Google Explains the Functionality of Its Helpful Content System

Google’s Search Liaison recently provided clarification regarding a potentially perplexing aspect of their Helpful Content System guidance, which had the potential to inadvertently impact innocent publishers.

The Helpful Content System

Google’s Helpful Content System relies on a machine learning model employing classifiers to generate a signal that subsequently informs Google’s ranking system, aiding in the identification and filtering of low-quality content.

In the context of this system, a classifier is essentially an algorithm within the machine learning model responsible for assigning labels to website content. This label assignment process results in the generation of a signal, akin to a thumbs-down rating.

Furthermore, it is essential to note that this signal is weighted. It means that a website containing only a small amount of unhelpful content receives a relatively minor thumbs-down signal, in contrast to a website with a substantial amount of unhelpful content, which garners a more pronounced thumbs-down signal.

The signal generated by the Helpful Content System is just one of the many signals, numbering in the hundreds or even thousands, that Google employs in its site ranking process. These signals encompass factors such as backlinks, relevancy, and more.

Google’s Recent Guidance on the Helpful Content System

In the most recent iteration of the Helpful Content System update, Google aimed to provide more transparent guidance. The intent was to assist publishers and SEO professionals in comprehending the factors contributing to ranking fluctuations.

The term ‘opaque’ signifies a lack of clarity or transparency. Regrettably, one specific section of this guidance inadvertently fell short in terms of clarity, and confusion. The passage in question reads as follows:

“Are you changing the date of pages to make them seem fresh when the content has not substantially changed?”

This passage is directed toward certain users who attempt to manipulate Google’s freshness algorithm. Their strategy involves making relatively insignificant alterations to the content and then updating the publication date. The goal is to deceive Google into perceiving the old content as a newly published webpage.

However, the problem arises from the fact that many individuals revisit a webpage and make minor adjustments to the content for legitimate reasons, such as:

Rectifying typos

  • Adding or substituting words to enhance grammatical correctness or clarity
  • Changing words to improve content clarity
  • There are numerous legitimate but small modifications that many individuals make to content.

The guidance that seemingly discouraged making small changes leading to date modifications inadvertently created a situation where even minor improvements could potentially result in a negative evaluation by the Helpful Content System.

This precise issue was raised on X (formerly known as Twitter), where Luke Jordan (@lr_jordan) expressed their valid concern.

“Google doesn’t understand nuance well enough to make blanket rules

It’s punishing websites for using a ‘last updated’ date for “small” changes

But in gaming, a patch/update could be as simple as an upgrade that cost 5 points now costs 6

And that tiny increase could change a lot about its usefulness

Users will want to know the post is up to date, and therefore relevant, so will refer to the date and patch number

A genuinely valuable update might require changing the number 6 to 5, and a patch number from 9.0.1 to 9.0.2.

If the date says the guide was last updated 6 months ago, that makes no sense

Plus the (massively outdated) date shows in Google results, so people would click it far less too, with CTR being another ranking factor

Of course they can just pretend they understand all of this and being super duper helpful will always win!”

Google SearchLiaison responded:

“No, we don’t do this if updates are made to be helpful to people.

Not something we say.

Not in our guidelines.”

SearchLiaison’s accuracy is evident, yet due to the ambiguity in that specific passage, it seems to align with Luke Jordan’s interpretation.

Luke subsequently added:

“So, to confirm, you know if a single character change to an article is designed to be helpful for people?”

There is one additional post from Luke, accompanied by a screenshot of the passage in the guidance:

“cos it’s literally in your guidelines that you shouldn’t change the date of pages when the content has not substantially changed.”

Related: Google Rolls Out November 2023 Core Update


SearchLiaison responded:

“The context of those question are if your doing something for Google.

If your just changing the date because you think “that’ll make Google think this is fresh,” you’re likely aligning with other behaviors that overall align with signals we use to identify the helpfulness of content.

It’s not just one thing. It’s not direct.

And it’s not an issue if you’re not doing things primarily for Google.”

The message from SearchLiaison suggests that the practice of altering publication dates is just one among several tactics employed by their machine learning model to assess the likelihood that a webpage is utilizing SEO techniques to optimize for Google, as opposed to genuinely delivering valuable content.

In the realm of statistics, a single metric used in isolation can lead to erroneous conclusions. This is why, in statistical models related to search, it is widely acknowledged that combining multiple signals is more reliable in calculating the statistical probability compared to relying solely on a single metric.

While we can’t presume to speak on behalf of SearchLiaison, it appears they are conveying that labeling a webpage as unhelpful based on a solitary indicator of potential unhelpfulness is inadequate when no other negative signals are present.

The following is SearchLiaison’s statement:

“If your just changing the date because you think “that’ll make Google think this is fresh,” you’re likely aligning with other behaviors that overall align with signals we use to identify the helpfulness of content.”


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