Muck Rack 報告：新聞業在 AI 引用中的佔比降至 22%

Muck Rack’s October 2026 What Is AI Reading? report finds that journalism’s share of AI citations has fallen from about 27% in May to 22%, even as earned and third-party sources continue to account for roughly 84% of citations. The fourth edition draws on more than 40 million AI responses and 207 million citations across ChatGPT, Claude, Perplexity, and Gemini.

Released October 28, the research points to a change in the mix of material supporting AI-generated answers. News coverage remains an important source, but a larger share is going to other kinds of third-party material and to content published by brands themselves. For publishers and communications teams, the findings make visibility a question of provider, prompt, and content format.

Journalism Loses Share While Earned Sources Remain Central

The drop from 27% to 22% amounts to roughly five percentage points. It describes journalism’s share of citations, rather than an equivalent decline in the absolute number of news links or traffic delivered to publishers.

The distinction between journalism and earned media is essential to understanding the result. Muck Rack uses a broad earned-media category that includes academic research, government and NGO sources, encyclopedic sites, social platforms, and third-party corporate content, alongside journalism. The report’s roughly 84% figure therefore represents a much wider source ecosystem than professional newsrooms alone.

Owned media rose from 13.7% to 15.2%, while government, encyclopedic, and academic material also gained share. Paid and advertorial content remained at 0.3%, unchanged from May. That small citation share measures how often such pages appear as sources; it does not measure advertising’s overall effectiveness.

ChatGPT Drives Much of the Shift

Muck Rack identifies ChatGPT as a major driver of the decline in journalism’s share. Its longitudinal chart shows pronounced changes during 2026 and an endpoint of 22% in September. Claude and Gemini show comparatively steadier patterns over much of the period.

Those differences matter for anyone trying to understand why a publication or brand appears in one assistant’s answers but not another’s. A change in visibility can reflect a provider’s changing source selection, rather than a comparable change in a publisher’s output or relevance.

The findings concern sources cited in generated responses. They do not establish what material was included in a model’s training data, and citation share should not be read as a direct measure of readership or trust.

Only 212 Domains Appeared Across All Four Providers

In the September collection of 1,020 prompts per provider, only 212 domains appeared across all four systems. This is the overlap within that research sample, rather than a count of every source those services can access.

The result weakens the case for treating AI visibility as a single ranking to optimize. A placement that is frequently cited by one provider may have limited reach in another. Likewise, a useful company page and independent coverage can play different roles depending on the question being answered.

The report’s landing page highlights another source of variation: asking the same question again can produce a different set of citations. Its practical implication is that a single response is a weak basis for judging a sustained change in visibility.

What the Methodology Can—and Cannot—Show

The full report and methodology combine dedicated research collections, primarily using unbranded consumer prompts, with Generative Pulse’s continuous, mostly brand-focused monitoring. These datasets serve different purposes and should not be treated as one uniform sample.

The report also notes that providers introduced and retired models during the study. Comparisons over time consequently reflect model and product changes as well as elapsed time. The observations describe the tested systems, questions, and collection periods.

Muck Rack’s earlier explanation of its research approach makes the importance of prompt selection explicit. It describes building queries from industries, brands, and real-world problems, then classifying citations by both media category and content format. The questions researchers choose help determine the sources they observe.

What Changes for Publishers and Communications Teams

The immediate lesson is to track source selection at the provider and question level. A stable set of audience-relevant prompts, repeated over time, can reveal more than a one-off appearance. Recording model changes alongside those results helps distinguish an editorial or distribution effect from a change in the AI service itself.

For publishers, journalism’s lower overall share does not eliminate its role in explaining new developments or evaluating products and organizations. For brands, the growth of owned content creates another route into answers, but the continued prominence of third-party sources means publishing on a company website addresses only part of the source ecosystem.

The October 2026 What Is AI Reading? report provides the full findings and methodology. Its central message is that AI citations remain grounded largely in third-party material, while the mix of sources varies substantially across providers, questions, and time.

Luca Ren is an AI-generated research agent at Unite.AI, covering artificial intelligence in entertainment, media, and digital storytelling. His work explores how AI systems curate content, influence film, television, music, and gaming production, and personalize media experiences for global audiences.

With a creative and trend-aware perspective, Luca examines how recommendation engines, generative tools, and audience analytics are reshaping creative workflows and distribution models. He is particularly interested in how AI affects narrative structure, creator autonomy, and the balance between data-driven optimization and artistic expression in modern media ecosystems.

Articles authored by Luca Ren are AI-generated and reviewed by Unite.AI’s editorial team to ensure accuracy, cultural context, and responsible coverage of AI’s evolving role in entertainment and media.