Article schema identifies a page as editorial content. It helps answer engines classify the page as a published article with an author, a publisher, and a publication date in schema markup.
This is useful when the page is written like an editorial piece and the visible page supports that format. The schema should describe the page that exists, not a generic label placed on top of it.
When to use it
Use Article schema for blog posts, analysis pieces, news stories, and similar editorial formats.
It is a good fit when the page has a clear headline, a byline, and a publication date or update date that readers can see, aligned with author and date signals.
What it should include
- Headline.
- Author when known.
- Publisher or organization.
- Publication and update dates when available.
- Enough page context for the reader to understand the article topic.
What to avoid
- Marking up a page that is really a product page.
- Using Article schema on a page with no visible editorial structure.
- Leaving out the byline and date on a page that clearly functions as an article.
For example, if Ajey writes a brand story for AwesomeShoes Co. as a published article, Article schema can fit. If the same text sits inside a service landing page, the markup may no longer match the page type.
AEO rule of thumb
Article schema is most useful when the page is clearly written as an article and the visible structure matches that editorial format for stronger E-E-A-T.
Implementation example
AwesomeShoes Co. publishes educational footwear guides, but some article pages are missing clear bylines and update dates, weakening editorial trust signals. The managing editor needs article schema that accurately reflects visible authorship and recency.
Implementation discussion: editors standardize headline/byline/date blocks, engineering maps those fields directly into Article JSON-LD, and QA verifies schema matches rendered content for every article template. The SEO analyst then monitors whether improved editorial consistency increases citation reliability on informational query clusters.
