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5 ways Google may evaluate E-A-T


We know Google wants to reward content and entities (like organizations and brands) that demonstrate high levels of expertise, authoritativeness and trust (E-A-T). We also know Google advises us to become familiar with its quality rater guidelines, especially when it comes to broad core algorithm updates.

What we don’t know with 100% certainty is how Google turns E-A-T – which is a concept, not a direct ranking factor or score – into signals the search engine can evaluate for the purpose of ranking search results.

In this article, I’ve compiled 5 potential on-page and off-page factors that Google could algorithmically use for E-A-T evaluation.

1. Quality of the website content in total

E-A-T is a kind of meta-rating of a publisher, author or the associated domain in relation to one or more topics. In contrast, Google evaluates the relevance on document level (i.e. each individual content in relation to the respective search query and its search intent).

So Google evaluates the quality of a publisher/author via E-A-T and the relevance via classic information retrieval methods (such as text analysis) in combination with machine learning innovations (such as Rankbrain).

In this context, content from different subject areas can influence each other positively as well as negatively, as Google confirms.

Hints on what you should pay attention to in order to evaluate the quality of website content in total can be found in the notes on the Google Panda update.

2. PageRank or references to the author / publisher

The fact that Google uses backlinks and the PageRank inherited from them to evaluate content and domains is not new and confirmed by Google. Also, that Google uses backlinks and PageRank for the evaluation regarding E-A-T is confirmed in the whitepaper “How Google fights Disinformation”.

“Google’s algorithms identify signals about pages that correlate with trustworthiness and authoritativeness. The best known of these signals is PageRank, which uses links on the web to understand authoritativeness.”

3. Distance to trust seed sites in the link graph

The more advanced form of the PageRank concept is based less on the number of incoming links and much more on the proximity of the linked documents to authority or seed websites.

The 2017 Google patent Producing a ranking for pages using distances in a web-link graph describes how a ranking score for linked documents can be produced based on the proximity to selected seed sites. In the process, the seed sites themselves are individually weighted.

The seed websites themselves are of high quality or the sources have high credibility.

According to the patent, these seed websites must be selected manually and the number should be limited to prevent manipulation. The length of a link between a seed page and the document to be ranked can be determined by the following criteria:

  • Position of the link.
  • Degree of thematic deviation of the source page.
  • Number of outgoing links of the source page.

It is interesting to note that websites that do not have a direct or indirect link to at least one seed website are not even included in the scoring.

This also allows conclusions to be drawn as to why some links are included by Google for ranking and some are not.

“Note that however, not all the pages in the set of pages receive ranking scores through this process. For example, a page that cannot be reached by any of the seed pages will not be ranked.”

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This concept can be applied to the document itself, but also to the publisher, domain or author in general. A publisher or author that is often directly referenced by seed sites gets a higher authority for the topic and semantically related keywords from which it is linked. These seed sites can be a set of sites per topic that are either manually determined or reach a threshold of authority and trust signals.

4. Anchor text of backlinks

According to Google, the anchor text of backlinks is not only a ranking signal for the linked target page, but also acts in thematic classification of the entire domain.

In the Google patent Search result ranking based on trust there are also references to the use of anchor text as a trust rating.

The patent describes how the ranking scoring of documents is supplemented based on a trust label. This information can be from the document itself or from referring third-party documents in the form of link text or other information related to the document or entity. These labels are associated with the URL and recorded in an annotation database.

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5. Credibility or trust of an author

In the exciting Google patent Credibility of an author of online content, reference is made to various factors that can be used to algorithmically determine the credibility of an author.

It describes how a search engine can rank documents under the influence of a credibility factor and reputation score of the author.

  • An author can have several reputation scores, depending on how many different topics he publishes content on. That is, an author can have reputation for multiple topics.
  • The reputation score of an author is independent of the publisher.
  • The reputation score can be downgraded if duplicates of content or excerpts are published multiple times.

In this patent there is again a reference to links – so the reputation score of an author can be influenced by the number of links of the published content.

The following possible signals for a reputation score are mentioned:

  • How long the author has a proven track record of producing content in a topic area.
  • How well known the author is.
  • Ratings of the published content by users.
  • If content by the author is published by another publisher with above-average ratings.
  • The number of content published by the author.
  • How long it has been since the author’s last publication.
  • The ratings of previous publications of similar topic by the author.

Other interesting information about the reputation score from the patent:

  • An author can have multiple reputation scores depending on how many different topics they publish content on.
  • An author’s reputation score is independent of the publisher.
  • Reputation score can be downgraded if duplicate content or excerpts are published multiple times.
  • The reputation score can be influenced by the number of links of the published content.

Furthermore, the patent discusses a credibility factor for authors. For this, verified information about the profession or the role of the author in a company is relevant. The relevance of the profession to the topics of the published content is also decisive for the credibility of the author. The level of education and training of the author can also have a bearing here.

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