We’ve all acquired used to the little numbered blue hyperlinks in ChatGPT’s responses. They’re the citations that again up ChatGPT’s responses with exterior info.
However, though ChatGPT crawls dozens of pages to reply a single question, in accordance with our analysis, it solely finally ends up citing ~50% of them.
Why does one web page get the credit score whereas one other, which the AI clearly retrieved, will get nothing?
In accordance with research by AI professional Dan Petrovic, when ChatGPT retrieves outcomes, each comes again with the web page title, a quick snippet or abstract, the URL, and an ID quantity.
ChatGPT makes use of this information to determine which pages are value opening and finally citing in its response.
Meaning there’s a gatekeeping layer earlier than ChatGPT opens and reads any of your precise web page content material. The title, snippet, and URL are doing the heavy lifting in that preliminary determination.
So we needed to know: what really influences that call? Does larger semantic similarity between a web page’s retrieval information and the person question improve quotation chance? Which fields matter most? Do human-readable URLs outperform opaque ones?
To search out out, we analyzed 1.4 million ChatGPT 5.2 prompts from February 2025 (desktop) with the assistance of Ahrefs information scientist Xibeijia Guan.
However earlier than we get into the findings, you should perceive how ChatGPT really gathers its sources—as a result of not all URLs enter the system the identical method.
When ChatGPT retrieves outcomes, it categorizes sources utilizing an inner discipline known as ref_type—primarily a label for the retrieval channel the URL got here by means of.
We found 5 classes: search, information, reddit, youtube, and academia.
The quotation charges between them are wildly uneven:
| ref_type | Quotation % | Whole information factors |
|---|---|---|
| search | 88.46% | 25,563,589 |
| information | 12.01% | 3,940,537 |
| 1.93% | 16,182,976 | |
| youtube | 0.51% | 953,693 |
| academia | 0.40% | 185,337 |
The overall “search” index dominates—each in quantity and quotation price—and 88% of the URLs that find yourself being cited by ChatGPT are taken straight from search.
If you wish to be cited by ChatGPT, you should be in that search choice pool—which suggests your content material must rank.
This isn’t new info. By now, most individuals are already conscious that rating performs a component, however it’s good to have some extra information to again it up.
Specialised verticals like YouTube (e.g. youtube.com) and Academia (e.g. arXiv.org), then again, are pulled in at scale however barely ever get surfaced as precise citations.
Sidenote.
The “search” ref_type does embody Reddit and YouTube outcomes too—any Reddit or YouTube web page that comes again by means of a typical internet search will present up there.
The separate “Reddit” and “YouTube” ref_types doubtless symbolize extra outcomes—i.e. these pulled in by way of devoted API integrations—on high of regardless of the internet search already returned.
That’s why the quantity on these channels is so excessive; ChatGPT is supplementing its search outcomes with a separate feed of Reddit and YouTube content material.
This issues quite a bit for deciphering the remainder of the evaluation.
On common, ChatGPT pulls ~16.57 cited URLs and ~16.58 non-cited URLs per immediate.
However as a result of Reddit makes up 67.8% of the non-cited pool, any mixture comparability of “cited vs. non-cited” is actually evaluating search outcomes to Reddit API output. Not apples to apples.
So all through this analysis, we’ve remoted the evaluation by ref_type wherever attainable to keep away from that distortion.
That is most likely probably the most hanging discovering within the dataset.
Reddit has its personal devoted ref_type in ChatGPT’s retrieval system, with over 16 million information factors in our dataset.
But it’s cited at a price of simply 1.93%.
In the meantime, 67.8% of all non-cited URLs come from Reddit.
In different phrases: ChatGPT is utilizing Reddit extensively to grasp subjects, gauge consensus, and construct context—however it virtually by no means offers Reddit the credit score.
It learns from the gang, then cites one other establishment.
As we’ve briefly lined, when ChatGPT retrieves search outcomes, each comes again with a set of fields together with a title, URL, and typically a snippet—a brief extract of web page content material saved in ChatGPT’s retrieval information.
We anticipated that having extra of those fields populated would correlate with larger quotation charges.
At first look, the combination information appeared to inform a unique story: non-cited pages even have extra populated fields in ChatGPT’s retrieval information than cited ones.
Non-cited URLs had snippets 14.81% of the time versus 4.36% for cited URLs, and had been way more more likely to carry a publication date (92.72% vs. 35.98%).
We virtually ran with that as a discovering, however I’m glad we didn’t.
After we dug into it, the discrepancy turned out to be virtually completely a compositional artifact—pushed by Reddit and the mechanics of ChatGPT’s retrieval pipeline.
As a result of the non-cited pool is overwhelmingly Reddit (67.8%), and Reddit content material pulled by way of API naturally carries pub_date metadata, the 92.72% determine is a Reddit artifact—not a sign about how ChatGPT evaluates internet pages typically.
The snippet hole is defined otherwise. In accordance with David McSweeney’s analysis on ChatGPT’s retrieval course of, the mannequin really abandons the snippet discipline (the quick content material extract) as soon as it’s determined to quote a URL, and opens the complete web page as an alternative.
So, it’s not a matter of ChatGPT preferring pages with no snippets. The low snippet share for cited pages is probably going a byproduct of how the pipeline works.
After we remoted the info to only the “search” ref_type—stripping out Reddit, information, YouTube, and the remaining—the image grew to become quite a bit clearer:
| Search ref_type | Has snippet | Has pub_date | Whole URLs |
|---|---|---|---|
| Cited | 2.52% | 33.79% | 22,612,529 |
| Not cited | 0.09% | 49.00% | 2,951,060 |
Snippet information is principally non-existent for each teams throughout the search vertical—it’s not a usable sign. And the publication date percentages are nearer, however non-cited search pages are nonetheless barely extra more likely to carry a pub_date (49%) than cited ones (33.79%).
The variations we initially noticed between cited and non-cited URLs appear to have been distorted by the info composition and retrieval mechanics. Any sign—if there may be one—is buried underneath the noise.
The trustworthy takeaway: we will’t draw sturdy conclusions about whether or not the snippet or publication date fields play a significant function in quotation from this information.
It’s value flagging that this downside doubtless applies to different quotation research too. Any analysis evaluating “cited vs. non-cited” URLs with out accounting for the place these URLs got here from dangers mistaking quirks of the info for actual patterns.
Discover your individual quotation gaps in Model Radar
The information on this research tells you what ChatGPT values. Model Radar tells you the place you’re falling quick.
Open Model Radar, arrange your model and rivals, and head straight to the Cited Pages report.
Then, filter for responses the place rivals are cited and also you aren’t.
That hole evaluation offers you a concrete record of content material to create, refresh, or restructure.
To determine what’s “citable,” ChatGPT estimates relevance, in a course of typically described as “semantic scoring”, to evaluate whether or not an article and a question are associated.
Since ChatGPT is a closed-source mannequin, we don’t have visibility into precisely how it determines relevance internally.
So, on this research, we used cosine similarity computed from embeddings generated by open-source fashions, to quantify and approximate how ChatGPT could work.
ChatGPT matches URLs towards its personal “fanout queries”—the sub-questions it generates internally (from a person’s seed immediate) to hunt for particular information.
The information confirms that title relevance to fanout queries is a crucial consider quotation:
- Immediate vs. cited URL title: 0.602
- Immediate vs. non-cited URL title: 0.484
- Fanout question vs. cited URL title (max match*): 0.656
Sidenote.
For every of those fanout queries, we compute its cosine similarity with the article title. The “max match” rating is the very best similarity amongst them—for instance, if scores are 0.45, 0.71, and 0.38, the max match is 0.71. This captures the best-aligned sub-question slightly than averaging throughout all interpretations, which might dilute the sign.
The field plots inform the story clearly. Throughout all ref_types, cited URLs have persistently larger similarity between their title and the unique immediate:
The hole widens additional after we evaluate towards fanout queries as an alternative of the unique immediate—reinforcing that creating content material related to ChatGPT’s inner sub-questions are what actually drive choice:
After we isolate the search ref_type particularly, the sample will get even sharper. Cited pages are clearly extra related, and the non-cited distribution drops considerably:
We additionally discovered that search outcomes with pure language URL slugs had an 89.78% quotation price, in comparison with 81.11% for these with out.
Finally, in case your URL and title don’t semantically align with the AI’s inner fanout queries, you’re much less more likely to get cited.
Optimize for fan-out queries utilizing Model Radar
You’ll be able to research fanout queries straight inside Model Radar. Head to the AI Responses report, choose any immediate, and also you’ll see the fanout queries ChatGPT generated alongside the cited URLs.
That is the precise set of sub-questions your content material must reply.
From there, use the AI Content material Helper to verify how nicely your web page covers the subjects these fanout queries deal with. It measures the cosine similarity between your content material and the subjects the SERP or AI response is attempting to cowl—and provides you a coloured spotlight as you write, exhibiting which gaps stay.
If a competitor’s web page is getting cited for a question the place yours isn’t, this is likely one of the quickest methods to diagnose why.
It’s widespread information that brisker content material will get cited extra by AI—and, the truth is, our personal research of 17 million citations helps that. We discovered that ChatGPT cited URLs that had been 458 days newer than Google’s natural outcomes—the strongest freshness desire of any platform we examined.
This research doesn’t contradict that narrative, however it does add an additional layer of nuance.
As an illustration, after we have a look at the search index, cited pages span a variety of ages—the median is round 500 days (~1.3 years previous), with some cited pages over 2,700 days previous (~7.4 years previous).
The median age is definitely far decrease than our preliminary freshness research linked above (958 days again in July vs 500 days on this dataset), suggesting that ChatGPT is skewing even youthful in its quotation preferences.
That mentioned, we additionally discovered that non-cited pages are overwhelmingly very younger.
So inside a single immediate’s retrieval set, it’s the older, extra established pages that are inclined to get cited, and the freshest content material that tends to get discarded.
In different phrases, ChatGPT prefers contemporary content material, however tends to quote comparatively “older” content material extra usually. That sounds counterintuitive, however each issues will be true on the similar time.
Throughout the broader inhabitants of AI citations, ChatGPT does skew brisker compared towards Google outcomes, and even towards it’s personal quotation preferences from solely final 12 months.
However inside a given retrieval set, freshness alone isn’t sufficient. Relevance nonetheless does the heavy lifting.
A brand new web page that matches fanout queries nicely will get cited. A brand new web page that doesn’t can be retrieved, but ignored.
It’s additionally value mentioning that the pool of non-cited pages (~3M) throughout the search ref_type is much smaller than the cited group (~23M), which limits how confidently we will interpret the age hole.
The place freshness issues most is in “information”.
On this class, title relevance scores for cited and non-cited pages are almost similar:
The AI can’t determine based mostly on relevance alone, so it defaults to a temporal tie-breaker: web page age. Cited information pages skew youthful:
For information queries, youthful pages have a transparent benefit, even when relevance scores between cited and non-cited pages are related.
Create the freshest information content material utilizing Firehose
In case you publish information or time-sensitive content material, freshness is non-negotiable.
Be the primary to interrupt information on sure tales utilizing Ahrefs Firehose—our real-time internet monitoring API that offers you a streaming feed of knowledge from our big crawler infrastructure.
For instance, in the event you work in SaaS journalism, you’ll be able to monitor content material modifications on pages like Google’s official weblog, so that you will be the primary one to cowl a brand new Google replace as quickly because it goes dwell.
Then, use Model Radar’s Mentions historical past within the AI Responses report to trace whether or not your ChatGPT visibility spikes after publication.
The 1.4 million prompts paint a reasonably clear image. ChatGPT is an aggressive editor. It favors its basic search index, makes use of semantic similarity to pick and cite sources, and treats Reddit as a textbook it’s embarrassed to confess it learn.
However the information additionally taught us a lesson in analytical warning.
Combination comparisons between “cited” and “non-cited” URLs will be deceptive if the non-cited pool is dominated by a single supply sort with its personal retrieval mechanics.
What initially seemed like a paradox—less-optimized pages getting cited extra—turned out to be a matter of dataset composition.
We’d have gotten that one very unsuitable if we hadn’t remoted by ref_type.
Finally, the pages that get cited are those whose titles and content material match the questions ChatGPT is asking behind the scenes, and that floor by means of the precise retrieval channel.
