{"content_id":"iwjyzvg4t2","slug":"geo-eeat-scoring-system-design","locale":"en","schema_type":"TechArticle","category":"ai_data","category_name":"AI Data","title":"How to Score E-E-A-T Reproducibly in GEO Analysis Tools","summary":"E-E-A-T is neither an official score provided by Google nor a publicly disclosed citation formula used by generative AI. In GEO analysis tools, an LLM should not determine scores arbitrarily. Instead, verifiable signals should be extracted from the same web snapshot, and scores and confidence should be calculated using code with fixed rules and versions.","sponsorship_disclosure":null,"affiliate_disclosure":null,"commerce_disclosure":null,"author":{"name":"Injoys Editorial Team","url":"https://injoys.com/ko/about"},"key_points":["To produce the same result from the same input, the versions of the webpage snapshot, evaluation rules, weights, and data sources must all be fixed.","Experience, Expertise, Authoritativeness, and Trustworthiness should be evaluated using different evidence, with self-assertions kept separate from external verification in particular.","An LLM is better suited to the limited role of explaining the extracted evidence and the results calculated by code, rather than calculating the final score.","E-E-A-T scores and AI citation likelihood scores are related but not identical, so they should be calculated in separate modules.","Do not disclose only the score; also provide the supporting URLs, extracted sentences, decision rules, missing-data status, and evaluation confidence."],"content_markdown":"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.\n\nGoogle’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.\n\n## Why You Should Not Let an LLM Assign Scores Directly\n\nYou 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.\n\n- Scores may vary between runs even with the same content and prompt.\n- If the model or provider changes, it becomes difficult to compare scores with historical results.\n- Hallucinations may occur in which the model infers experience, credentials, or reputation not found on the page.\n- It is difficult to audit the sentences and rules that affected the score.\n- The system may accept a site’s self-description as fact without external verification.\n- Results may change if a long document is truncated or the extraction order changes.\n\nThe recommended architecture is as follows.\n\n```text\nWebsite\n  → Collect a single-point-in-time snapshot\n  → Extract body content, metadata, entities, and claims\n  → Cross-check against external sources\n  → Calculate scores using version-locked rules\n  → Store scores, evidence, and confidence\n  → Have an LLM explain the results in natural language\n```\n\nThis 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.\n\n## Defining the Evaluation Target and Scope First\n\nBefore calculating scores, you must establish what is being evaluated. Signals for the overall site, organization, author, and individual document are not interchangeable.\n\n| Evaluation unit | Key question | Representative evidence |\n|---|---|---|\n| Document | Can this article’s claims and production process be trusted? | Body content, citations, publication date, modification date, experimental data |\n| Author | Is the author’s experience or expertise in this subject verifiable? | Author page, credentials, work history, research and publication history |\n| Organization | Is the publisher identifiable, with a structure of accountability? | About page, contact details, editorial policy, Organization data |\n| Domain | Is it recognized externally as a source on the subject? | Citations by independent institutions, relevant backlinks, media and academic materials |\n| Technical surface | Can crawlers read the content and source information? | Status codes, robots policy, canonical, HTML, structured data |\n\nThe 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.\n\n## How to Collect a Consistent Web Snapshot\n\nThe 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.\n\nWhere possible, store the following items in the snapshot.\n\n- Final URL, redirect path, HTTP status code, and response headers\n- Raw HTML and, where necessary, rendered HTML\n- Extracted body content, title, description, canonical, and language information\n- Author, publication date, modification date, and publishing organization\n- Internal and external links and anchor text\n- Structured data such as JSON-LD and Microdata\n- robots directives and related URLs identified in the sitemap\n- Collection time, collector version, and content hash\n\nWhen 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.\n\n## Separating On-Site and Off-Site Evidence\n\nOn-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.\n\nOff-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.\n\nThe two types of evidence must be distinguished in scoring.\n\n- Self-promotional claims such as “the best in the industry” are not independent evidence of Authority.\n- Even an external article has low independence if it simply republishes a press release.\n- If the same article is duplicated across different URLs, do not count the sources more than once.\n- Verify entities to avoid linking the achievements of a different person or organization with the same name.\n- sameAs links are clues connecting entity candidates; they do not automatically guarantee that the stated experience is true.\n\n## Experience: Turning Firsthand Experience into Evidence\n\nExperience 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.\n\n### Signals to Detect\n\n- Statements indicating firsthand use, purchase, visitation, installation, operation, or comparison\n- Duration of use, number of tests, sample size, environment, and equipment\n- Steps performed, failures, constraints, and exceptions\n- Original images, logs, raw data, and reproduction procedures\n- Before-and-after measurement results and measurement methods\n\nAssigning 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.\n\nExperience scores can be tiered as follows.\n\n| Level | Example | Processing principle |\n|---|---|---|\n| Weak | Only a claim of having used it | Low baseline score |\n| Moderate | Specific duration, environment, and procedures | Add a specificity score |\n| Strong | Provides raw data, photographs, logs, and comparison criteria | Add a verifiability score |\n| Verified | Consistent with independent sources or reproduction tests | Apply a cross-verification weight |\n\n## Expertise: Evaluating Verifiable Expertise\n\nExpertise 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.\n\nPotential items for analysis include:\n\n- Relevant title, affiliation, field of work, and duration of experience\n- Degrees, recognized certifications, licenses, and issuing institutions\n- Relevant research, papers, books, lectures, and projects\n- Actual work experience related to the subject\n- Accuracy, scope, limitations, and sources of professional explanations\n- Expert reviewer and review date\n\nIf 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.\n\nExpertise 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.\n\n## Authoritativeness: External Recognition and Entity Matching\n\nAuthoritativeness 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.\n\nAn authoritativeness analyzer can follow this process.\n\n1. Create canonical entities for the organization name, author name, domain, and brand.\n2. Link verified aliases such as former names, English names, and abbreviations.\n3. Determine whether external documents refer to the same entity.\n4. Evaluate each source’s independence, quality, topical relevance, and recency.\n5. Group press release republications and duplicate copies of the same document.\n6. Distinguish whether a citation or mention represents positive recognition, a simple listing, or criticism.\n\nIf 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.\n\nIt 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.\n\n## Trustworthiness: The Broadest and Most Important Safety Dimension\n\nTrustworthiness 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.\n\nThe trustworthiness module should review the following items.\n\n### Publisher and Accountability\n\n- Whether the operating organization and author are clearly identified\n- Whether contact methods and customer support information exist on actual pages\n- Whether editorial, review, and correction policies can be verified\n- Whether advertising, sponsorships, affiliate relationships, and conflicts of interest are distinguished\n\n### Claims and Evidence\n\n- Whether key facts are linked to original sources or appropriate evidence\n- Whether quotations and statistics are consistent with the intent of the original source\n- Whether publication and modification dates correspond to actual content changes\n- Whether facts, opinions, and promotional claims are clearly distinguished\n- Whether uncertainty, scope of application, exceptions, and limitations are disclosed\n\n### Transactions and Safety\n\n- Whether the privacy policy and terms of use are appropriate for the nature of the service\n- Whether payment, refund, and shipping conditions are clear on sites where they are needed\n- Whether technical risks such as insecure HTTP, malicious redirects, or broken certificates are absent\n- Whether health, financial, and legal information avoids dangerous categorical claims or guarantees\n\nThe 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.\n\n## Structured Data and Entity Verification\n\nPerson 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.\n\nHowever, Schema markup should be evaluated according to the following principles.\n\n- Verify that structured data matches the content visible on the page.\n- Do not treat credentials, awards, or reputation as established facts merely because they are present.\n- Check whether identifiers and relationships among Person, Organization, and Article are consistent.\n- Verify that sameAs targets are actual official profiles.\n- Distinguish syntax errors from missing required or recommended properties.\n- Do not award extra points for excessive or irrelevant use of Schema types.\n\nStructured data is a presentation layer that assists understanding and extraction. It does not guarantee high search rankings or citations by generative AI.\n\n## Designing a Reproducible Scoring Formula\n\nIf 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.\n\n- `presence`: Whether the signal exists or the degree to which it is satisfied\n- `verification`: The degree to which it has been independently verified\n- `relevance`: Relevance to the evaluated topic\n- `source_quality`: Quality and independence of the supporting source\n- `freshness`: Validity of signals for which recency matters\n- `weight`: Signal importance managed by version\n\nAn example formula is as follows.\n\n```text\nSignal contribution = weight × presence × verification × relevance × source_quality × freshness\nDimension score = 100 × sum of contributions ÷ sum of applicable weights\n```\n\nThis 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`.\n\n### Displaying Confidence Separately from the Score\n\nEven 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.\n\n```text\nEvaluation confidence = collection coverage × entity match confidence × proportion of verifiable evidence\n```\n\nAt a minimum, the results screen should display the following together.\n\n- Scores by dimension and overall score\n- Evaluation confidence\n- Verified, unverified, conflicting, and excluded statuses\n- Evidence URLs and excerpts from the original text\n- Collection time and snapshot hash\n- Rule, weight, and source registry versions\n\n## Separating the Overall GEO Score from E-E-A-T\n\nHigh 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.\n\nA GEO tool is better organized into separate modules as follows.\n\n| Module | Analysis target |\n|---|---|\n| Content E-E-A-T | Experience, expertise, external authority, trustworthiness |\n| AI citation readiness | Independently understandable sentences, question relevance, evidence linkage, summarizability |\n| Brand authority | Independent recognition by external institutions and entity matching |\n| Technical accessibility | Crawling, status codes, canonical, rendering, body-content access |\n| Schema quality | Syntax, consistency with displayed content, entity relationships |\n\nFor 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.\n\nAI 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.\n\n## An Architecture That Prevents Error Propagation Between Modules\n\nIf 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.\n\n```text\nSnapshot Store\n ├─ Content / Claim Analyzer\n ├─ Author / Expertise Analyzer\n ├─ Entity / Authority Analyzer\n ├─ Trust Analyzer\n ├─ Technical Analyzer\n └─ Schema Analyzer\n          ↓\n Evidence Store → Deterministic Scorer → Explanation LLM\n```\n\nEach 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.\n\n## Commonly Overlooked Problems: Uncertainty and Adversarial Manipulation\n\nMany 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.\n\n- Experience and keywords may be repeatedly inserted into invisible areas.\n- Fake authors and Person structured data may be created.\n- The same press release may be distributed across multiple domains to inflate external mention counts.\n- Nonexistent research, credentials, or figures may be cited.\n- Only the modification date may be updated to present an old article as current content.\n- The system may be induced to incorrectly associate the authority of a person with the same name or a similar brand.\n\nCountermeasures 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.\n\n## How to Validate the Evaluation Model\n\nEven if a scoring formula is deterministic, that does not automatically make the evaluation valid. The following tests are necessary.\n\n1. **Repeatability test:** Verify that the same snapshot and version produce bit-for-bit identical results.\n2. **Expert benchmark set:** Compare system results with evidence independently labeled by subject-matter experts.\n3. **Inter-rater agreement:** Identify items on which even human evaluators struggle to agree, and revise the rules.\n4. **Perturbation test:** Remove the author name, date, Schema, or evidence links and check whether the result changes in the expected direction.\n5. **Manipulation-resistance test:** Verify that hidden phrases, fake profiles, and duplicate press releases do not raise scores excessively.\n6. **Regional bias test:** Measure whether entities of equivalent quality are systematically disadvantaged by language or country.\n7. **Result calibration:** When using observed citation data, record the question, time, model, and location together.\n\nGenerative 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.\n\n## Data Specification to Include in Production Results\n\nThe following structure can be provided so that people and other systems can revalidate the results.\n\n```json\n{\n  \"snapshot_id\": \"sha256:...\",\n  \"collected_at\": \"ISO-8601 timestamp\",\n  \"scoring_version\": \"eeat-1.3.0\",\n  \"scope\": \"document\",\n  \"topic_class\": \"software-review\",\n  \"scores\": {\n    \"experience\": 72,\n    \"expertise\": 61,\n    \"authoritativeness\": 54,\n    \"trustworthiness\": 80\n  },\n  \"confidence\": 0.74,\n  \"evidence\": [\n    {\n      \"dimension\": \"experience\",\n      \"status\": \"verified\",\n      \"source_url\": \"https://example.invalid/page\",\n      \"rule_id\": \"EXP-METHOD-02\"\n    }\n  ],\n  \"missing\": [\"independent_author_profile\"]\n}\n```\n\nThe 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.\n\n## Implementation Checklist\n\n- Distinguish evaluation units by document, author, organization, and domain.\n- Provide every analyzer with a snapshot from the same point in time.\n- Separate self-asserted claims from independent external verification.\n- Calculate experience and expertise as separate dimensions.\n- Maintain country- and language-specific Authority source registries.\n- Check consistency between structured data and visible page content.\n- Store missing, conflicting, and excluded items as distinct statuses.\n- Publish scores together with evaluation confidence.\n- Increment the version whenever weights or rules change.\n- Prevent LLM explanations from adding facts outside the evidence.\n- Establish manipulation detection and manual-review workflows.\n- Record the model, question, and time when observing actual AI citation results.\n\nThe 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.","content_html":"\u003cp\u003eGEO (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.\u003c/p\u003e\n\u003cp\u003eGoogle’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.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#why-you-should-not-let-an-llm-assign-scores-directly\" class=\"anchor\" id=\"why-you-should-not-let-an-llm-assign-scores-directly\"\u003e\u003c/a\u003eWhy You Should Not Let an LLM Assign Scores Directly\u003c/h2\u003e\n\u003cp\u003eYou 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.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eScores may vary between runs even with the same content and prompt.\u003c/li\u003e\n\u003cli\u003eIf the model or provider changes, it becomes difficult to compare scores with historical results.\u003c/li\u003e\n\u003cli\u003eHallucinations may occur in which the model infers experience, credentials, or reputation not found on the page.\u003c/li\u003e\n\u003cli\u003eIt is difficult to audit the sentences and rules that affected the score.\u003c/li\u003e\n\u003cli\u003eThe system may accept a site’s self-description as fact without external verification.\u003c/li\u003e\n\u003cli\u003eResults may change if a long document is truncated or the extraction order changes.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe recommended architecture is as follows.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u003cspan\u003eWebsite\n\u003c/span\u003e\u003cspan\u003e  → Collect a single-point-in-time snapshot\n\u003c/span\u003e\u003cspan\u003e  → Extract body content, metadata, entities, and claims\n\u003c/span\u003e\u003cspan\u003e  → Cross-check against external sources\n\u003c/span\u003e\u003cspan\u003e  → Calculate scores using version-locked rules\n\u003c/span\u003e\u003cspan\u003e  → Store scores, evidence, and confidence\n\u003c/span\u003e\u003cspan\u003e  → Have an LLM explain the results in natural language\n\u003c/span\u003e\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis 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.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#defining-the-evaluation-target-and-scope-first\" class=\"anchor\" id=\"defining-the-evaluation-target-and-scope-first\"\u003e\u003c/a\u003eDefining the Evaluation Target and Scope First\u003c/h2\u003e\n\u003cp\u003eBefore calculating scores, you must establish what is being evaluated. Signals for the overall site, organization, author, and individual document are not interchangeable.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eEvaluation unit\u003c/th\u003e\n\u003cth\u003eKey question\u003c/th\u003e\n\u003cth\u003eRepresentative evidence\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Evaluation unit\"\u003eDocument\u003c/td\u003e\n\u003ctd data-label=\"Key question\"\u003eCan this article’s claims and production process be trusted?\u003c/td\u003e\n\u003ctd data-label=\"Representative evidence\"\u003eBody content, citations, publication date, modification date, experimental data\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Evaluation unit\"\u003eAuthor\u003c/td\u003e\n\u003ctd data-label=\"Key question\"\u003eIs the author’s experience or expertise in this subject verifiable?\u003c/td\u003e\n\u003ctd data-label=\"Representative evidence\"\u003eAuthor page, credentials, work history, research and publication history\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Evaluation unit\"\u003eOrganization\u003c/td\u003e\n\u003ctd data-label=\"Key question\"\u003eIs the publisher identifiable, with a structure of accountability?\u003c/td\u003e\n\u003ctd data-label=\"Representative evidence\"\u003eAbout page, contact details, editorial policy, Organization data\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Evaluation unit\"\u003eDomain\u003c/td\u003e\n\u003ctd data-label=\"Key question\"\u003eIs it recognized externally as a source on the subject?\u003c/td\u003e\n\u003ctd data-label=\"Representative evidence\"\u003eCitations by independent institutions, relevant backlinks, media and academic materials\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Evaluation unit\"\u003eTechnical surface\u003c/td\u003e\n\u003ctd data-label=\"Key question\"\u003eCan crawlers read the content and source information?\u003c/td\u003e\n\u003ctd data-label=\"Representative evidence\"\u003eStatus codes, robots policy, canonical, HTML, structured data\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#how-to-collect-a-consistent-web-snapshot\" class=\"anchor\" id=\"how-to-collect-a-consistent-web-snapshot\"\u003e\u003c/a\u003eHow to Collect a Consistent Web Snapshot\u003c/h2\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003eWhere possible, store the following items in the snapshot.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFinal URL, redirect path, HTTP status code, and response headers\u003c/li\u003e\n\u003cli\u003eRaw HTML and, where necessary, rendered HTML\u003c/li\u003e\n\u003cli\u003eExtracted body content, title, description, canonical, and language information\u003c/li\u003e\n\u003cli\u003eAuthor, publication date, modification date, and publishing organization\u003c/li\u003e\n\u003cli\u003eInternal and external links and anchor text\u003c/li\u003e\n\u003cli\u003eStructured data such as JSON-LD and Microdata\u003c/li\u003e\n\u003cli\u003erobots directives and related URLs identified in the sitemap\u003c/li\u003e\n\u003cli\u003eCollection time, collector version, and content hash\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWhen 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.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#separating-on-site-and-off-site-evidence\" class=\"anchor\" id=\"separating-on-site-and-off-site-evidence\"\u003e\u003c/a\u003eSeparating On-Site and Off-Site Evidence\u003c/h2\u003e\n\u003cp\u003eOn-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.\u003c/p\u003e\n\u003cp\u003eOff-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.\u003c/p\u003e\n\u003cp\u003eThe two types of evidence must be distinguished in scoring.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSelf-promotional claims such as “the best in the industry” are not independent evidence of Authority.\u003c/li\u003e\n\u003cli\u003eEven an external article has low independence if it simply republishes a press release.\u003c/li\u003e\n\u003cli\u003eIf the same article is duplicated across different URLs, do not count the sources more than once.\u003c/li\u003e\n\u003cli\u003eVerify entities to avoid linking the achievements of a different person or organization with the same name.\u003c/li\u003e\n\u003cli\u003esameAs links are clues connecting entity candidates; they do not automatically guarantee that the stated experience is true.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca href=\"#experience-turning-firsthand-experience-into-evidence\" class=\"anchor\" id=\"experience-turning-firsthand-experience-into-evidence\"\u003e\u003c/a\u003eExperience: Turning Firsthand Experience into Evidence\u003c/h2\u003e\n\u003cp\u003eExperience 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.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#signals-to-detect\" class=\"anchor\" id=\"signals-to-detect\"\u003e\u003c/a\u003eSignals to Detect\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eStatements indicating firsthand use, purchase, visitation, installation, operation, or comparison\u003c/li\u003e\n\u003cli\u003eDuration of use, number of tests, sample size, environment, and equipment\u003c/li\u003e\n\u003cli\u003eSteps performed, failures, constraints, and exceptions\u003c/li\u003e\n\u003cli\u003eOriginal images, logs, raw data, and reproduction procedures\u003c/li\u003e\n\u003cli\u003eBefore-and-after measurement results and measurement methods\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAssigning 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.\u003c/p\u003e\n\u003cp\u003eExperience scores can be tiered as follows.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eLevel\u003c/th\u003e\n\u003cth\u003eExample\u003c/th\u003e\n\u003cth\u003eProcessing principle\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Level\"\u003eWeak\u003c/td\u003e\n\u003ctd data-label=\"Example\"\u003eOnly a claim of having used it\u003c/td\u003e\n\u003ctd data-label=\"Processing principle\"\u003eLow baseline score\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Level\"\u003eModerate\u003c/td\u003e\n\u003ctd data-label=\"Example\"\u003eSpecific duration, environment, and procedures\u003c/td\u003e\n\u003ctd data-label=\"Processing principle\"\u003eAdd a specificity score\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Level\"\u003eStrong\u003c/td\u003e\n\u003ctd data-label=\"Example\"\u003eProvides raw data, photographs, logs, and comparison criteria\u003c/td\u003e\n\u003ctd data-label=\"Processing principle\"\u003eAdd a verifiability score\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Level\"\u003eVerified\u003c/td\u003e\n\u003ctd data-label=\"Example\"\u003eConsistent with independent sources or reproduction tests\u003c/td\u003e\n\u003ctd data-label=\"Processing principle\"\u003eApply a cross-verification weight\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca href=\"#expertise-evaluating-verifiable-expertise\" class=\"anchor\" id=\"expertise-evaluating-verifiable-expertise\"\u003e\u003c/a\u003eExpertise: Evaluating Verifiable Expertise\u003c/h2\u003e\n\u003cp\u003eExpertise 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.\u003c/p\u003e\n\u003cp\u003ePotential items for analysis include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelevant title, affiliation, field of work, and duration of experience\u003c/li\u003e\n\u003cli\u003eDegrees, recognized certifications, licenses, and issuing institutions\u003c/li\u003e\n\u003cli\u003eRelevant research, papers, books, lectures, and projects\u003c/li\u003e\n\u003cli\u003eActual work experience related to the subject\u003c/li\u003e\n\u003cli\u003eAccuracy, scope, limitations, and sources of professional explanations\u003c/li\u003e\n\u003cli\u003eExpert reviewer and review date\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIf the author value is a role name such as \u003ccode\u003eadmin\u003c/code\u003e, \u003ccode\u003eadministrator\u003c/code\u003e, \u003ccode\u003e관리자\u003c/code\u003e, \u003ccode\u003e운영자\u003c/code\u003e, \u003ccode\u003e운영팀\u003c/code\u003e, or \u003ccode\u003eeditor\u003c/code\u003e, 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.\u003c/p\u003e\n\u003cp\u003eExpertise 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.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#authoritativeness-external-recognition-and-entity-matching\" class=\"anchor\" id=\"authoritativeness-external-recognition-and-entity-matching\"\u003e\u003c/a\u003eAuthoritativeness: External Recognition and Entity Matching\u003c/h2\u003e\n\u003cp\u003eAuthoritativeness 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.\u003c/p\u003e\n\u003cp\u003eAn authoritativeness analyzer can follow this process.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCreate canonical entities for the organization name, author name, domain, and brand.\u003c/li\u003e\n\u003cli\u003eLink verified aliases such as former names, English names, and abbreviations.\u003c/li\u003e\n\u003cli\u003eDetermine whether external documents refer to the same entity.\u003c/li\u003e\n\u003cli\u003eEvaluate each source’s independence, quality, topical relevance, and recency.\u003c/li\u003e\n\u003cli\u003eGroup press release republications and duplicate copies of the same document.\u003c/li\u003e\n\u003cli\u003eDistinguish whether a citation or mention represents positive recognition, a simple listing, or criticism.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIf 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.\u003c/p\u003e\n\u003cp\u003eIt 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.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#trustworthiness-the-broadest-and-most-important-safety-dimension\" class=\"anchor\" id=\"trustworthiness-the-broadest-and-most-important-safety-dimension\"\u003e\u003c/a\u003eTrustworthiness: The Broadest and Most Important Safety Dimension\u003c/h2\u003e\n\u003cp\u003eTrustworthiness 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.\u003c/p\u003e\n\u003cp\u003eThe trustworthiness module should review the following items.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#publisher-and-accountability\" class=\"anchor\" id=\"publisher-and-accountability\"\u003e\u003c/a\u003ePublisher and Accountability\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eWhether the operating organization and author are clearly identified\u003c/li\u003e\n\u003cli\u003eWhether contact methods and customer support information exist on actual pages\u003c/li\u003e\n\u003cli\u003eWhether editorial, review, and correction policies can be verified\u003c/li\u003e\n\u003cli\u003eWhether advertising, sponsorships, affiliate relationships, and conflicts of interest are distinguished\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca href=\"#claims-and-evidence\" class=\"anchor\" id=\"claims-and-evidence\"\u003e\u003c/a\u003eClaims and Evidence\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eWhether key facts are linked to original sources or appropriate evidence\u003c/li\u003e\n\u003cli\u003eWhether quotations and statistics are consistent with the intent of the original source\u003c/li\u003e\n\u003cli\u003eWhether publication and modification dates correspond to actual content changes\u003c/li\u003e\n\u003cli\u003eWhether facts, opinions, and promotional claims are clearly distinguished\u003c/li\u003e\n\u003cli\u003eWhether uncertainty, scope of application, exceptions, and limitations are disclosed\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca href=\"#transactions-and-safety\" class=\"anchor\" id=\"transactions-and-safety\"\u003e\u003c/a\u003eTransactions and Safety\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eWhether the privacy policy and terms of use are appropriate for the nature of the service\u003c/li\u003e\n\u003cli\u003eWhether payment, refund, and shipping conditions are clear on sites where they are needed\u003c/li\u003e\n\u003cli\u003eWhether technical risks such as insecure HTTP, malicious redirects, or broken certificates are absent\u003c/li\u003e\n\u003cli\u003eWhether health, financial, and legal information avoids dangerous categorical claims or guarantees\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#structured-data-and-entity-verification\" class=\"anchor\" id=\"structured-data-and-entity-verification\"\u003e\u003c/a\u003eStructured Data and Entity Verification\u003c/h2\u003e\n\u003cp\u003ePerson 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.\u003c/p\u003e\n\u003cp\u003eHowever, Schema markup should be evaluated according to the following principles.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eVerify that structured data matches the content visible on the page.\u003c/li\u003e\n\u003cli\u003eDo not treat credentials, awards, or reputation as established facts merely because they are present.\u003c/li\u003e\n\u003cli\u003eCheck whether identifiers and relationships among Person, Organization, and Article are consistent.\u003c/li\u003e\n\u003cli\u003eVerify that sameAs targets are actual official profiles.\u003c/li\u003e\n\u003cli\u003eDistinguish syntax errors from missing required or recommended properties.\u003c/li\u003e\n\u003cli\u003eDo not award extra points for excessive or irrelevant use of Schema types.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eStructured data is a presentation layer that assists understanding and extraction. It does not guarantee high search rankings or citations by generative AI.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#designing-a-reproducible-scoring-formula\" class=\"anchor\" id=\"designing-a-reproducible-scoring-formula\"\u003e\u003c/a\u003eDesigning a Reproducible Scoring Formula\u003c/h2\u003e\n\u003cp\u003eIf 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.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ccode\u003epresence\u003c/code\u003e: Whether the signal exists or the degree to which it is satisfied\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003everification\u003c/code\u003e: The degree to which it has been independently verified\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003erelevance\u003c/code\u003e: Relevance to the evaluated topic\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003esource_quality\u003c/code\u003e: Quality and independence of the supporting source\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003efreshness\u003c/code\u003e: Validity of signals for which recency matters\u003c/li\u003e\n\u003cli\u003e\n\u003ccode\u003eweight\u003c/code\u003e: Signal importance managed by version\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAn example formula is as follows.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u003cspan\u003eSignal contribution = weight × presence × verification × relevance × source_quality × freshness\n\u003c/span\u003e\u003cspan\u003eDimension score = 100 × sum of contributions ÷ sum of applicable weights\n\u003c/span\u003e\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThis 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 \u003ccode\u003eN/A\u003c/code\u003e, while a state in which required evidence was not found should be distinguished from a score of 0 and stored as \u003ccode\u003emissing\u003c/code\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#displaying-confidence-separately-from-the-score\" class=\"anchor\" id=\"displaying-confidence-separately-from-the-score\"\u003e\u003c/a\u003eDisplaying Confidence Separately from the Score\u003c/h3\u003e\n\u003cp\u003eEven 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.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u003cspan\u003eEvaluation confidence = collection coverage × entity match confidence × proportion of verifiable evidence\n\u003c/span\u003e\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eAt a minimum, the results screen should display the following together.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eScores by dimension and overall score\u003c/li\u003e\n\u003cli\u003eEvaluation confidence\u003c/li\u003e\n\u003cli\u003eVerified, unverified, conflicting, and excluded statuses\u003c/li\u003e\n\u003cli\u003eEvidence URLs and excerpts from the original text\u003c/li\u003e\n\u003cli\u003eCollection time and snapshot hash\u003c/li\u003e\n\u003cli\u003eRule, weight, and source registry versions\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca href=\"#separating-the-overall-geo-score-from-e-e-a-t\" class=\"anchor\" id=\"separating-the-overall-geo-score-from-e-e-a-t\"\u003e\u003c/a\u003eSeparating the Overall GEO Score from E-E-A-T\u003c/h2\u003e\n\u003cp\u003eHigh 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.\u003c/p\u003e\n\u003cp\u003eA GEO tool is better organized into separate modules as follows.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eModule\u003c/th\u003e\n\u003cth\u003eAnalysis target\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Module\"\u003eContent E-E-A-T\u003c/td\u003e\n\u003ctd data-label=\"Analysis target\"\u003eExperience, expertise, external authority, trustworthiness\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Module\"\u003eAI citation readiness\u003c/td\u003e\n\u003ctd data-label=\"Analysis target\"\u003eIndependently understandable sentences, question relevance, evidence linkage, summarizability\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Module\"\u003eBrand authority\u003c/td\u003e\n\u003ctd data-label=\"Analysis target\"\u003eIndependent recognition by external institutions and entity matching\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Module\"\u003eTechnical accessibility\u003c/td\u003e\n\u003ctd data-label=\"Analysis target\"\u003eCrawling, status codes, canonical, rendering, body-content access\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Module\"\u003eSchema quality\u003c/td\u003e\n\u003ctd data-label=\"Analysis target\"\u003eSyntax, consistency with displayed content, entity relationships\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eFor 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.\u003c/p\u003e\n\u003cp\u003eAI 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.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#an-architecture-that-prevents-error-propagation-between-modules\" class=\"anchor\" id=\"an-architecture-that-prevents-error-propagation-between-modules\"\u003e\u003c/a\u003eAn Architecture That Prevents Error Propagation Between Modules\u003c/h2\u003e\n\u003cp\u003eIf 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.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u003cspan\u003eSnapshot Store\n\u003c/span\u003e\u003cspan\u003e ├─ Content / Claim Analyzer\n\u003c/span\u003e\u003cspan\u003e ├─ Author / Expertise Analyzer\n\u003c/span\u003e\u003cspan\u003e ├─ Entity / Authority Analyzer\n\u003c/span\u003e\u003cspan\u003e ├─ Trust Analyzer\n\u003c/span\u003e\u003cspan\u003e ├─ Technical Analyzer\n\u003c/span\u003e\u003cspan\u003e └─ Schema Analyzer\n\u003c/span\u003e\u003cspan\u003e          ↓\n\u003c/span\u003e\u003cspan\u003e Evidence Store → Deterministic Scorer → Explanation LLM\n\u003c/span\u003e\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eEach judgment record should include \u003ccode\u003eclaim_id\u003c/code\u003e, \u003ccode\u003eevidence_id\u003c/code\u003e, 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.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#commonly-overlooked-problems-uncertainty-and-adversarial-manipulation\" class=\"anchor\" id=\"commonly-overlooked-problems-uncertainty-and-adversarial-manipulation\"\u003e\u003c/a\u003eCommonly Overlooked Problems: Uncertainty and Adversarial Manipulation\u003c/h2\u003e\n\u003cp\u003eMany 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.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eExperience and keywords may be repeatedly inserted into invisible areas.\u003c/li\u003e\n\u003cli\u003eFake authors and Person structured data may be created.\u003c/li\u003e\n\u003cli\u003eThe same press release may be distributed across multiple domains to inflate external mention counts.\u003c/li\u003e\n\u003cli\u003eNonexistent research, credentials, or figures may be cited.\u003c/li\u003e\n\u003cli\u003eOnly the modification date may be updated to present an old article as current content.\u003c/li\u003e\n\u003cli\u003eThe system may be induced to incorrectly associate the authority of a person with the same name or a similar brand.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCountermeasures 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.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#how-to-validate-the-evaluation-model\" class=\"anchor\" id=\"how-to-validate-the-evaluation-model\"\u003e\u003c/a\u003eHow to Validate the Evaluation Model\u003c/h2\u003e\n\u003cp\u003eEven if a scoring formula is deterministic, that does not automatically make the evaluation valid. The following tests are necessary.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eRepeatability test:\u003c/strong\u003e Verify that the same snapshot and version produce bit-for-bit identical results.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eExpert benchmark set:\u003c/strong\u003e Compare system results with evidence independently labeled by subject-matter experts.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eInter-rater agreement:\u003c/strong\u003e Identify items on which even human evaluators struggle to agree, and revise the rules.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePerturbation test:\u003c/strong\u003e Remove the author name, date, Schema, or evidence links and check whether the result changes in the expected direction.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eManipulation-resistance test:\u003c/strong\u003e Verify that hidden phrases, fake profiles, and duplicate press releases do not raise scores excessively.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eRegional bias test:\u003c/strong\u003e Measure whether entities of equivalent quality are systematically disadvantaged by language or country.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eResult calibration:\u003c/strong\u003e When using observed citation data, record the question, time, model, and location together.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eGenerative 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.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#data-specification-to-include-in-production-results\" class=\"anchor\" id=\"data-specification-to-include-in-production-results\"\u003e\u003c/a\u003eData Specification to Include in Production Results\u003c/h2\u003e\n\u003cp\u003eThe following structure can be provided so that people and other systems can revalidate the results.\u003c/p\u003e\n\u003cpre\u003e\u003ccode\u003e\u003cspan\u003e{\n\u003c/span\u003e\u003cspan\u003e  \"\u003c/span\u003e\u003cspan\u003esnapshot_id\u003c/span\u003e\u003cspan\u003e\": \"\u003c/span\u003e\u003cspan\u003esha256:...\u003c/span\u003e\u003cspan\u003e\",\n\u003c/span\u003e\u003cspan\u003e  \"\u003c/span\u003e\u003cspan\u003ecollected_at\u003c/span\u003e\u003cspan\u003e\": \"\u003c/span\u003e\u003cspan\u003eISO-8601 timestamp\u003c/span\u003e\u003cspan\u003e\",\n\u003c/span\u003e\u003cspan\u003e  \"\u003c/span\u003e\u003cspan\u003escoring_version\u003c/span\u003e\u003cspan\u003e\": \"\u003c/span\u003e\u003cspan\u003eeeat-1.3.0\u003c/span\u003e\u003cspan\u003e\",\n\u003c/span\u003e\u003cspan\u003e  \"\u003c/span\u003e\u003cspan\u003escope\u003c/span\u003e\u003cspan\u003e\": \"\u003c/span\u003e\u003cspan\u003edocument\u003c/span\u003e\u003cspan\u003e\",\n\u003c/span\u003e\u003cspan\u003e  \"\u003c/span\u003e\u003cspan\u003etopic_class\u003c/span\u003e\u003cspan\u003e\": \"\u003c/span\u003e\u003cspan\u003esoftware-review\u003c/span\u003e\u003cspan\u003e\",\n\u003c/span\u003e\u003cspan\u003e  \"\u003c/span\u003e\u003cspan\u003escores\u003c/span\u003e\u003cspan\u003e\": {\n\u003c/span\u003e\u003cspan\u003e    \"\u003c/span\u003e\u003cspan\u003eexperience\u003c/span\u003e\u003cspan\u003e\": \u003c/span\u003e\u003cspan\u003e72\u003c/span\u003e\u003cspan\u003e,\n\u003c/span\u003e\u003cspan\u003e    \"\u003c/span\u003e\u003cspan\u003eexpertise\u003c/span\u003e\u003cspan\u003e\": \u003c/span\u003e\u003cspan\u003e61\u003c/span\u003e\u003cspan\u003e,\n\u003c/span\u003e\u003cspan\u003e    \"\u003c/span\u003e\u003cspan\u003eauthoritativeness\u003c/span\u003e\u003cspan\u003e\": \u003c/span\u003e\u003cspan\u003e54\u003c/span\u003e\u003cspan\u003e,\n\u003c/span\u003e\u003cspan\u003e    \"\u003c/span\u003e\u003cspan\u003etrustworthiness\u003c/span\u003e\u003cspan\u003e\": \u003c/span\u003e\u003cspan\u003e80\n\u003c/span\u003e\u003cspan\u003e  },\n\u003c/span\u003e\u003cspan\u003e  \"\u003c/span\u003e\u003cspan\u003econfidence\u003c/span\u003e\u003cspan\u003e\": \u003c/span\u003e\u003cspan\u003e0.74\u003c/span\u003e\u003cspan\u003e,\n\u003c/span\u003e\u003cspan\u003e  \"\u003c/span\u003e\u003cspan\u003eevidence\u003c/span\u003e\u003cspan\u003e\": [\n\u003c/span\u003e\u003cspan\u003e    {\n\u003c/span\u003e\u003cspan\u003e      \"\u003c/span\u003e\u003cspan\u003edimension\u003c/span\u003e\u003cspan\u003e\": \"\u003c/span\u003e\u003cspan\u003eexperience\u003c/span\u003e\u003cspan\u003e\",\n\u003c/span\u003e\u003cspan\u003e      \"\u003c/span\u003e\u003cspan\u003estatus\u003c/span\u003e\u003cspan\u003e\": \"\u003c/span\u003e\u003cspan\u003everified\u003c/span\u003e\u003cspan\u003e\",\n\u003c/span\u003e\u003cspan\u003e      \"\u003c/span\u003e\u003cspan\u003esource_url\u003c/span\u003e\u003cspan\u003e\": \"\u003c/span\u003e\u003cspan\u003ehttps://example.invalid/page\u003c/span\u003e\u003cspan\u003e\",\n\u003c/span\u003e\u003cspan\u003e      \"\u003c/span\u003e\u003cspan\u003erule_id\u003c/span\u003e\u003cspan\u003e\": \"\u003c/span\u003e\u003cspan\u003eEXP-METHOD-02\u003c/span\u003e\u003cspan\u003e\"\n\u003c/span\u003e\u003cspan\u003e    }\n\u003c/span\u003e\u003cspan\u003e  ],\n\u003c/span\u003e\u003cspan\u003e  \"\u003c/span\u003e\u003cspan\u003emissing\u003c/span\u003e\u003cspan\u003e\": [\"\u003c/span\u003e\u003cspan\u003eindependent_author_profile\u003c/span\u003e\u003cspan\u003e\"]\n\u003c/span\u003e\u003cspan\u003e}\n\u003c/span\u003e\u003c/code\u003e\u003c/pre\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#implementation-checklist\" class=\"anchor\" id=\"implementation-checklist\"\u003e\u003c/a\u003eImplementation Checklist\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDistinguish evaluation units by document, author, organization, and domain.\u003c/li\u003e\n\u003cli\u003eProvide every analyzer with a snapshot from the same point in time.\u003c/li\u003e\n\u003cli\u003eSeparate self-asserted claims from independent external verification.\u003c/li\u003e\n\u003cli\u003eCalculate experience and expertise as separate dimensions.\u003c/li\u003e\n\u003cli\u003eMaintain country- and language-specific Authority source registries.\u003c/li\u003e\n\u003cli\u003eCheck consistency between structured data and visible page content.\u003c/li\u003e\n\u003cli\u003eStore missing, conflicting, and excluded items as distinct statuses.\u003c/li\u003e\n\u003cli\u003ePublish scores together with evaluation confidence.\u003c/li\u003e\n\u003cli\u003eIncrement the version whenever weights or rules change.\u003c/li\u003e\n\u003cli\u003ePrevent LLM explanations from adding facts outside the evidence.\u003c/li\u003e\n\u003cli\u003eEstablish manipulation detection and manual-review workflows.\u003c/li\u003e\n\u003cli\u003eRecord the model, question, and time when observing actual AI citation results.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n","tags":["AI","Generative AI","Personal data protection","AI Development","Technology strategy","GEO"],"faqs":[{"question":"Is the E-E-A-T score an official metric provided by Google?","answer":"No. Google describes E-E-A-T as a concept for understanding content quality, but it does not publish an official score for each webpage. An analysis tool's E-E-A-T score is a proprietary metric based on the signals, weights, and data scope defined by that tool."},{"question":"Can I have an LLM score content directly if I set its temperature to 0?","answer":"This can reduce variability, but it does not guarantee complete reproducibility or factual accuracy. The model version, input truncation, and system settings may vary, and there is also a risk of inferring facts that are not on the page, so it is safer to perform the final calculation using version-locked code."},{"question":"Why should Experience and Expertise be evaluated separately?","answer":"Experience means having directly used, visited, tested, or operated something, while Expertise means having relevant knowledge, work experience, qualifications, and research capabilities. A reviewer with extensive hands-on experience is not necessarily a technical expert, and someone with professional qualifications may not have used the product directly."},{"question":"Can the Experience score be increased if the author says they used it directly?","answer":"It is best to treat self-declaration alone as a weak signal. A higher weight should be applied when it is accompanied by specific, verifiable evidence, such as the duration, environment, procedures performed, measurement methods, raw data, or original materials created by the author."},{"question":"Is Authority low if there are no Wikipedia pages or news articles?","answer":"Not necessarily. Important external sources vary by country and industry. Independent sources appropriate to the region and field, such as government and public agencies, academic institutions, professional associations, and industry media, should also be evaluated, and republished press releases should be deduplicated."},{"question":"Does adding a lot of Schema increase the GEO score?","answer":"Accuracy and consistency with the displayed content are more important than the amount of Schema. Structured data helps systems understand entities and document relationships, but it does not guarantee search visibility or AI citations, and false or irrelevant markup can pose a trust risk."},{"question":"Does a high E-E-A-T score guarantee citations in ChatGPT or Gemini?","answer":"No. Each service's source selection methods are not fully disclosed, and various factors may affect selection, including relevance to the query, recency, accessibility, and presentation structure. E-E-A-T and AI citation readiness may be related, but they should be managed as separate scores."},{"question":"If the scores are the same, can the analysis results be considered to be of the same quality?","answer":"No. A score of 80 based on collecting a sufficient number of pages has a different meaning from a score of 80 with half the data missing. Along with the score, the evaluation confidence should be shown, reflecting collection coverage, entity match quality, and the proportion of verified evidence."},{"question":"How should the weights for a GEO score be determined?","answer":"Initial weights should be set as an explicit product policy, then calibrated using an expert benchmark set, perturbation tests, manipulation-resistance tests, and actual observational data. Different profiles are needed for fields with different levels of topic risk and user intent, such as healthcare, finance, and product reviews."},{"question":"What roles are LLMs suited for in a GEO analysis system?","answer":"They can be used to extract potential claims, classify sentences, identify name variants, and provide natural-language explanations of results calculated by code. However, it is best to link every result to evidence in the source text and have deterministic code handle the final score calculation and cap and penalty rules."}],"sources":[{"url":"https://developers.google.com/search/docs/fundamentals/creating-helpful-content","title":"Creating helpful, reliable, people-first content","type":"source"},{"url":"https://developers.google.com/search/docs/appearance/ai-features","title":"AI features and your website","type":"source"},{"url":"https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data","title":"Introduction to structured data markup in Google Search","type":"source"},{"url":"https://developers.google.com/search/docs/crawling-indexing/sitemaps/overview","title":"Learn about sitemaps","type":"source"},{"url":"https://schema.org/Person","title":"Schema.org Person","type":"source"},{"url":"https://schema.org/Organization","title":"Schema.org Organization","type":"source"},{"url":"https://www.w3.org/TR/prov-o/","title":"PROV-O: The PROV Ontology","type":"source"},{"url":"https://arxiv.org/abs/2311.09735","title":"GEO: Generative Engine Optimization","type":"source"}],"images":[{"id":902,"url":"https://injoys.com/rails/active_storage/blobs/proxy/eyJfcmFpbHMiOnsiZGF0YSI6MTIwOTYsInB1ciI6ImJsb2JfaWQifX0=--085c51ba6ec88e1b1f3d7efa109fae3f43e51b14/ai-9f49d198.webp","is_representative":true,"generation_method":"ai_photo","license":"ai_generated","mime_type":"image/webp","translations":{"ko":{"alt":"사무실에서 대형 터치스크린 분석 대시보드를 조작하는 여성","caption":"분석가가 대형 대시보드에서 데이터와 평가 항목을 검토하고 있다.","description":null},"en":{"alt":"Woman using a large touchscreen analytics dashboard in an office","caption":"An analyst reviews data and evaluation criteria on an interactive dashboard.","description":null},"ja":{"alt":"オフィスで大型タッチスクリーンの分析ダッシュボードを操作する女性","caption":"分析担当者が対話型ダッシュボードでデータと評価項目を確認している。","description":null},"es":{"alt":"Mujer usando un panel analítico táctil de gran tamaño en una oficina","caption":"Una analista revisa datos y criterios de evaluación en un panel interactivo.","description":null},"id":{"alt":"Perempuan menggunakan dasbor analitik layar sentuh besar di kantor","caption":"Seorang analis meninjau data dan kriteria penilaian pada dasbor interaktif.","description":null},"pt":{"alt":"Mulher usando um painel analítico em uma grande tela sensível ao toque","caption":"Uma analista revisa dados e critérios de avaliação em um painel interativo.","description":null},"zh-hant":{"alt":"女子在辦公室操作大型觸控分析儀表板","caption":"分析人員透過互動式儀表板檢視資料與評估項目。","description":null},"de":{"alt":"Frau bedient ein großes Touchscreen-Analyse-Dashboard in einem Büro","caption":"Eine Analystin prüft Daten und Bewertungskriterien auf einem interaktiven Dashboard.","description":null}}},{"id":903,"url":"https://injoys.com/rails/active_storage/blobs/proxy/eyJfcmFpbHMiOnsiZGF0YSI6MTIxMDIsInB1ciI6ImJsb2JfaWQifX0=--899ddbc79483c66a1ff40af5062b06b2fc48617a/ai-79b1d31e.webp","is_representative":false,"generation_method":"ai_image","license":"ai_generated","mime_type":"image/webp","translations":{"ko":{"alt":"웹페이지 데이터를 보안 서버와 4개 평가 모듈로 분석해 점수 대시보드로 출력하는 흐름도","caption":"콘텐츠, 작성자, 연결망, 신뢰도 신호를 통합해 E-E-A-T 점수를 산출하는 분석 과정을 보여준다.","description":null},"en":{"alt":"Flowchart of webpage data passing through a secure server and four modules into scoring dashboards","caption":"The diagram shows content, author, network, and trust signals being combined into reproducible E-E-A-T scores.","description":null},"ja":{"alt":"ウェブページのデータを安全なサーバーと4つの評価モジュールで処理し、スコア画面へ送るフロー図","caption":"コンテンツ、著者、ネットワーク、信頼性のシグナルを統合してE-E-A-Tスコアを算出する流れを示している。","description":null},"es":{"alt":"Diagrama del paso de datos web por un servidor seguro y cuatro módulos hasta paneles de puntuación","caption":"El diagrama muestra cómo se combinan señales de contenido, autoría, red y confianza para puntuar E-E-A-T.","description":null},"id":{"alt":"Diagram alur data halaman web melalui server aman dan empat modul menuju dasbor penilaian","caption":"Diagram ini menunjukkan penggabungan sinyal konten, penulis, jaringan, dan kepercayaan untuk menghasilkan skor E-E-A-T.","description":null},"pt":{"alt":"Fluxograma de dados de página web passando por servidor seguro e quatro módulos até painéis de pontuação","caption":"O diagrama mostra a combinação de sinais de conteúdo, autoria, rede e confiança para gerar pontuações de E-E-A-T.","description":null},"zh-hant":{"alt":"網頁資料經安全伺服器與四個評估模組處理後輸出至評分儀表板的流程圖","caption":"此圖呈現內容、作者、網路與信任訊號如何整合為可重現的 E-E-A-T 分數。","description":null},"de":{"alt":"Flussdiagramm: Webseitendaten laufen über einen sicheren Server und vier Module zu Bewertungs-Dashboards","caption":"Das Diagramm zeigt, wie Inhalts-, Autoren-, Netzwerk- und Vertrauenssignale zu E-E-A-T-Werten kombiniert werden.","description":null}}}],"published_at":"2026-08-26T15:49:11+09:00","updated_at":"2026-08-26T15:49:11+09:00","license":"cc_by","translation_status":"reviewed","available_locales":["ko","en","ja","es"],"data_locales":["ko","en","ja","es","id","pt","zh-hant","de"],"url":"https://injoys.com/en/articles/geo-eeat-scoring-system-design"}