AI Search Source Credibility: Build a 32-Field Audit Ledger
Adapt an EACL 2026 study into a claim-source audit that preserves sample, user role, credibility rules, groundedness, adjudication and limitations.
Topic tag · Research
Methods for measuring AI-search visibility, citations, referrals, outcomes, and uncertainty.
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Open an article for the complete method, evidence, limitations, and a focused reader discussion.
Adapt an EACL 2026 study into a claim-source audit that preserves sample, user role, credibility rules, groundedness, adjudication and limitations.
Audit what an AI answer actually uses from each cited source with a claim-passage ledger for contribution, fidelity, review, limits, and re-audit.
Match AI-visibility scores to referral sessions by page, platform, date, coverage and lag before calculating correlation or making a traffic claim.
Compare AI visibility tools by prompt provenance, execution method, evidence access, metric definitions, platform coverage, cadence, and decision fit.
A critical 2026 survey separates retrieval, citation, absorption and downstream behavior—and finds the evidence narrower than most GEO claims.
Separate intervention-aligned ChatGPT referral movement from platform growth with treated and control definitions, first-party evidence, placebo checks, organic guardrails, and a reusable ledger.
Use matched weekdays, one primary action, live-post checks, UTM discipline, and explicit confounders to compare Business Profile post and control windows without treating interaction counts as ranking proof.
Create a maintained AI-assistant referral segment in GA4, preserve the raw source evidence, and report click visits without presenting them as complete citation coverage.
This guide separates Capterra mentions, citations, SaaS recommendations, and measurable referrals, then gives you a 43-field ledger and browser-local evidence auditor.
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