Pillar · Hub
E-E-A-T: the four dimensions and their systems
The framework Google uses to evaluate content quality — and the 25 systems that implement it
What Google goes by
E-E-A-T is not a single ranking signal, meaning not a single measured value. It is a quality framework that Google's ranking systems implement together. Each of the 25 systems documented in this reference contributes to one or more E-E-A-T dimensions. This pillar page is the hub that connects them.
The four questions
Has someone here actually done the thing, or only read about it?Experience · 2 systems →Can you tell from the page that whoever wrote it understands the subject?Expertise · 3 systems →Do other people treat this site as the address for the subject?Authoritativeness · 5 systems →Can a reader act on this, and is it clear who stands behind it?Trustworthiness · 11 systems →The systems
Primary (15)
Systems where Experience / Expertise / Authoritativeness / Trustworthiness is the primary E-E-A-T dimension.
NavBoost, the system that evaluates clicks from Google search, watches what users do after a search. Do they click on a weather page and stay, or jump straight back to Google? The last, longest click of a session is the strongest signal, because it shows the need was genuinely met. Real user behavior thus becomes a re-ranking signal, without Google having to read the content directly.
Glue is the counterpart to NavBoost for special Google elements like carousels, 'People also ask' boxes, or news features. It measures whether users interact with these elements: tapping a carousel, expanding an answer in a PAA box, or arriving at a page via a news feature. Instant Glue is the real-time variant for fast-moving events.
Chrome data show how often a site is accessed in the Chrome browser overall, regardless of whether someone arrived via Google search or typed the URL directly. High direct visits show that users trust the site enough to navigate there without asking Google. The metric measures brand popularity and willingness to return.
Q* is a summary quality score for the entire website. It works like a 'balance' made up of the site's authority (siteAuthority), a low-quality flag (lowQuality), and the overall NSR value. The score changes slowly: internal Google documents describe it as 'largely static'. It is a sluggish foundation, not a day-to-day signal.
NSR (Normalized Site Rank) is the real quality core. It is a normalized quality and authority value, computed for individual 'chunks' (subsections) of a website. New sections initially inherit the average of their neighboring chunks ('fallback inheritance'). The historicized signal (predictedDefaultNsr) shows development over time: the trajectory matters, not today's single value.
chard rates the content quality of individual pages — calibrated against the judgments of human quality raters that Google trains according to strict guidelines (QRG). A variance score shows how confident the model is in its verdict. chard is especially sharp on YMYL content (Your Money or Your Life — topics with real life consequences such as health, safety, severe weather) and on suspected misinformation.
A language model estimates how much editorial effort and depth went into a page. The system distinguishes between shallow, machine-generated content and pages with recognizable research, structure and substance. The value is not a hard measurement but a model estimate, one that increasingly reliably separates thin AI content from genuine editorial engagement.
This score measures how much first-hand material a piece of content contains: information found only there, that an AI could not reconstruct from other sources. The higher the score, the more original the content. It is the closest machine equivalent to the E-E-A-T dimension 'Experience' (lived experience and direct observation).
Not a live signal used daily in ranking — but the foundation on which all quality models are calibrated. The IS-Score (Information Satisfaction) summarizes how well a page meets a user's information need, rated by trained human raters. The QRG (Quality Rater Guidelines) are the public rulebook by which these raters judge.
Google deducts points for three concrete quality and navigation deficiencies. navDemotion penalizes poor page guidance, such as unclear navigation or misleading structure. anchorMismatch penalizes links whose link text does not match the destination, for example 'more info' instead of 'Germany rain radar'. serpDemotion comes from negative user behavior in search results: users click, bounce straight back and click on competitors.
Google deducts quality points in three ways. clutterScore measures layout overload: too many ads, pop-ups or distracting elements that disrupt the information flow. scamness measures fraud proximity: patterns that resemble disreputable sites. unauthoritativeScore measures missing credibility: no author details and no recognizable institution behind the page.
Panda lowers the ranking of an entire website when too much of it consists of thin or low-quality content. It applies to the whole site, not just to individual pages. If too large a share of a site is rated as 'thin content' (content-poor pages), all pages of the site rank worse. BabyPandaV2 is a newer, more finely calibrated variant of the same principle.
This system detects manipulative link patterns in anchor text, the clickable text of a hyperlink. A page that is linked exclusively with exact keyword texts ('Berlin weather', 'free weather forecast') looks unnatural, like deliberate SEO manipulation. Penguin is the historic Google update that addressed this problem. Its logic lives on in today's system.
SpamBrain is Google's AI-based spam detection system. Its most practically relevant aspect is 'scaled content abuse': the mass production of content following the same pattern every time, without genuine editorial added value. A typical example is thousands of location pages that only plug variables (city name, coordinates, measurement data) into a template, with no human editorial quality check.
Google detects a 'date lie' by comparing three different signals for a page's date. (1) The visible date in the article (bylineDate) is easy to fake. (2) The date from URL, markup or timestamp (syntacticDate) is also easy to manipulate. (3) The date inferred from the text content (semanticDate) can only be changed through genuine content updates. When the three don't agree, Google sees a 'date lie'.
Flanking (9)
Systems that support Experience / Expertise / Authoritativeness / Trustworthiness indirectly.
Deep Dives (27)
NavBoost: How Google Uses Click Behavior to Re-Rank Search Results
From DOJ testimony to leaked API documentation — the most important ranking signal you can't directly manipulate
Panda: The Site-Wide Quality System Named After Its Inventor
How Google's war on content farms produced a patent trail that leads directly to the update's name — and what the API leak reveals about its current form
Q* and P*: The Two Pillars of Google's Ranking Architecture
The DOJ trial revealed that Google's ranking ultimately reduces to two top-level signals — Quality (Q*) and Popularity (P*). This is what the evidence shows about how they work.
hostAge-Sandbox: Why New Domains Start at a Disadvantage
The patent that reveals how Google evaluates domain legitimacy — and why patience is the only strategy for new sites
Anchor-Spam: How Google Detects Manipulative Link Patterns
From 'miserable failure' Google bombs to the patent that reveals how context analysis catches anchor text manipulation
FreshnessTwiddler: How Google Decides When Fresh Content Matters
The query-dependent freshness system — and the patent that reveals how update frequency, staleness, and link freshness become ranking signals
Date triangulation: How Google Determines When a Page Was Really Published
The patent that reveals six different date sources Google uses — and why faking a byline date doesn't work
IS-Calibration: The Human Rater System That Trains Google's Quality Models
The hardest evidence in the entire matrix — DOJ/sworn material confirms the rater system that calibrates every quality signal Google uses
SpamBrain: Google's AI-Powered Spam Detection System
How machine learning identifies scaled content abuse, link spam, and manipulated rankings — and why 'AI-generated content at scale' is the new content farm
chard: Google's Page-Level Content Quality Model
The leak field that rates individual page quality — calibrated by human raters, feeding into NSR, and why YMYL pages face stricter standards
OriginalContentScore: How Google Measures Content Originality
The leak field that measures how much original material a document contains — information that is only found there and that an AI couldn't reconstruct from other sources
contentEffort: How Google Estimates Editorial Effort
The AI model that estimates how much editorial work went into a page — and why 'effort' is becoming a ranking signal
Topic-Embeddings: How Google Understands What Your Page Is About
The focus vectors and radius that determine topical relevance — and why a weather site needs a narrow focus to rank for weather queries
Glue: How Google Measures Interaction with Special Search Features
The NavBoost counterpart for carousels, 'People also ask' boxes, and news features — measuring whether users interact with special elements, not just blue links
Chrome signals: How Browser Data Complements Search Data
The signals that measure how often a site is accessed directly in Chrome — independent of Google Search
IP-Prior: How Google Protects Click Data from Manipulation
The system that detects unnatural click patterns and protects NavBoost from gaming — the integrity layer of behavioral ranking
clutterScore, scamness, unauthoritativeScore: Three Quality Demotions
The three signals that penalize layout overload, fraud proximity, and untrustworthiness — and why they combine into a single Trust signal
QualityBoost-Demotions: The Penalty System for Quality Deficits
navDemotion, anchorMismatch, and serpDemotion — three specific penalties for navigational, linking, and SERP quality issues
RankBrain and DeepRank: How AI Changed Google's Understanding of Queries
From keyword matching to semantic understanding — the AI systems that interpret what users mean, not what they type
Crawl-Budget: How Google Decides How Often to Visit Your Site
The officially documented system that determines crawl frequency — and why it matters for large sites with thousands of URLs
Tangram: The Assembly Layer That Builds Search Results
Tangram evaluates nothing itself — it takes ratings from NavBoost and Glue and decides what appears on the results page, where, and in what order
tofu, keto, Rhubarb: The Subchunk Refinement Signals
Codenames for refinement signals at the most granular level, the individual subsections of a page. They are the weakest evidence in the matrix.
SegIndexer: How Google Tiers Its Index
Google does not index all pages equally. The SegIndexer decides which tier (level) of the index a page lands in, from lightning-fast to slow storage.
Site Reputation Abuse: What Google Suspended in Europe — and What It Did Not
What Google suspended in the EEA is the manual action — the replacement removes exactly what parasite SEO pays for
Google Update History: What a Timeline of Named Updates Can and Cannot Show
Google names roughly ten updates a year and ships thousands of changes. The timeline is real, dated and official — and it is a filtered excerpt of Google's own communication decisions
What Good Content Is: contentEffort and Google's Soft Metrics
One leaked field, one rater definition, twelve official questions, and a checklist that marks which parts are evidence and which are our reading.
Links That Count: Five Link Tactics Against the Leak and the Patents
A popular link guide reads PageRank buckets, trusted anchors and context hashes out of Google's leak. The fields are real. The trusted sources are news sites, the bucket counts onsite anchors, and the cap is 200, not zero.