EEAT Mechanics

Topic-Embeddings (Focus/Radius)

BLeak, verbatim

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
Function class
Quality & Prediction
Axis
Geometry
Reach
Site
Calibration source
Language
Experience· noneExpertise· indirectAuthority· indirectTrust· none

What 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.

Derived from

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

Metric detail

siteEmbeddings = vector representation of the site's topic space; siteFocusScore = how sharply outlined the topic field is; siteRadius = how widely the site scatters in topic space (small = focused). [B fields]

Why this attribution

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.

Strategic consequence

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
13 claims· Expertise Authority

Related systems

Answer modules will appear here (coming soon)