AI Search Citation Drift: Test Web and App Separately
Measure web/app citation drift with paired prompts, repeated runs, preserved URLs, overlap distributions, answer-use labels, change control, and a reusable observation ledger.
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Measure web/app citation drift with paired prompts, repeated runs, preserved URLs, overlap distributions, answer-use labels, change control, and a reusable observation ledger.
A 2026 U.S. desktop study separates ChatGPT’s session-level outbound-click rate from an estimated 9.4% reduction in traditional search use.
Three prominent 2026 findings use different samples, treatments and outcomes. Here is the denominator-safe comparison.
Use date-filtered search to find candidates, then inspect snapshots, page updates, related modules, metadata conflicts and leakage severity before admitting evidence.
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.
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.
We measured compressed response size, decoded HTML, server-extractable text and rendered text across 25 public sites. The results show why page weight alone is not a crawlability verdict.
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