EEAT Mechanics

Topic-Embeddings (Focus/Radius)

BVerbatim from the leak

Google computes a kind of 'topic map' from the entire text content of a website (embedding, a mathematical vector representation of the topic space).

By Thomas Wawra·Last reviewed
Role
Quality & Prediction
Axis
Geometry
Scope
Site
Calibrated against
Language
Experience· noneExpertise· indirectAuthority· indirectTrust· none

01What it measures

Google computes a kind of 'topic map' from the entire text content of a website (embedding, a mathematical vector representation of the topic space). siteFocusScore measures how sharply the site's topic field is defined. siteRadius measures how widely it scatters thematically. A small radius means a tightly focused site. The system measures thematic consistency, not subject-matter competence.

02Derived from

The system learns self-supervised from the site's language use, meaning from the actual text of all pages, without human labels. The resulting vectors feed into NSR as a focus vector.

03How it's measured

siteEmbeddings is the vector representation of the site's topic space. siteFocusScore shows how sharply the topic field is outlined. siteRadius shows how widely the site scatters in topic space: a small radius means a focused site. [B fields]

04Why it counts here

Expertise and Authority are indirect ('flanking'). A focused weather site is more likely to be perceived as a weather expert than one that also offers recipes and travel reports alongside weather. But focus alone doesn't substitute for quality: it is a multiplier.

05What to do about it

A clear thematic focus feeds the focus score positively. Content far outside the thematic core can raise the radius. For international expansions or new brands: plan thematic coherence from day one.

Deep dive →
Topic-Embeddings: How Google Understands What Your Page Is About
17 claims· Expertise Authority
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