Trust signals are the visible evidence that a real, accountable expert stands behind a page: a named author with a byline linking to a real profile, credentials that can be verified elsewhere (LinkedIn, ORCID, Wikidata), machine-readable published and updated dates, claims linked to primary sources, and site-level pages for contact, privacy and editorial policy. Google's quality rater guidelines call the bundle experience, expertise, authoritativeness and trust. AI engines corroborate the same signals before citing a source. This hub links the pillar-three audit checks, the author entity page, the Entity Authority framework and Laurelin's own published Non-Commodity audit.
The third pillar is the experience, expertise, authoritativeness and trust that tells Google and AI models a real, accountable person stands behind the page. It is the newest pillar on the site, opened with our own Non-Commodity audit as the first proof, and the priority for the next commissions.
Which Google search systems bear on E-E-A-T trust signals?
Panda
Panda was a Google ranking system announced in February 2011 to reduce rankings for low-quality sites and reward sites with original, useful content. Google confirms it by name in its ranking systems guide, which lists it as retired: it became part of the core ranking systems in 2015. There is no separate Panda update to recover from today. The practical lesson still holds, because Google said low-quality content on part of a site can affect the whole site's rankings, so thin and duplicated sections remain a sitewide risk.
Helpful content system
The helpful content system was a Google ranking system launched in August 2022 to demote content made mainly to attract search traffic rather than to help people. Google confirms it in its ranking systems guide, which lists it as retired: in March 2024 it became part of the core ranking systems, and Google says no single signal now does this job. For a site, the practical point is unchanged. Sections written for search demand outside the site's real purpose and audience are the pattern Google describes as unhelpful.
Medic update
The Medic update is the industry nickname for Google's broad core algorithm update confirmed on 1 August 2018. Google confirmed a broad core update but did not name it Medic: Barry Schwartz of Search Engine Roundtable coined the name after a survey in which health and medical sites were heavily represented among those affected. Google said the update involved all searches and that there was nothing specific to fix. For sites, the lasting point is that health and money topics are where Google says expertise and trust carry more weight.
Core updates
Core updates are significant, broad changes that Google makes to its search algorithms and systems several times a year and announces on its Search Status Dashboard. Google confirms them and says they do not target specific sites or pages. It also documents how to assess a drop: wait a week after the rollout ends, compare equal periods in Search Console, separate small drops from large ones, and only for large, sustained drops review the site against its helpful content self-assessment. Quick fixes are discouraged and recovery can take months.
Reliable information systems
Reliable information systems are the confirmed Google ranking systems that aim to surface authoritative pages, demote low-quality content and elevate quality journalism. When reliable information is lacking, Google shows content advisories on rapidly changing topics or when it lacks confidence in the overall quality of results. Google says it places even more emphasis on expertise and trustworthiness for health, finance, civic and crisis topics. For a site, the practical test is whether its claims are sourced, current and consistent with authoritative consensus.
Crisis information systems
Crisis information systems are confirmed Google systems that show helpful, timely information during crises. For personal crises, Google detects searches about suicide, sexual assault, poison ingestion, gender-based violence or drug addiction and shows hotlines and content from trusted organisations. For natural disasters and wide-spread emergencies, SOS Alerts show updates from local, national or international authorities. Google said in 2022 it uses MUM to detect a wider range of personal crisis searches. Sites cannot rank into these features, but publishers on these topics can signpost help clearly.
Q* (Quality score)
Q* (Q star) is Google's measure of the quality of a document, described in US Department of Justice records of 2025 calls with Google engineers that were filed as exhibits in the search antitrust case. The records describe it as largely static and related to the site rather than the query, with PageRank as one input. The 2024 Content Warehouse leak names several attributes as applied in Qstar. Google has not documented Q* publicly. For a site, it means quality is judged largely at site level and changes slowly.
Site authority (siteAuthority)
siteAuthority is an attribute in the Google Content Warehouse API documentation leaked in May 2024, described as converted from a quality signal and applied in Qstar, Google's quality score. Googlers had said for years that Google does not have an overall domain authority score. The two can both be true: siteAuthority is a quality input, not a third-party style link metric. Google has not confirmed how it is used. For a site, it means reputation accrues at site level and new hosts start from little.
Site quality score patent
The site quality score patent (US9031929B1), granted to Google in 2015 with Navneet Panda and April R. Lehman as inventors, describes scoring a site as the ratio of queries that refer to the site by name to queries that lead to clicks on the site's pages. A related Panda patent predicts site quality from phrase use. Google has not confirmed either is used in ranking. For a site, the practical reading is that branded demand relative to generic visibility is worth tracking.
Local news systems
Local news systems are confirmed Google ranking systems that identify local sources of news and surface them when relevant, through features such as Top stories and Local news. Google announced a local news carousel in Search in November 2021 and said in 2022 that authoritative local sources now appear more often alongside national publications. Google has not published which signals mark a source as local. For publishers, clear transparency about who you are, where you report and when, plus consistent local coverage, are the inputs you control.
What has Laurelin published on E-E-A-T trust signals?
Which of the 387 audit checks belong to E-E-A-T trust signals?
29 checks in the Trust Signals pillar of the 387-check AEO audit, each cited to a primary source. The most consulted first.
- No named author
- Author has no bio/credentials
- No author Person schema
- Author not linked to consistent profile
- No expertise signals on YMYL
- No first-hand experience evident
- Claims without sources
- Citations to low-quality sources
- Factual inaccuracies
- No ‘last reviewed/updated’ on time-sensitive content
- Anonymous content where authority matters
- No About page
- No contact information
- No company/address details (YMYL/commerce)
- Missing privacy policy/terms
- No editorial/sourcing policy
- Excessive/deceptive ads
- No HTTPS (trust)
- Unverifiable reviews/testimonials
- Misleading/clickbait titles vs content
- No trust badges/affiliations (commerce)
- Inconsistent NAP across the site
- YMYL section sits outside the site's demonstrated expertise (Medic-era risk)
- Large post-core-update drops not assessed with Google's documented method (core updates)
Frequently asked questions about E-E-A-T trust signals
What are E-E-A-T trust signals on a web page?
Experience, expertise, authoritativeness and trustworthiness, as Google's search quality rater guidelines define them, made visible in markup: a named author with a linked profile and Person schema, dates, cited sources, an about page, a contact page and an editorial policy. They are not a direct ranking factor but they are what raters and answer engines check before trusting a page.
Does an author page really matter for AI search?
Yes. Answer engines resolve the author to an entity before weighing the page, so a byline that links to a profile with resolvable sameAs identifiers (Wikidata, ORCID, LinkedIn) is corroborated, while an anonymous page or a name with no profile cannot be.