About Voidly
Structured censorship intelligence for researchers, journalists, and human rights organizations
Mission
An estimated 4.2 billion people live in countries where internet freedom is restricted (Freedom House, Freedom on the Net 2024). Traditional monitoring detects blocks hours or days after they happen — too late for journalists covering breaking events, human rights defenders documenting abuses, or people trying to access critical information.
Voidly makes censorship measurable, verifiable, and actionable — in near real-time.
We correlate data from multiple measurement networks into verified, ML-classified incidents with human-readable IDs, evidence chains, and confidence scores that researchers and journalists can cite.
Who This Serves
- •Journalists verifying censorship claims with citable evidence
- •Human rights defenders documenting internet shutdowns
- •Researchers studying internet freedom trends across countries
- •Developers building circumvention and internet-freedom tools
- •Civil society organizations monitoring government censorship
- •People in repressive information environments who need accurate, timely data
How We Know the Need
During the 2022 Mahsa Amini protests and subsequent unrest, Iran imposed multiple nationwide internet shutdowns. Our incident database documents hundreds of Iran-related censorship events, including WhatsApp, Instagram, and Signal blocking.
Since the 2021 military coup, Myanmar has maintained blocks on Facebook, Twitter, and Wikipedia. Researchers tracking the situation need verified, timestamped evidence — not anecdotal reports.
Russia's TSPU (technical means of countering threats) has expanded from blocking individual domains to throttling entire protocols. Our probe network detects VPN blocking, protocol throttling, and DNS poisoning patterns that evolve weekly.
When journalists cover a breaking shutdown, they cite OONI raw measurements or anecdotal user reports. There's no single source providing classified, citable incidents with evidence chains and confidence scores — that's the gap Voidly fills.
How We Engage Users
Community probe operators report blocking patterns from their networks. A leaderboard and trust scoring system incentivizes sustained contribution. Contributors in high-censorship countries provide ground-truth signals that validate our ML classifier.
Citable incident IDs (e.g. IR-2026-0142) were designed after feedback from researchers who needed stable references for academic citation. MCP server integration lets journalists query censorship data directly from their AI tools.
All incident data is CC BY 4.0. When external researchers find discrepancies, they can file corrections through our API or GitHub. This creates a self-correcting loop independent of our team.
Structured user interviews with journalists and human rights defenders in target regions. Advisory relationships with civil society organizations. These are planned deliverables for the next funding cycle.
Why This Exists
Existing measurement projects are essential but leave a gap:
- —OONI generates raw probe measurements — doesn't classify incidents
- —CensoredPlanet runs remote DNS/HTTP tests — no real-time alerting
- —IODA tracks network outages — not targeted domain/platform blocking
Voidly bridges this gap: we correlate data from all three (plus our own 55 node probe network) into verified, ML-classified incidents with human-readable IDs (like IR-2026-0142), evidence chains, and confidence scores.
What We Provide
- •55 globally distributed probe nodes generating proprietary measurement data
- •317 verified censorship incidents with evidence chains
- •ML classifier (99.8% F1, internal validation) trained on 37K labeled samples
- •Shutdown forecast model (74.6% AUC, internal validation) — 7-day risk predictions across 126+ countries
- •Multi-source correlation: our probe network + OONI + CensoredPlanet + IODA
- •Real-time VPN accessibility testing across major providers
- •10-year historical archive (1.6M records, 126+ countries)
Research & Open Data
Live Stats
Data as of March 2026
Technical
- • Censorship classifier: 99.8% F1 (GradientBoosting, internal eval)
- • Shutdown forecast: 74.6% AUC (XGBoost, internal eval)
- • <50ms inference time
- • Privacy-preserving: trained on aggregate data only
- • 55 nodes, 6 continents
- • 99.8% uptime (self-healing)
- • WireGuard + Stealth mode
- • Zero manual intervention
Tools for Users in Censored Regions
Tools are free. Your anonymous usage data contributes to censorship measurement.
Transparency & Verification
Operator & Capacity
Dillon Parkes
Founder & Lead Engineer
Built Voidly from the ground up — infrastructure, ML pipeline, probe network, and data ingestion. Also serves as CEO of Nexcom Media Group and as a board director of the Warriors Fund. Elected public official as Director of Montgomery County MUD No. 238 in Texas.
LinkedIn ↗Expanded measurement coverage in underserved regions (Sub-Saharan Africa, Central Asia, SE Asia). Beneficiaries: researchers and civil society. Metric: coverage in 80+ countries, ≤5min detection latency.
Sub-30-minute verified incident reports with evidence chains. Beneficiaries: journalists covering breaking censorship events. Metric: median detection-to-published < 30 minutes.
Desktop probe app + leaderboard + measurement contribution. Beneficiaries: activists and researchers contributing local measurements. Target metric: 500+ contributors across 50+ countries within 12 months of launch.
Explore the data. Verify the methodology.
All censorship data is open. All tools are free.