Algorithm
EEAT
Also known as: E-E-A-T, Experience Expertise Authoritativeness Trustworthiness
Google's quality framework: Experience, Expertise, Authoritativeness, Trustworthiness. EEAT signals are increasingly weighted in both classical ranking and AI Overview source selection.
What it is
Google's quality framework covering Experience, Expertise, Authoritativeness, and Trustworthiness, used to judge how much a page and its creator can be relied upon. It is a lens for human raters and an influence on automated quality signals rather than a single score.
Why it matters
It increasingly shapes both classical ranking and which sources AI Overviews choose to cite, so weak EEAT can exclude a page from answer surfaces entirely. For sensitive topics it is often the deciding factor in whether content is shown at all.
How it works
It works through corroborating signals such as named authors with verifiable credentials, first-hand experience, citations, and a trustworthy site reputation, so the response is to make expertise and experience explicit and verifiable. Strengthen author bios, sourcing, and off-site reputation rather than adding keyword volume.
When it applies
It applies to every query but weighs heaviest on health, finance, safety, and other your-money-your-life topics.
Examples
- A medical article reviewed and bylined by a licensed clinician.
- A travel guide written by someone who clearly visited, with original photos and specific detail.
- A finance page citing primary regulatory sources rather than restating competitors.
How it is measured
- Presence and completeness of author credentials and bios
- Volume and quality of authoritative inbound citations
- First-hand experience signals such as original media and specifics
- Brand and entity reputation across independent sources
Related terms in Algorithm
- Core updateA broad Google algorithm update affecting search rankings sitewide rather than a single feature. Recent core updates (e.g. May 2026) have increasingly favoured intent-aligned content over keyword-optimised content.
- Entity salienceEntity salience is a measure of how central an entity is to a piece of content, as opposed to merely being mentioned in it. Natural language processing systems assign a salience score based on signals such as position, frequency, grammatical role and surrounding context. It helps a system decide what a document is actually about.
- Google Search algorithmThe Google Search algorithm is the collection of ranking and retrieval systems Google uses to find, order and present results for a query. It is not a single formula but many interacting systems that assess content, links, query intent, context and user signals. Google updates these systems continuously, with larger changes announced as core updates.
- Google Spam UpdateA Google Spam Update is a rollout of changes to the systems Google uses to detect and demote content that breaks its spam policies. These updates target things like scaled content abuse, cloaking, hacked pages, link schemes and site reputation abuse rather than general content quality. Sites hit by a spam update usually need to remove the offending practices before they can recover.
- Helpful Content UpdateGoogle's quality-focused update class targeting low-value, AI-generated, or thin content. Successive HCUs have favoured first-hand experience and named expertise over comprehensive but shallow coverage.
- Ranking signalsRanking signals are the inputs a search system uses to decide which results to show and in what order. They include relevance signals from the page and query, quality and authority signals, and context such as location, device and language. Search engines do not publish the full list or the weightings, so practitioners work from documented signals, patents, testing and observed patterns.