Scaled content abuse is Google's spam policy against generating many pages mainly to manipulate search rankings instead of helping users, whether automation, people or both produce them. The policy sets no page count and no publishing speed; it defines abuse by why the pages exist and what users get from them.
- Google's spam policy defines scaled content abuse by purpose and value: many pages made mainly to manipulate rankings and not to help users, usually with large amounts of unoriginal content (Google, Search Central, read 30 October 2026).
- The section lists five examples and says the list is not exhaustive, and it gives no number of pages, rate or time window (Google, Search Central, read 30 October 2026).
- Google announced the strengthened policy on 5 March 2024 and said it covers content made at scale by automation, people or both (Google, The Keyword, read 30 October 2026).
- The Manual actions report names scaled content abuse only as an example under "Major spam problems" (Google, Search Console Help, read 30 October 2026).
What counts as scaled content abuse?
Under Google's spam policy, pages count as scaled content abuse when many of them are generated for the primary purpose of manipulating search rankings and not helping users
. The policy adds that the practice is typically about large amounts of unoriginal content that gives users little or no value, no matter how it's created
.
Our reading: two questions carry the definition, why the pages exist and what readers get from them; the tool and the author do not change the test. The table paraphrases the five examples Google lists, which it says are not exhaustive, and its second column is our reading, not Google's.
| Example Google lists | What it turns on (our reading) |
|---|---|
| Generative AI or similar tools making many pages without adding value for users | The pages add no value; the tool is not the test |
| Feeds, search results or other content scraped into many pages, even after automated rewording or translation, where little value is provided to users | Reused material in disguise |
| Content from different web pages stitched or combined without adding value | Recombined material, nothing new |
| Multiple sites created with the intent of hiding the scaled nature of the content | Hiding how much content there is |
| Many pages that make little sense to a reader but carry search keywords | Text written for search terms, not readers |
Is publishing many pages at once a problem?
Google's policy text does not say that publishing many pages at once is a problem. The definition does involve many pages, but the test it states is purpose and value, and the section gives no page count, rate or time window. That is an absence in the text, not a promise: no safe number is named either, and we cannot say how Google's systems treat a particular batch.
What Google does say about quantity is that more pages do not make a better site. Its guide to generative AI features says that creating separate content for every possible variation of how people might search, primarily to manipulate rankings or generative AI responses, violates the scaled content abuse policy, and that a high quantity of pages does not make a site higher quality or more relevant to users.
Our reasoning, not a Google statement: a page count and a launch date describe the batch, while the policy's examples describe what each page does. A large launch of pages that each tell a reader something new differs in purpose and value from a small set rewritten from one scraped feed. The pages we read do not say whether the size or speed of a launch changes how Google's systems treat it.
Does it matter whether AI or people made the pages?
No: the policy applies to content made at scale however it is produced. Google's announcement of 5 March 2024 says it strengthened the policy to target producing content at scale to boost rankings, whether automation, people or both produced it, and Google's Search Central post of the same date says the change builds on the earlier policy against automatically generated content.
Generative AI is the first of the five examples, but only when it is used to make many pages without adding value for users. Google's guidance on generative AI content describes the technology as useful for research and for structuring original content, and says what you publish must still meet the spam policies. Our guide to whether Google penalizes AI-generated content covers the wider question.
How does Google find and act on scaled content abuse?
Google says automated systems and, as needed, human review detect violations of its spam policies, and that a violation can lead to a manual action or to a site ranking lower or not appearing at all. A manual action is issued when a human reviewer at Google decides that a site violates the policies; the owner gets a notice in Search Console and can request reconsideration.
In the Manual actions report, scaled content abuse is named as an example under the type "Major spam problems"; no type carries the policy's own name, and a separate type, "Thin content with little or no added value", covers low-quality or shallow pages. The report is about manual actions, so in our reading an algorithmic demotion would not appear in it. Google says to fix all affected pages before requesting a review; fixing only some earns no partial return. The policy section ends with an instruction for a site that hosts such content: exclude it from Search
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On its Search Status Dashboard, Google's entry for the September 2026 spam update names no target beyond spam and gives site owners no actions, so, in our reasoning, an update's date alone cannot show that a site was caught by this policy.
What should you check before publishing a large batch of pages?
Check whether each kind of page in the batch gives a reader something original, by reading a random sample page by page before anything goes live. The Google pages we read give no checklist for batches, so these checks are our reasoning, built on questions its own guidance asks.
- Original value. Does the page offer original information, reporting, research or analysis, or does it mainly summarize what others have to say without adding much value? Google's helpful content guidance asks both, and whether you produce lots of content on many different topics in hopes that some of it might perform well in search results.
- Sameness (our question). Would a reader learn anything from a second page in the batch that the first did not already say? If not, the batch is one page published many times.
- Accuracy. Google calls it critical to manually fact-check and review all AI-generated content before publishing, as guidance rather than a spam-policy clause; see how to fact-check AI-written content against primary sources and what a human should approve before AI-drafted content is published.
- Records. Write down which URLs went live, when and from which template, so a later traffic change can be read against that list; what a content operations ledger should record lists the fields.
Example (illustrative numbers)
These numbers are invented to show the arithmetic; they are not data from any site. Suppose a team has drafted 2,400 location pages from one template and reads a random sample of 36 before publishing, marking a page as failing when a reader would learn nothing from it that the template's other pages do not already say.
If a share d of the pages fails, a random page passes with chance 1 − d, so all 36 sampled pages pass with chance (1 − d)^36, and the sample shows at least one failing page with chance 1 − (1 − d)^36. Treating each draw as independent is close enough here: the sample is 36 ÷ 2,400 = 1.5% of the batch, and drawing without replacement changes each percentage below by under 0.3 points.
| Share that fails (d) | Chance the sample has none: (1 − d)^36 | Chance it shows at least one |
|---|---|---|
| 1% (0.01 × 2,400 = 24 pages) | 0.99^36 = 0.696 | 1 − 0.696 = 30.4% |
| 2% (48 pages) | 0.98^36 = 0.483 | 1 − 0.483 = 51.7% |
| 5% (120 pages) | 0.95^36 = 0.158 | 1 − 0.158 = 84.2% |
| 10% (240 pages) | 0.90^36 = 0.023 | 1 − 0.023 = 97.7% |
A clean sample does not show that no page fails. A share d stays possible while a clean sample would still turn up at least 5% of the time, so the largest such share solves (1 − d)^36 = 0.05: 1 − d = 0.05^(1/36) = 0.9202, so d = 0.0798, and 0.0798 × 2,400 = 192 pages (rounded) could fail after 36 clean pages.
Our reading: a 36-page read finds common problems (a 10% share shows up in 97.7% of samples) and often misses rare ones (a 1% share shows up in 30.4%), and it says nothing about how Google would judge the batch.
What this cannot tell you
- The policy gives no number. Google's text names no page count, rate or time window, and none of the Google pages we read gives a safe one, so nothing here says that any batch size or schedule is safe. The policy page was last updated on 28 August 2026; check it again before relying on it.
- Google's judgment is out of view. Whether pages add value or exist mainly to manipulate rankings is decided by Google's systems and reviewers, and we cannot see either. The five examples are not a complete list, so a pattern outside them can count.
- A traffic drop is not a finding. A change near a spam update is timing evidence, not proof, and, in our reading, the Manual actions report would not show an algorithmic demotion; how to measure the impact of a Google update compares a change with your own ordinary swings.
- The rater guidelines are not a rulebook. Google's guidance on generative AI content points to section 4.6.5 of the Search Quality Rater Guidelines on scaled content abuse, and says raters use the guidelines to evaluate its ranking systems and that their ratings do not directly influence ranking.
- The sample arithmetic is ours. It assumes a random sample, independent draws and an honest pass or fail call on every page, and it shows how many pages to read, not what Google will decide.
Questions people also ask
What is scaled content abuse?
Scaled content abuse is a practice Google's spam policies target: making pages in bulk to win rankings rather than to serve readers. The typical case is a large set of pages with unoriginal text that gives people little, and Google says the production method, whether software, people or a mix, does not alter that.
What is Google's scaled content abuse policy?
The policy is one section of Google's Spam Policies for Google Web Search, which apply to all web search results. Violations are found by automated systems, with human reviewers added where Google judges it necessary, and the outcomes Google names are a manual action and lower rankings or no appearance at all. A site owner hit by a spam manual action receives a Search Console notice and can ask for reconsideration.
What does scaled content abuse mean in practice?
It describes pages whose main job is to catch searches, not to help the person who lands on them: text from generative AI or similar tools without adding value, material scraped or reworded from feeds and other pages where little value reaches users, fragments of different pages stitched together without adding value, extra sites set up to hide how much content exists, or pages whose content makes little sense to a reader but contains search keywords. Google says its list of examples is not exhaustive, so, in our reading, a pattern outside it can still be judged by the same two questions of purpose and value.
Is it bad to publish a lot of pages at once?
Google's policy does not put the timing of a launch into the test, and it names no number of pages; it asks why pages exist and what readers gain. That says nothing about how any particular batch will be treated, so the practical step is to read a random sample before launch and to keep a dated record of which pages went live.
Sources
- Spam Policies for Google Web Search, Google, Search Central, read 30 October 2026.
for the primary purpose of manipulating search rankings and not helping users
no matter how it's created
exclude it from Search
- New ways we’re tackling spammy, low-quality content on Search, Google, The Keyword, read 30 October 2026.
- What web creators should know about our March 2024 core update and new spam policies, Google, Search Central Blog, read 30 October 2026.
- Manual actions report, Google, Search Console Help, read 30 October 2026.
- Google Search's Guidance on Generative AI Content on Your Website, Google, Search Central, read 30 October 2026.
- Creating helpful, reliable, people-first content, Google, Search Central, read 30 October 2026.
- Google's Guide to Optimizing for Generative AI Features on Google Search, Google, Search Central, read 30 October 2026.
- Incident details | Google Search Status Dashboard (September 2026 spam update), Google, Search Status Dashboard, read 30 October 2026.