Investigating Health Misinformation with Pattern Analysis
Bringing systematic review rigor and frontier AI to health misinformation detection. Track rumor propagation, synthesize evidence at scale, and deliver research-grade verdicts in real-time.
Pattern Recognition in Action
"Drinking hot water with lemon kills the new virus in 15 minutes"
"CDC recommends updated vaccines for those over 65 during flu season"
"New study links artificial sweeteners to increased infection risk"
How Sherlock Detects False Narratives
Ingest
Multi-source data collection from social platforms, news outlets, and messaging apps
Analyze
Pattern recognition using transformer models trained on health claim datasets
Cross-Reference
Verify against peer-reviewed sources, health authority databases, and fact-check registries
Visualize
Real-time dashboards showing spread patterns, origin clusters, and intervention opportunities
Evidence-Based Verification at Scale
The rigor of systematic reviews meets frontier AI
Traditional fact-checking relies on single-source verification. We bring the gold standard of scientific evidence synthesis — meta-analysis and systematic review protocols — directly into misinformation detection. Every claim is evaluated against the full body of available evidence, weighted by source quality and methodological strength.
Systematic Evidence Synthesis
PRISMA-aligned protocols scan thousands of sources per claim — peer-reviewed literature, health authority statements, clinical registries, and fact-check databases
Weighted Source Hierarchies
Evidence grading from randomized trials to observational studies to expert opinion. Each source contributes to confidence scores proportional to methodological quality
Frontier LLM Reasoning
Powered by the most advanced large language models for nuanced claim decomposition, context understanding, and synthesis of contradictory evidence into coherent verdicts
Continuous Evidence Updates
Living review methodology — verdicts update automatically as new evidence emerges, preprints get peer-reviewed, or consensus shifts
Built for Organizations Fighting Misinformation
News Agencies & Media
Real-time verification pipeline for health stories before publication. Prevent amplification of unverified claims.
Pharmaceutical Companies
Monitor narratives about drug safety, vaccine efficacy, and treatment protocols across patient communities.
Healthcare Systems
Identify emerging health myths in patient populations. Deploy targeted counter-messaging before misinformation spreads.
Public Health Agencies
Outbreak intelligence and infodemic response. Track how health guidance competes with false narratives.
Social Media Platforms
Content moderation intelligence. Prioritize review queues with AI-assisted claim classification.
Research Institutions
Study misinformation dynamics at scale. Access structured datasets for infodemic research.
Insurance & Risk
Assess population health risk from misinformation exposure. Model intervention ROI for wellness programs.
Fact-Check Organizations
Augment human reviewers with AI pre-screening. Prioritize claims by spread velocity and harm potential.
Building a Network of Verified Sources
Core
Graph Neural Networks for Rumor Propagation
Misinformation doesn't spread randomly — it follows network topology. Our GNN architecture learns propagation patterns across social graphs, identifying super-spreader nodes and predicting viral trajectories before they peak. Built on a native graph database infrastructure that models relationships between claims, sources, actors, and evidence in real-time.
Ready to Investigate?
Join the network of organizations protecting public health from misinformation.