The premise needs correcting before answering it: Google has stated repeatedly and explicitly, through both Danny Sullivan and John Mueller, that E-E-A-T is not itself a single, directly computed algorithmic score or ranking factor. It’s a rater-evaluation framework used in the Quality Rater Guidelines to describe what human raters should look for, and Google’s actual ranking systems approximate the qualities E-E-A-T describes through a variety of other, more concrete signals, not through a unified “E-E-A-T score” that gets calculated and applied. Any answer to this question has to lead with that correction, because treating the proxy signals below as confirmed algorithmic inputs specifically measuring “E-E-A-T” would misrepresent how Google has described its own system.
Why there’s no single E-E-A-T computation
Google’s public position is that E-E-A-T is best understood as a conceptual framework, useful for explaining what “quality” means across the many different systems that actually influence rankings, rather than a discrete module in the ranking pipeline. There’s no published “E-E-A-T algorithm” because E-E-A-T was never designed as an algorithm, it’s a rater-facing evaluative lens, and Google has been consistent that various existing systems (content quality classifiers, link-based authority signals, spam detection, and others) are what approximate, in aggregate, the qualities raters are trained to identify.
That distinction matters practically. It means there’s no single lever to pull that “improves your E-E-A-T score,” because no such unified score exists to move. What follows is more diffuse: multiple independent signal categories that plausibly correlate with what raters would judge as expertise, authoritativeness, and trustworthiness, evaluated separately by different systems rather than combined into one E-E-A-T metric.
Plausible on-page proxy signals (labeled as inference, not confirmed algorithmic inputs)
These are reasonable, industry-consensus signals that align with what the Quality Rater Guidelines instruct raters to look for, not a confirmed list of algorithmic inputs Google has disclosed:
- Author identification and bio depth. Content with a clearly identified author, relevant credentials, and a bio that establishes subject-matter connection aligns with what raters are told to check when researching “who created this and why.” Structured data (Person/Author markup) can support this but isn’t itself a ranking signal, it aids machine-readability of information that’s already present.
- Citations and sourcing within content. Content that references and links to authoritative external sources for factual claims is consistent with what raters are asked to evaluate when judging trustworthiness, particularly on YMYL topics.
- Content depth and demonstrated first-hand experience. Google’s helpful-content guidance explicitly names firsthand experience and evidence of it (specific details, original documentation, direct testing) as a quality signal, which maps onto the “Experience” component added to E-E-A-T’s rater framework.
- Transparency signals. Clear “about,” contact, and editorial-policy information, which raters are instructed to check as part of trustworthiness assessment for a site overall.
Plausible off-page proxy signals (same caveat)
- Backlink profile quality and relevance, evaluated as an established, long-standing ranking input independent of E-E-A-T specifically, but plausibly correlated with the authoritativeness raters are asked to assess.
- Brand and entity recognition, including branded search volume and independent mentions/citations of the author or organization across the web, consistent with the “reputation research” raters are explicitly instructed to perform (searching for outside information about a site or author beyond the page itself).
- Third-party reviews and reputation signals, particularly relevant for YMYL and local/product-related content, aligned with what raters check when researching site reputation.
What to avoid concluding from this list
Do not treat this as a confirmed checklist of algorithmic ranking factors that, if satisfied, guarantee improved rankings via an “E-E-A-T boost.” None of these signals has a disclosed weight, and Google has not confirmed that they’re combined into anything resembling an E-E-A-T computation. They’re best understood as informed inference: things that plausibly correlate with what human raters are trained to look for, which in turn presumably correlates with outcomes Google’s various ranking systems are trained toward, but that’s several inferential steps removed from “Google’s algorithm computes E-E-A-T using these inputs.”
The practical implication
The useful takeaway isn’t a technical checklist to game, it’s that genuine investment in the underlying substance, real authorship and credentials where relevant, real citations, real firsthand experience reflected in the content, genuine external reputation, tends to align with both what raters are trained to reward and what a range of independent ranking signals plausibly respond to. Treating any single proxy (adding an author bio, adding a schema markup type) as a direct algorithmic lever misunderstands the mechanism; the actual, defensible position is that these are correlated signals across multiple independent systems, not inputs to a unified, computable E-E-A-T score.
As a hypothetical example, imagine a supplement-review site, “Site N,” that adds Person schema and generic author bios to every article without changing the underlying content, expecting an “E-E-A-T score” to improve. If rankings hypothetically didn’t move, that outcome would be consistent with everything above: there’s no discrete score to nudge with markup alone. Contrast that with a hypothetical second scenario where Site N instead has its articles reviewed and co-signed by a credentialed dietitian, adds citations to primary clinical sources, and publishes original testing notes, changes that plausibly move the underlying proxy signals raters and various ranking systems actually respond to, rather than just labeling the same content differently.
