This document describes how we classify citations returned by AI engines in our AEO product.
Our goal is to turn raw citations (URLs/domains) into structured data that explains:
- WHO is behind the content
- WHY / under what incentives the content was created
- WHAT the page looks like structurally
This 3-layer model lets us analyze citation patterns in a way that existing AEO tools cannot. Most AEO tools today classify citations only at the domain level (for example, Owned vs. Media vs. Social), which loses critical page-level nuance such as affiliate monetization or hybrid editorial content.
We classify every citation independently along three axes:
- Source Type — WHO is behind this site?
- Content Type (Content Origin) — WHY was this specific page created and under what incentives?
- Format Type — WHAT does this page look like structurally?
Question: What type of organization is publishing this domain?
Level: Domain-level (applies to all URLs on the same domain)
Definition:
Source Type describes the publisher / domain owner, independent of any particular article or page. It answers: “What kind of entity runs this website?” Examples include: a brand’s own site, a competitor, a news outlet, a review platform, a government agency, or a non-profit.
We use Source Type to understand whose voice AI models are amplifying when they cite content about a brand.
- Different types of publishers (media, social, institutional, brand-owned, etc.) tend to be cited in different situations by AI engines.
- Domain type is a low-cost, stable signal we can compute and cache. It is the foundation for understanding the mix of voices in a brand’s citation profile.
Question: What is the economic and editorial origin of this page?
Level: Page-level (varies across URLs on the same domain)
Definition:
Content Type captures who created this specific piece of content and what incentives or disclosures apply. Examples:
- Independent editorial coverage written by a journalist.
- Affiliate content earning commission through outbound links.
- Brand-authored marketing copy on a product page.
- User-generated reviews or forum posts.
- Sponsored posts or press releases.
- Product documentation or developer docs.
- Original research reports or benchmark studies.
This is not about the technical act of publishing in a CMS. It is about the business model and editorial origin of the page:
- Is this editorial coverage written by an independent publisher?
- Is it affiliate content with commission-based links and disclosures?
- Is it UGC (user-generated content) on a platform or community?
- Is it sponsored content or a press release?
- Is it brand-owned marketing or documentation created by the product team?
We use Content Type to understand the incentive structure and potential bias behind citations.
Question: How is this page structured and presented to the reader?
Level: Page-level
Definition:
Format Type describes the layout and content structure of a specific URL. Examples include:
- Listicle – "Top 10 Best CRMs" style numbered lists.
- Comparison – "X vs Y vs Z" side‑by‑side evaluations.
- Review – single product/service deep‑dive review.
- Product page – price, features, and conversion CTAs.
- Landing page – campaign or offer page with one dominant CTA.
- Category page – collection or listing of multiple products.
- How‑to / Guide / Educational – structured instructional or explainer content.
- FAQ / Wiki / Glossary – Q&A, reference, or term‑definition structures.
- Case study / Research / News / PR – narrative or report‑style formats.
- Tool – interactive calculators, generators, or checkers.
- Social thread / Forum thread – conversational posts and replies.
Academic web genre research and real‑world citation data both show that format strongly predicts likelihood of being cited: listicles, comparisons, and guides are disproportionately present in AI answers.
We use Format Type to understand which structural page types AI engines prefer to quote and where a brand may need to produce or improve specific formats.
The three layers work together as follows:
- Source Type tells us who is speaking.
- Content Type tells us why that specific page exists and what incentives shaped it.
- Format Type tells us how the information is structured for readers and AI engines.
By keeping these layers separate and orthogonal, we can:
- Avoid forcing a single, lossy label on complex publishers (e.g., Forbes being both editorial and affiliate).
- Run much richer analyses on which voices, incentive models, and formats dominate a brand’s AI citation profile.
- Extend the model later (for example, with Brand Framing or trust scores) without breaking the core classification scheme.