Author Authority Signals

What this parameter measures

This parameter checks whether your content shows who wrote it and why they are qualified. friendly4AI parses your page HTML and JSON-LD for five author-authority signals and combines them into a weighted composite. The five sub-signals are: Person JSON-LD carrying a name (25%), a Person.sameAs list covering at least two distinct platform hosts such as LinkedIn and Twitter (20%), a visible bio over 20 words inside an author, bio, or byline element (20%), an author-page link whose href contains /author/, /authors/, or /team/ (15%), and credential markers such as "PhD", "Dr.", "professor", "certified", or "years of experience" found in the schema jobTitle or a byline context (20%). The composite is the weighted sum of these signals on a 0-100 scale.

Why it matters for AI-readiness

AI systems weigh author expertise heavily when deciding what to trust and cite. Content with clear authorship (a named author, verifiable credentials, and links to a profile) maps directly onto the experience, expertise, authoritativeness, and trustworthiness (E-E-A-T) signals that answer engines use to rank and attribute sources. Anonymous content reads as lower-trust and is less likely to be quoted. A named expert with a Person schema and cross-platform sameAs links gives a model a verifiable identity it can stand behind when surfacing your content.

How we score it

This Authority and Trust parameter is a weighted composite under the v4.4 methodology, not a simple tri-level ladder. The processor scores each of the five sub-signals as present (100) or absent (0), then computes round(0.25·schema + 0.20·sameAs + 0.20·bio + 0.15·authorLink + 0.20·credentials). The published rubric maps that composite to bands: a score of 80 or higher passes, a score of 1-79 is a partial, and a composite of 0 (no qualifying signal at all) fails. One nuance is worth noting: the schema sub-signal only counts when a Person schema carries a name (a byline name alone does not satisfy it), so schema-less pages cap their own ceiling even with strong bios and credentials.

How to fix common issues

• Add Person JSON-LD with name and jobTitle for each author, and include a sameAs array linking at least two profiles (for example LinkedIn and a personal site).
• Publish a visible author bio of more than 20 words inside an element whose class or id contains author, bio, or byline.
• Link bylines to a dedicated author page under /author/, /authors/, or /team/.
• Surface credentials in the bio or schema jobTitle, such as degrees, certifications, or years of experience, so the credential sub-signal fires.
• Re-scan and check the score_schema, score_same_as, score_bio, score_author_link, and score_credentials evidence fields to see which sub-signal is missing.