How to Score E-E-A-T Reproducibly in GEO Analysis Tools

GEO (Generative Engine Optimization) is a practical concept for improving and analyzing the likelihood that content will be discovered and used as a basis for answers in generative AI environments such as ChatGPT and Gemini. However, generative AI services have not disclosed a complete formula for selecting sources, and there is no single standardized score commonly used for GEO.

Google’s E-E-A-T stands for Experience, Expertise, Authoritativeness, and Trustworthiness, but it is not an official numerical score that Google publishes for each webpage. Therefore, it must be made clear that an “E-E-A-T score of 78” displayed by an analysis tool is not Google’s assessment but an observed metric defined by that tool.

Why You Should Not Let an LLM Assign Scores Directly

You can quickly build a prototype by feeding an entire webpage into an LLM and asking it to rate experience, expertise, authoritativeness, and trustworthiness out of 100. However, this creates the following problems for a production measurement system.

• Scores may vary between runs even with the same content and prompt.
• If the model or provider changes, it becomes difficult to compare scores with historical results.
• Hallucinations may occur in which the model infers experience, credentials, or reputation not found on the page.
• It is difficult to audit the sentences and rules that affected the score.
• The system may accept a site’s self-description as fact without external verification.
• Results may change if a long document is truncated or the extraction order changes.

The recommended architecture is as follows.

Website
  → Collect a single-point-in-time snapshot
  → Extract body content, metadata, entities, and claims
  → Cross-check against external sources
  → Calculate scores using version-locked rules
  → Store scores, evidence, and confidence
  → Have an LLM explain the results in natural language

This does not mean that LLMs should not be used at all. They can be used for sentence classification, candidate claim extraction, and name-variant discovery, but their outputs should be linked to the original supporting text and cached. Having code handle the final arithmetic and cap and penalty rules improves reproducibility and auditability.

Defining the Evaluation Target and Scope First

Before calculating scores, you must establish what is being evaluated. Signals for the overall site, organization, author, and individual document are not interchangeable.

The topic must also be classified. Fields where misinformation can cause serious harm, such as medicine, finance, and law, require different evidence of expertise and trust than personal hobby reviews. Applying one fixed set of weights to every field weakens the meaning of the score.

How to Collect a Consistent Web Snapshot

The analyzer should share a single saved snapshot. If the body content is requested at 10:00 a.m., JSON-LD at 10:05, and the author page at 10:10, the snapshot may combine different states after changes have occurred.

Where possible, store the following items in the snapshot.

• Final URL, redirect path, HTTP status code, and response headers
• Raw HTML and, where necessary, rendered HTML
• Extracted body content, title, description, canonical, and language information
• Author, publication date, modification date, and publishing organization
• Internal and external links and anchor text
• Structured data such as JSON-LD and Microdata
• robots directives and related URLs identified in the sitemap
• Collection time, collector version, and content hash

When exploring multiple pages, do not rely only on the homepage. Use sitemap.xml and internal links to explore candidates such as About, Company, Team, Author, Profile, Editorial Policy, Contact, Privacy, and Terms to a limited depth. Pages containing personal information or access restrictions should not be collected aggressively, and robots policies, terms of service, and applicable laws must also be reviewed.

Separating On-Site and Off-Site Evidence

On-site data is information published directly by the site. This includes author bios, product descriptions, customer case studies, editorial policies, contact details, and Person and Organization structured data.

Off-site data shows how independent external sources verify an author or organization. This includes government and public agency records, academic institutions, professional associations, trustworthy media, industry sources, external profiles, and relevant backlinks and citations.

The two types of evidence must be distinguished in scoring.

• Self-promotional claims such as “the best in the industry” are not independent evidence of Authority.
• Even an external article has low independence if it simply republishes a press release.
• If the same article is duplicated across different URLs, do not count the sources more than once.
• Verify entities to avoid linking the achievements of a different person or organization with the same name.
• sameAs links are clues connecting entity candidates; they do not automatically guarantee that the stated experience is true.

Experience: Turning Firsthand Experience into Evidence

Experience evaluates the author’s firsthand experience using a product, visiting a place, carrying out a procedure, or running an experiment. It is separate from Expertise. A purchaser who has used a laptop for a long time may have extensive real-world experience but may not be an expert in battery engineering.

Signals to Detect

• Statements indicating firsthand use, purchase, visitation, installation, operation, or comparison
• Duration of use, number of tests, sample size, environment, and equipment
• Steps performed, failures, constraints, and exceptions
• Original images, logs, raw data, and reproduction procedures
• Before-and-after measurement results and measurement methods

Assigning a high score based only on a single statement such as “I tried it myself” is easy to manipulate. Specific numbers do not by themselves prove that something is true. They should be treated as stronger evidence when the measurement method, duration, raw data, and context are mutually consistent.

Experience scores can be tiered as follows.

Expertise: Evaluating Verifiable Expertise

Expertise examines whether the author has the knowledge and capability to cover the subject accurately. Simply labeling someone an “expert” next to their name is not enough.

Potential items for analysis include:

• Relevant title, affiliation, field of work, and duration of experience
• Degrees, recognized certifications, licenses, and issuing institutions
• Relevant research, papers, books, lectures, and projects
• Actual work experience related to the subject
• Accuracy, scope, limitations, and sources of professional explanations
• Expert reviewer and review date

If the author value is a role name such as admin, administrator, 관리자, 운영자, 운영팀, or editor, do not identify it as an actual person. Even when Person structured data exists, verify that it matches the author information displayed on the page.

Expertise must reflect relevance to the topic. A legal qualification can be a strong signal for legal content, but it does not automatically establish expertise in every medical or technical topic. In fields where credential verification is necessary, increase the verification level only when the issuing institution or an official lookup source can be linked.

Authoritativeness: External Recognition and Entity Matching

Authoritativeness evaluates the extent to which a person, organization, or site is recognized externally in a specific subject area. Source quality, topical relevance, independence, and diversity matter more than the raw number of mentions.

An authoritativeness analyzer can follow this process.

1. Create canonical entities for the organization name, author name, domain, and brand.
2. Link verified aliases such as former names, English names, and abbreviations.
3. Determine whether external documents refer to the same entity.
4. Evaluate each source’s independence, quality, topical relevance, and recency.
5. Group press release republications and duplicate copies of the same document.
6. Distinguish whether a citation or mention represents positive recognition, a simple listing, or criticism.

If a Korean-language web analysis uses only Wikipedia, Wikidata, Reddit, and English-language media, it may underestimate the authority of Korean institutions and companies. Depending on the market being evaluated, the source registry should include government and public agencies, public data, academic databases, professional associations, major media outlets, and specialized industry publications. Conversely, exposure on a portal in a particular country should not by itself be treated as proof of global authority.

It is advisable to record jurisdiction, publisher, topical scope, independence, original-source status, update frequency, and access conditions in the source registry. Even when country-specific sources are added, the scoring rules and selection criteria should be disclosed.

Trustworthiness: The Broadest and Most Important Safety Dimension

Trustworthiness supports the other three elements. Even when firsthand experience and credentials exist, confirmed false claims, concealed conflicts of interest, or source manipulation may justify applying a cap to the overall evaluation.

The trustworthiness module should review the following items.

Publisher and Accountability

• Whether the operating organization and author are clearly identified
• Whether contact methods and customer support information exist on actual pages
• Whether editorial, review, and correction policies can be verified
• Whether advertising, sponsorships, affiliate relationships, and conflicts of interest are distinguished

Claims and Evidence

• Whether key facts are linked to original sources or appropriate evidence
• Whether quotations and statistics are consistent with the intent of the original source
• Whether publication and modification dates correspond to actual content changes
• Whether facts, opinions, and promotional claims are clearly distinguished
• Whether uncertainty, scope of application, exceptions, and limitations are disclosed

Transactions and Safety

• Whether the privacy policy and terms of use are appropriate for the nature of the service
• Whether payment, refund, and shipping conditions are clear on sites where they are needed
• Whether technical risks such as insecure HTTP, malicious redirects, or broken certificates are absent
• Whether health, financial, and legal information avoids dangerous categorical claims or guarantees

The mere presence of HTTPS or a privacy policy does not make content accurate. These are baseline safety signals; claim-level trustworthiness must be verified separately.

Structured Data and Entity Verification

Person and Organization structured data provide names, affiliations, titles, official URLs, and relationships with external profiles in a machine-readable form. Article properties such as author, publisher, datePublished, and dateModified are also useful for representing a document’s source structure.

However, Schema markup should be evaluated according to the following principles.

• Verify that structured data matches the content visible on the page.
• Do not treat credentials, awards, or reputation as established facts merely because they are present.
• Check whether identifiers and relationships among Person, Organization, and Article are consistent.
• Verify that sameAs targets are actual official profiles.
• Distinguish syntax errors from missing required or recommended properties.
• Do not award extra points for excessive or irrelevant use of Schema types.

Structured data is a presentation layer that assists understanding and extraction. It does not guarantee high search rankings or citations by generative AI.

Designing a Reproducible Scoring Formula

If each signal is treated only as “present” or “absent,” differences in quality will be missed. Separating values as follows makes it easier to trace the evidence.

• presence: Whether the signal exists or the degree to which it is satisfied
• verification: The degree to which it has been independently verified
• relevance: Relevance to the evaluated topic
• source_quality: Quality and independence of the supporting source
• freshness: Validity of signals for which recency matters
• weight: Signal importance managed by version

An example formula is as follows.

Signal contribution = weight × presence × verification × relevance × source_quality × freshness
Dimension score = 100 × sum of contributions ÷ sum of applicable weights

This formula is a design example, not an official E-E-A-T formula. Each coefficient can be normalized to a value between 0 and 1. Only logically inapplicable items should be excluded as N/A, while a state in which required evidence was not found should be distinguished from a score of 0 and stored as missing.

Displaying Confidence Separately from the Score

Even a result of 78 is difficult to trust if half of the required pages could not be collected. Evaluation confidence or evidence coverage should therefore be calculated separately.

Evaluation confidence = collection coverage × entity match confidence × proportion of verifiable evidence

At a minimum, the results screen should display the following together.

• Scores by dimension and overall score
• Evaluation confidence
• Verified, unverified, conflicting, and excluded statuses
• Evidence URLs and excerpts from the original text
• Collection time and snapshot hash
• Rule, weight, and source registry versions

Separating the Overall GEO Score from E-E-A-T

High E-E-A-T does not necessarily mean that content will be cited in AI answers. Answer systems may also consider relevance to the question, ease of information extraction, recency, crawlability, and document format, while the specific selection method differs by service.

A GEO tool is better organized into separate modules as follows.

For example, the overall score could consist of 30% AI citation readiness, 20% brand authority, 20% content E-E-A-T, 20% technical accessibility, and 10% Schema. These percentages are only an example of product policy. Actual weights should be calibrated using topic-specific validation data, and their version should be displayed.

AI citation readiness should not automatically favor short sentences alone. It should evaluate whether key claims can be understood independently, whether evidence and conditions appear nearby, and whether the structure of tables, lists, and headings preserves meaning. Hidden text, repetitive phrases, and unsupported mass-produced pages intended to deceive search engines or AI should be treated as penalties or risk signals.

An Architecture That Prevents Error Propagation Between Modules

If each analyzer directly accepts another analyzer’s conclusion as input, early errors can be amplified. What should be shared is the raw snapshot and normalized evidence rather than conclusions.

Snapshot Store
 ├─ Content / Claim Analyzer
 ├─ Author / Expertise Analyzer
 ├─ Entity / Authority Analyzer
 ├─ Trust Analyzer
 ├─ Technical Analyzer
 └─ Schema Analyzer
          ↓
 Evidence Store → Deterministic Scorer → Explanation LLM

Each judgment record should include claim_id, evidence_id, the original-text location, the judgment rule, and the module version. Only the calculated JSON and permitted evidence sentences should be passed to the LLM, with constraints prohibiting it from adding new credentials or external reputation information.

Commonly Overlooked Problems: Uncertainty and Adversarial Manipulation

Many GEO analyses focus on finding signals but do not adequately address situations in which a site attempts to deceive the analyzer. This issue should be handled through a separate quality and security layer.

• Experience and keywords may be repeatedly inserted into invisible areas.
• Fake authors and Person structured data may be created.
• The same press release may be distributed across multiple domains to inflate external mention counts.
• Nonexistent research, credentials, or figures may be cited.
• Only the modification date may be updated to present an old article as current content.
• The system may be induced to incorrectly associate the authority of a person with the same name or a similar brand.

Countermeasures include comparing visible page content with markup, clustering original sources, verifying credential issuers, detecting changes based on content hashes, cross-checking entity attributes, and detecting abnormal link patterns. When serious discrepancies are found, it is safer to apply an overall score cap or manual-review status rather than a simple penalty.

How to Validate the Evaluation Model

Even if a scoring formula is deterministic, that does not automatically make the evaluation valid. The following tests are necessary.

1. Repeatability test: Verify that the same snapshot and version produce bit-for-bit identical results.
2. Expert benchmark set: Compare system results with evidence independently labeled by subject-matter experts.
3. Inter-rater agreement: Identify items on which even human evaluators struggle to agree, and revise the rules.
4. Perturbation test: Remove the author name, date, Schema, or evidence links and check whether the result changes in the expected direction.
5. Manipulation-resistance test: Verify that hidden phrases, fake profiles, and duplicate press releases do not raise scores excessively.
6. Regional bias test: Measure whether entities of equivalent quality are systematically disadvantaged by language or country.
7. Result calibration: When using observed citation data, record the question, time, model, and location together.

Generative AI citation results may vary depending on how a question is phrased and on service updates. Therefore, actual citation rates should be treated as time-stamped external validation metrics rather than absolute ground truth.

Data Specification to Include in Production Results

The following structure can be provided so that people and other systems can revalidate the results.

{
  "snapshot_id": "sha256:...",
  "collected_at": "ISO-8601 timestamp",
  "scoring_version": "eeat-1.3.0",
  "scope": "document",
  "topic_class": "software-review",
  "scores": {
    "experience": 72,
    "expertise": 61,
    "authoritativeness": 54,
    "trustworthiness": 80
  },
  "confidence": 0.74,
  "evidence": [
    {
      "dimension": "experience",
      "status": "verified",
      "source_url": "https://example.invalid/page",
      "rule_id": "EXP-METHOD-02"
    }
  ],
  "missing": ["independent_author_profile"]
}

The URL above is an invalid example string intended to demonstrate the data structure. Actual results should include the collected evidence URL and the location in the original text. If there is no right to republish the entire original text, store only brief evidence excerpts within the necessary scope, along with hashes and location information.

Implementation Checklist

• Distinguish evaluation units by document, author, organization, and domain.
• Provide every analyzer with a snapshot from the same point in time.
• Separate self-asserted claims from independent external verification.
• Calculate experience and expertise as separate dimensions.
• Maintain country- and language-specific Authority source registries.
• Check consistency between structured data and visible page content.
• Store missing, conflicting, and excluded items as distinct statuses.
• Publish scores together with evaluation confidence.
• Increment the version whenever weights or rules change.
• Prevent LLM explanations from adding facts outside the evidence.
• Establish manipulation detection and manual-review workflows.
• Record the model, question, and time when observing actual AI citation results.

The key is not to compress E-E-A-T into a single vague impression score. Verifiable evidence, applied rules, uncertainty, and source provenance must be provided together for GEO analysis results to become data that can support operational decisions and long-term comparisons.