Charter
To document and expose fraud and social-engineering operations through lawful, transparent, open-source investigation, and to deliver verifiable, auditable findings to the public, to platform trust-and-safety teams, to regulators, and to law enforcement.
A distributed collective of human analysts augmented by AI tools, working under a published methodology with full evidentiary auditability, producing research that survives scrutiny in newsrooms, courtrooms, and regulatory hearings. The standard of success is not reach or engagement; it is that every claim MACSITNA publishes is traceable to preserved evidence and withstands independent verification.
Recruit and vet a core team of real, named (internally accountable) analysts. Volunteers contribute research labor, language skills, and domain expertise. They do not contribute their personal accounts or credentials, and no one acts under a false identity.
Collect only publicly available data and never engage in unauthorized access of private systems.
AI handles translation, summarization, entity resolution, deduplication, pattern detection, and lead triage at scale. A human analyst verifies every factual claim before it enters the evidentiary record. AI output is always labeled as machine-generated until human-reviewed.
All raw captures, source URLs, retrieval timestamps, and content hashes are committed to GitHub with a documented chain of custody, so every published claim is traceable and auditable by third parties.
Apply standard tradecraft: link analysis, infrastructure and domain mapping, financial-flow tracing where lawful, and formal confidence levels plus source-reliability ratings on every assessment.
Produce standardized intelligence briefs routed to the correct audience: platform trust-and-safety teams, consumer-protection regulators, law-enforcement referrals, and public reporting once legal review clears it. Briefs distinguish fact, assessment, and speculation.
Minimize bystander data, redact uninvolved third parties, apply a right-of-reply practice where safe, maintain a public corrections policy, and never publish accusations against named individuals without legal review.
Operational security exists to protect researchers from retaliation by the networks they investigate, never to disguise automated activity as human behavior.