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What sources get cited by AI search engines, measured on 602 controlled prompts.

The only public academic taxonomy of AI citations breaks 21,143 of them down by source type, by engine, and by how much of the answer each one shapes. The third cut is the one nobody quotes.

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The short answer

Sources the paper labels official are the largest single category of AI citation on every engine measured, at 34.22% for ChatGPT, 46.35% for Google's AI surfaces and 44.07% for Perplexity, followed by news and vertical publications. Those three types cover 79–88% of all citations in a 602-prompt academic dataset of 21,143 search-layer citations and 18,151 fetched pages.1

Key takeaways
  • Official sources, news and vertical publications carry the list. Forums and encyclopedias are not the bulk of it at volume.
  • A citation is not one unit across engines: mean influence per fetched page runs 0.2713 on ChatGPT against 0.0584 and 0.0646 elsewhere.
  • The most-cited type is not the most absorbed type: news media scores lowest of the seven domain types measured, encyclopedia sources highest.
The taxonomy

What types of source do AI engines actually cite?

AI engines cite official sources more than any other source type, then news, then vertical publications; those three are 79–88% of all citations in the only public academic taxonomy of the question.

602 controlled prompts, 21,143 search-layer citations, 23,745 citation-level records, 18,151 fetched pages, 72 features. An April 2026 preprint with an open dataset.1

Share of citations by source typeChatGPTGoogle (AIO + Gemini)Perplexity
Official34.22%46.35%44.07%
News31.17%18.99%16.07%
Vertical / niche22.13%22.00%18.99%
Those three combined87.52%87.34%79.12%
Mean citations per response6.8812.0616.35
Mean influence per fetched page0.27130.05840.0646

The first row is the one that changes what a founder should do on Monday. Official sources are not a marginal category in AI answers; they are the largest one on all three platforms measured. The paper labels that column official without defining it, and its limitations call the source-type taxonomy noisy. The widely repeated claim that AI answers are mostly forums and encyclopedias is not supported at volume here, though one high-traffic discussion platform can still dominate a specific question.

The second thing the table shows is that engines have distinct diets. News is 31.17% of ChatGPT's citations and 16.07% of Perplexity's, nearly a two-fold difference in one measurement window. If your category is covered heavily in trade press, one engine is structurally friendlier before you write a word.

Units

How many sources does one answer cite, and does that matter?

The count varies by more than a factor of two between engines, 6.88 citations per response for ChatGPT, 12.06 for Google's AI surfaces and 16.35 for Perplexity, and the count is the less important half of the finding.1 The more important half is mean influence per fetched page: 0.2713 for ChatGPT against 0.0584 for Google and 0.0646 for Perplexity, roughly a four-fold difference in how much of the finished answer a single source accounts for.

A citation is therefore not a fixed-value unit across engines, and any report that adds citations from different engines into one total is adding quantities with different meanings. Being one of seven sources on an engine that leans hard on each one is a materially different outcome from being one of sixteen on an engine that samples widely and shallowly. Set the target per engine, and say which outcome you are aiming for.

This also reframes what a low citation count means. An engine citing fewer sources is not being stingy, it is concentrating, which makes it higher variance for a brand: you are either substantially inside the answer or absent from it. The wide-sampling engine offers thinner, more frequent appearances. Neither is better in general.

Selection vs absorption

What is the difference between being cited and shaping the answer?

Being cited means a link to your page is attached to the answer; shaping the answer means its text actually came from your page, and the April 2026 preprint separates these into two measured stages it calls citation selection and citation absorption.1 A page can be fetched, listed among the sources, and contribute almost nothing the reader reads. Influence per fetched page is the metric for the second stage, and the one that tracks commercial value.

A third, weaker outcome sits below both: appearing as a link the answer never names in prose. A small multi-platform study of 115 prompts and 3,981 domain appearances, published June 2026, classified roughly 62% of source appearances as links whose brand the answer text never mentioned.2 The sample is thin and the figure is directional at best, but the distinction it names is worth tracking separately: brand mentioned in the prose, brand linked only, brand absent.

Track those three outcomes separately and most confusion about AI visibility dissolves. Cited but nobody clicks is usually the link-only outcome; mentioned but not linked is a different problem with a different fix. A report that collapses all three into one presence percentage cannot tell you which one you have.

Influence by type

Which source types shape the answer, not just appear in it?

Scored by mean influence rather than by share, the ranking inverts: encyclopedia sources come first, news media last of the seven domain types reported.

Not the ones cited most often. The same dataset also scores mean influence by domain type, and that ranking does not match the share table above: encyclopedia sources average 0.2144, commercial sources 0.1028, news media 0.0726 across 1,546 citations, last of the seven types reported.1 News is 31.17% of ChatGPT's citations and near the bottom for how much of the answer it accounts for, which the authors read as engines reaching for explanatory pages after timely ones.

A second cut scores the role a passage plays rather than the kind of site it sits on. Definition passages average 0.1531 and comparison passages 0.1524, against 0.0801 for background and 0.0529 for reference-only citations, roughly a third of a definition. What a page is for inside the answer matters more than what hosts it: a commercial page that supplies a clean definition is doing the job an encyclopedia usually does.

Correlates

Which page features show up alongside higher citation influence?

Query relevance shows the strongest association with citation influence of any page feature measured, r = 0.4322, ahead of numbers, definitions, headings and list density.

The grid below gives descriptive associations from the same dataset, top quartile against bottom quartile. The authors reserve confirmatory analysis for future work.1

+61.6%

Pages containing numbers

Mean influence 0.1171 for pages carrying statistics against 0.0725 for those without. The 2026 critical survey attaches a condition: the criterion is not to add numbers but to provide relevant, verifiable, dated and properly attributed evidence.3

+57.3%

Pages with definition markers

Mean influence 0.1252 against 0.0795. Definition markers are among Google's strongest per-platform signals in this dataset, alongside answer-to-citation embedding similarity.

r = 0.43

Relevance, the strongest correlate

Machine-judged query relevance correlates with influence at r = 0.4322 across platforms, ahead of answer-citation embedding similarity at 0.3561 and content quality at 0.2917. It beats every formatting feature measured.

12.5×

Heading count

Top-influence-quartile pages carried 10.59 headings against 0.85 in the bottom quartile, and list density was 8.94× higher. The authors say plainly they cannot tell whether headings cause absorption or good pages simply have more.

−5.7%

Q&A page format

Q&A formatting was associated with slightly lower influence, 0.0947 against 0.1005, which the authors suggest may be confounding with low-quality FAQ pages rather than evidence against question-shaped writing.

Length, where the evidence conflicts

This dataset finds influence rising with length past 3,000 words. A commercial study of 174,048 cited pages, December 2025, found word count against citation position at Spearman 0.04, with 53.4% of citations going to pages under 1,000 words.4 Different outcome variables; treat length as neutral.

Read that grid in the order the numbers give you, not the order that flatters a content plan. Relevance leads, and the 2026 critical survey rates query-document relevance Strong in controlled settings, one of only two levers in its support table at that level.3 Verifiable numbers and flat definitions come next, with the survey's warning attached: adding a fabricated statistic may increase reuse while degrading epistemic quality. Headings and lists come last, are correlational, and earn their place by making the page usable. And the strongest published test of pushing any of this on purpose found that, out of 54 cases, only three produced statistically significant ranking improvements.11

The honest limit of this article

Every number in the grid above comes from one preprint whose authors call it descriptive statistics and reserve confirmatory analysis for future work; it is a single sampling window on three platforms, and none of the associations is causal. The shares also depend on the prompt mix: 602 controlled prompts are not a sample of what your buyers ask, and a category dominated by forum discussion will look nothing like this table. Use the shape and the ordering, not the decimals. One number below needs its own caveat: the 27.1% unscrapable figure measures whether a third-party researcher could fetch a cited URL, not whether the engine that cited it could.

Ownership

How much of an AI answer can you realistically own?

A minority of it on most commercial questions, even though official sources are the largest single category overall. The 34–46% official share is spread across every brand in the answer, competitors included, and the rest is news, vertical publications and discussion threads you do not control. A 4,706-query academic audit of Google AI Overviews, collected September 2025 in the US and Germany, found 53% of the domains it consults were absent from the organic top 10 and 27% from the top 100.5 A 55,393-query preprint collected between 13 March and 21 April 2026 measured the comparable first-page gap at 29.8%, so the defensible statement is a range of roughly 30% to 53% with both collection windows named, not a point from either study.8 An 11,500-query SIGIR 2026 study makes the point from another angle: for one query, the sources returned by Google organic search, AI Overviews and Gemini overlap at under 0.2 average Jaccard similarity.10 Kirsten counts domains consulted, not domains cited.

Two consequences follow. The first is that the pages worth writing are the ones that can be selected on their own merits: documentation that answers a real sub-question, a pricing page that states prices in text, a comparison page honest enough to be quotable. Google is blunt about what qualifies a page at all: it must be indexed and eligible to be shown in Search with a snippet, and there is no special structured data to add.6 Its optimization guide adds that these features are “rooted in our core Search ranking and quality systems”.9

The second is that being fetchable is a precondition, not a detail. A 2026 preprint, reported via the critical survey, found 27.1% of URLs already cited in AI answers could not be scraped, being inaccessible, removed or non-textual, and roughly 16% of the 19,154 pages retrieved were labelled AI-generated by its detector.7 A page behind a login, an interstitial or a bot-blocking gate is out of the running however well it is written, the subject of Technical GEO.

Where a product fits, and where it does not

The whole method is readable from the papers below, and the practical version is a spreadsheet: run your prompts, copy out the cited URLs, tag each one official, news, vertical or discussion, and count. Bavior does that collection step across five engines on a schedule, so the source mix for your own questions is visible rather than inferred from someone else's 602 prompts. It does not measure influence per page, which needs the answer and the source aligned, and it makes no claim about getting your pages into that list. The free AI visibility check and free GEO audit run without a paid plan; paid tracking starts from $99/mo billed monthly, or $79.17/mo billed annually (as of 30 Aug 2026).

Sources, all checked 30 Aug 2026
  1. Zhang, He, Yao, “From Citation Selection to Citation Absorption” (a measurement framework for GEO across AI search platforms), 28 Apr 2026, arXiv:2604.25707 (preprint, open dataset): arxiv.org/abs/2604.25707
  2. Multi-platform study, June 2026: 115 prompts, 3,981 domain appearances; roughly 62% of source appearances were links whose brand the answer never named. Small sample, vendor-published, described not linked.
  3. “Optimizing Visibility in Generative Engines: A Critical Survey of Generative Engine Optimization (2023–2026)”, 15 Jul 2026, arXiv:2607.14035 (survey preprint; Table 4 rates query-document relevance “Strong in controlled settings”): arxiv.org/abs/2607.14035
  4. Study of 560,346 AI Overviews resolving to 174,048 pages, December 2025: word count against citation position at Spearman 0.04, 53.4% of citations to pages under 1,000 words. Vendor-published, described not linked.
  5. Kirsten et al., “Characterizing Web Search in The Age of Generative AI”, Findings of ACL 2026; 4,706 queries, Sept 2025, US and Germany: “on average 53% (27%) of domains that AIO consults are not contained in top-10 (top-100) Organic search results”: aclanthology.org/2026.findings-acl.526
  6. Google Search Central, “AI features and your website”, last updated 10 Dec 2025: snippet eligibility, and “no special schema.org structured data that you need to add”: developers.google.com/search/docs/appearance/ai-features
  7. Allaham & Diakopoulos, 2026 preprint (reported in arXiv:2607.14035 §8.3): 27.1% of cited URLs could not be scraped; roughly 16% of 19,154 retrieved textual pages were labelled AI-generated
  8. Xu, Iqbal & Montgomery, “Measuring Google AI Overviews”, preprint, 2026; 55,393 queries, 13 March to 21 April 2026: “29.8% of AIO reference domains do not appear anywhere on the corresponding first page”: arxiv.org/abs/2605.14021
  9. Google Search Central, “Optimizing for generative AI features”, last updated 10 Jul 2026: “our generative AI features on Google Search are rooted in our core Search ranking and quality systems”: developers.google.com/search/docs/fundamentals/ai-optimization-guide
  10. Grossman et al., “How Generative AI Disrupts Search”, ACM SIGIR 2026, arXiv:2604.27790; 11,500 queries: sources are “substantially different for each search engine (<0.2 average Jaccard similarity)”: arxiv.org/abs/2604.27790
  11. Puerto et al., “C-SEO Bench: Does Conversational SEO Work?”, NeurIPS Datasets & Benchmarks 2025, arXiv:2506.11097 §6.2: “Out of 54 cases, we uncover only three where the ranking improvements are statistically significant”: arxiv.org/abs/2506.11097
FAQ

Frequently asked questions.

Do AI answers mostly cite forums and Wikipedia?

Not at volume, on the only public academic taxonomy of the question. In a 602-prompt dataset covering 21,143 search-layer citations, official sources were the largest single category on every platform measured: 34.22% for ChatGPT, 46.35% for Google's AI surfaces, 44.07% for Perplexity, with news and vertical publications making up most of the rest. A single discussion platform can still dominate one specific question, so the shape is more durable than the decimals.

Is one AI citation worth the same on every engine?

No, and the gap is roughly four-fold. In a 602-prompt academic dataset, mean influence per fetched page was 0.2713 for ChatGPT against 0.0584 for Google's AI surfaces and 0.0646 for Perplexity, while mean citations per response ran 6.88, 12.06 and 16.35 respectively. An engine that cites fewer sources leans harder on each one. Adding citation counts across engines into a single total therefore sums quantities with different meanings.

Which source types actually shape the answer the most?

Not the ones cited most often. Scored by mean influence per fetched page in the same dataset, encyclopedia sources average 0.2144 and news media 0.0726, last of the seven domain types reported, even though news is 31.17% of ChatGPT's citations. By the role a passage plays, definitions average 0.1531 and comparisons 0.1524, against 0.0529 for reference-only citations.

Should I add more headings and lists to get cited more?

Add them because they make the page usable, and promise nothing for citations. The association is real and large, with top-influence-quartile pages carrying 10.59 headings against 0.85 in the bottom quartile, but the authors of that dataset state directly that they cannot tell whether headings cause absorption or whether good pages simply have more of them. The 2026 critical survey rates document structure moderate and heterogeneous.

What is the difference between citation selection and citation absorption?

Citation selection is whether a link to your page is attached to the answer; citation absorption is how much of the answer's text your page actually accounts for. The April 2026 preprint that introduced the distinction measures the second as influence per fetched page, and the two come apart routinely: a page can be fetched, listed as a source, and contribute almost nothing readers see.

Bavior Editorial

The team that researches and maintains Bavior’s writing on Reddit marketing and AI search visibility. Every figure here is attributed to a named source with the date it was checked, and none of our links are affiliate links.

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