-by-side normalize_v05() vs. normalize_v06() implementations, and a 95.3% pass-through rate from the drop-reason table.
Censorship and information control · Engineering and infrastructure · Machine learning and OSINT
How we processed the OONI raw measurement archive into a flat ML-ready CSV: handling probe version schema drift across 12 years, normalizing test_keys across 20 measurement types, streaming 200M+ records, and what we decided to leave out.
Censorship and information control · Engineering and infrastructure
How Voidly attributes censorship infrastructure to specific DPI vendors using network signatures and open-source intelligence: a six-vendor signature table (TSPU/Sandvine/NetClean/Iran ARRS/Cisco IronPort/GFW), DpiVendorSignature dataclass with a score_signature_match() function weighting RST timing (0.35), block page (0.30), injection IP (0.25), and CA SPKI (0.10), procurement scraping with PROCUREMENT_SOURCES across five government tender portals, BGP TTL-hop attribution, and case studies for Russia, Iran, and Ethiopia.
Censorship and information control · Machine learning and OSINT · Engineering and infrastructure