Google uses language-understanding systems, reliability signals and feature-specific methods to process information. Its public documentation does not establish a universal fact-versus-opinion classifier with a published accuracy rate or a minimum citation count for every page. For a publisher, the practical task is to distinguish checkable claims, judgments, predictions and attributed statements—and support each appropriately.
A factual claim is not necessarily a verified fact
A factual claim can be checked and can turn out to be false. An unsupported specification does not become a harmless opinion merely because no evidence is supplied. Opinions can also be well supported; their criteria and trade-offs should be visible.
| Type | Hypothetical statement | What to check |
|---|---|---|
| Factual claim | “The annual plan costs $240.” | The actual plan, seats, region, tax treatment and observation date |
| Recommendation | “I would choose A for a team that only needs report exports.” | Requirements, comparison criteria, supporting information and exceptions |
| Prediction | “The feature may arrive next quarter.” | Who predicts it, on what basis, and with what uncertainty |
| Attribution | “The manufacturer says Model A supports this application.” | Whether the source says that and under which conditions; not automatic independent proof of performance |
| Experience | “During this installation, I found the instructions difficult to locate.” | Whether the experience occurred, and its version and circumstances |
Adding “I think” to “this device works at every temperature” does not remove its performance claim. Likewise, calling something “best” needs a defined audience and comparison, not merely a confident tone.
What Google's public descriptions actually establish
The ranking-systems guide describes BERT as helping interpret combinations of words and intent, neural matching as connecting concepts, and reliability systems as working in several ways to surface reliable information. These are not instructions to classify every numerical sentence as true or every personal judgment as low quality. For page-level expression, see entity and context checks for NLP content.
Knowledge-panel documentation explains that panels are generated automatically from multiple web sources, with data partners and entity feedback contributing in some cases. This describes how panel information is sourced. It does not disclose a universal database that certifies every statement on every website.
Google's August 2022 announcement described using MUM to check whether the prominent words above a featured snippet agree with other high-quality sources. The current ranking guide still describes MUM as serving specific applications rather than general ranking. This particular consensus check should not be rewritten as “all articles need two or three authoritative citations.”
Multiple sites repeating the same press release, study or mistake do not constitute multiple independent checks. Trace a material claim to its original evidence and inspect the scope, rather than counting agreeing pages.
Useful opinions are compatible with useful search content
A reader may need a measurement, but may instead need an interpretation, a comparison or a reasoned recommendation. Google's helpful-content guidance asks about original analysis, completeness, sourcing and easily verified factual errors. It does not prescribe a required percentage of factual sentences.
Its review guidance explicitly includes independent opinions and recommends explaining evidence, advantages, drawbacks and suitable uses. A recommendation becomes more useful when readers can see how it was reached. If it is a document-based comparison, describe it that way rather than claiming hands-on testing.
For example, a basic plan might suit a team that only needs exports, while a team needing collaborative approvals may need another option. Verify those capabilities first, then explain the conditional judgment. Neither decision establishes which option is universally best.
A list of correct facts may still leave the reader unable to decide. Use a reader-focused article structure to connect the evidence, comparison and next step rather than adding unrelated facts to increase a percentage.
Work through one mixed claim
Hypothetical draft: “A costs only $20 a month, is the best value for every team, and increased our productivity by 30%.” This sentence contains pricing, judgment and a claimed result. Check them separately.
- Pricing: if the source actually specifies $240 paid annually, write “$240 billed annually, equivalent to $20 per month.” That does not establish a $20 monthly payment option. Check seat, regional and tax conditions too.
- Judgment: identify the required capabilities and acceptable cost, compare alternatives on the same basis, and remove the unsupported “every team.”
- Result: without a genuine test, a defined productivity measure and comparable records, remove “increased by 30%” and the implied firsthand experience. Turn it into a question to test, not a smaller invented number.
This is an editing example, not an actual subscription offer or customer experiment. When using AI to revise the wording, retain the numbers, units, conditions, negations and attribution. The fact-preserving editing guide develops that distinction.
Build a claim record that can be maintained
Start with statements that affect a purchase, procedure or significant decision. Record the sentence, claim type, primary source, version or date, supporting passage, applicable conditions and unresolved questions.
- Product information: match the exact model, formulation, units and operating conditions. Maximum values from different catalogs do not define one usable operating envelope.
- Statistics: check the sample, denominator, time, geography and comparison method. An observed association is not automatically a causal result.
- Rules and recommendations: distinguish official requirements, a company's own service scope and editorial advice. High-impact health, legal and financial topics need the relevant current jurisdiction and expertise.
- Quotations: establish who said what and in which context. A correct attribution does not by itself prove the quoted proposition.
- Updates: correct what has changed. Preserve historical context and original publication dates rather than replacing a year to simulate verification.
E-E-A-T information should reflect real authorship, experience and supporting material. Google's helpful-content guidance states that E-E-A-T is not a single ranking factor and that quality raters do not control page rankings. It is not a fact-confidence score a website can assign to itself.
Do not confuse fact-check markup with certification
The current ClaimReview documentation says support in Google Search is being phased out, while the Fact Check Explorer tool continues to support the markup. It describes a publisher's review of another claim; it is not a general “verified by Google” switch for ordinary articles. Older announcements are not a current display guarantee.
Correcting a demonstrated factual error improves the article, but does not establish a recovery schedule or explain every traffic change. Use the content-quality audit by page type and the click-change diagnostic to examine other evidence. Verify the published repair, then evaluate indexing, search appearances and qualified inquiries separately.