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 14 potential on-page and off-page factors that Google could algorithmically use for E-A-T evaluation.

Disclaimer

This article is not meant to be an exhaustive list of every possible E-A-T signal Google could be using, but a look at some signals that Google is most likely to be using to evaluate E-A-T, based on supporting evidence and documentation available to us.

It is also not a cheat sheet for SEOs looking to manipulate the SERPs. E-A-T can only be influenced to a limited extent by SEO measures. Most of the signals discussed in this article are more likely to be influenced by marketing and PR.

There is no single E-A-T score into which all signals are summarized or added up, according to Google. I can imagine that Google gets an overall impression of E-A-T of an author, publisher or website through many different algorithms (a.k.a, “baby” or “tiny” algorithms).

This overall impression is not so much a score, but an approximation of sample image for an entity that has E-A-T. Google could use selected sample entities to train the algorithms to produce a benchmark pattern for E-A-T. The more the entity resembles this pattern image across different signals, the higher the quality.

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.”

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.

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.

6. Name recognition of the author / publisher (number of mentions & search volume)

The higher the popularity of an author/publisher, the more credible he/she is and the higher his/her authority in a topical area. Google can algorithmically measure the level of awareness via the number of mentions and the search volume for the name. In addition to the patent already mentioned, there are further statements from Google on the degree of awareness as a possible ranking factor.

With regard to local search, the following statement can be found on the Google support pages regarding local ranking:

“Awareness Level: This refers to how well known a company is. Some places or things are better known than others. This is taken into account in the ranking of local search results. For example, famous museums, hotels or retail brands that are well known to many users are also very likely to appear in a prominent position in the local search results. In addition, awareness or importance is derived from information we obtain about a company from the web – for example, via links, from articles or from directories.”

Google’s Gary Illyes spoke at Brighton SEO in 2017 about the influence of mentions, (e.g. in social networks), and seemed to suggest that Google could be interested in such signals:

“If you publish high-quality content that is highly cited on the internet — and I’m not talking about just links, but also mentions on social networks and people talking about your branding, crap like that. Then you are doing great.”

7. Sentiment around mentions or ratings and click-through rate

Google is able to perform sentiment analysis via Natural Language Processing.

In other words, the sentiment surrounding an entity such as a publisher or author can be determined. If the sentiment is positive, the author/publisher can be given more credibility. If it is negative, the opposite is true.

The Google patent Sentiment detection as a ranking signal for reviewable entities describes how sentiment analysis can be used to identify sentiment around reviewable entities in documents. The results can then be used to rank entities and associated documents.

Evaluable entities include people, places, or things about which sentiment can be expressed, such as restaurants, hotels, consumer products such as electronics, movies, books and live performances.

Structured and unstructured data can be used as a source. Structured reviews are collected from popular review websites such as Google Maps, TripAdvisor, Citysearch, or Yelp.

The entities stored in the Sentiment database are represented by tuples in the form of the entity ID, entity type and one or more reviews. The reviews are assigned different scores, which are calculated in the Ranking Analysis Engine.

Sentiment scores concerning the respective reviews including additional information such as the author are determined in the Ranking Analysis Engine.

The patent also discusses the use of interaction signals to complement sentiment in terms of ranking as a factor.

• User Interaction Score
• Consensus Sentiment Score

How could Google determine a user interaction score? By looking at user signals such as SERP CTR and duration of stay.

8. Cooccurrences of the author / publisher with thematically relevant terms in videos, podcasts & documents (offpage)

Cooccurrences of an entity in crawlable and interpretable content with terms from certain subject areas could help Google to classify an author or publisher in a thematic context.

The number of co-occurrences as well as the authority and trustworthiness of the sources in which the co-occurrences occur can be used for the evaluation according to E-A-T.

Due to the developments that Google is driving with innovations such as MUM, this content can also be images, video and audio content in addition to text content.

9. Cooccurrences of the author / publisher with thematically relevant terms in search queries (offpage)

Co-occurrences from entities and topic-related terms in content could help Google perform an E-A-T evaluation. Cooccurrences in search queries can also be an important signal.

If many people search for “mercedes cabrio” or “olaf kopp content marketing”, this can be an indication that Mercedes is an authority on cabrios or that Olaf Kopp is an authority on content marketing.

10. Percentage of content that an author / publisher has contributed to a thematic document corpus (onpage / offpage).

The Google patent Systems and Methods for Re-Ranking ranked Search Results describes how search engines can take into account the author’s contribution to a thematic document corpus in addition to the author’s content links.

This Google patent was drawn in August 2018. It describes the refinement of search results according to an author scoring including a citation scoring. Citation scoring is based on the number of references to an author’s documents.

Another criterion for author scoring is the proportion of content that an author has contributed to a corpus of documents.

“…wherein determining the author score for a respective entity includes: determining a citation score for the respective entity, wherein the citation score corresponds to a frequency at which content associated with the respective entity is cited; determining an original author score for the respective entity, wherein the original author score corresponds to a percentage of content associated with the respective entity that is a first instance of the content in an index of known content; and combining the citation score and the original author score using a predetermined function to produce the author score; …”

11. Transparency to the author / publisher via author profiles & About Us pages (Onpage)

Transparency about the publisher or the authors is mentioned in the Quality Rater Guidelines as a signal that search evaluators should use for the E-A-T rating. In addition, the Guidelines for Web Credibility of Stanford University provide some hints on which questions should be addressed when designing an About Us page and/or author profiles.

“Show that there’s a real organization behind your site. Showing that your web site is for a legitimate organization will boost the site’s credibility. The easiest way to do this is by listing a physical address. Other features can also help, such as posting a photo of your offices or listing a membership with the chamber of commerce.

Highlight the expertise in your organization and in the content and services you provide. Do you have experts on your team? Are your contributors or service providers authorities? Be sure to give their credentials. Are you affiliated with a respected organization? Make that clear. Conversely, don’t link to outside sites that are not credible. Your site becomes less credible by association.

Show that honest and trustworthy people stand behind your site. The first part of this guideline is to show there are real people behind the site and in the organization. Next, find a way to convey their trustworthiness through images or text. For example, some sites post employee bios that tell about family or hobbies.”

For an algorithmic transfer of this measure, I think only the use of an author box or an About Us page is too simple. After all, one can simply invent an author and then present him as an expert.

With a view to semantic search or entity-based search, it would make sense for Google to include the information collected about the entity, including verification. Say About Us pages and author boxes can only help with regard to E-A-T if the publisher or author is an authority and/or expert that can be checked by Google. This author must have already left crawlable traces on the web.

12. Links to own references

To find references that identify a publisher and/or author as an authority and expert, you can make Google’s review easier by linking to publications, interviews, speaker profiles at professional conferences, articles in external media, etc., from your website.

13. Use of https on the domain

Google has confirmed that https is a light ranking factor. With regard to trust (i.e., the trustworthiness of a source), this ranking factor makes sense. While the influence on ranking is rather small, every little bit of trustworthiness can add up.

14. Knowledge-Based Trust (agreement with common opinion and facts)

The scientific paper Knowledge-Based Trust: Estimating the Trustworthiness of Web Sources from Google deals with the algorithmic determination of the credibility of websites.

This scientific paper deals with how to determine the trustworthiness of online sources. Besides the analysis of links, a new method is presented, which is based on the examination of the published information for correctness.

“We propose a new approach that relies on endogenous signals, namely, the correctness of factual information provided by the source. A source that has few false facts is considered to be trustworthy.

We call the trustworthiness score we computed Knowledge-Based Trust (KBT). On synthetic data, we show that our method can reliably compute the true trustworthiness levels of the sources.”

The previous evaluation of the credibility of sources based on links and browser data on website usage behavior has weaknesses, as less popular sources have worse cards and are unfairly shortchanged, even though they provide very good information.

Using this approach, sources can be rated with a “trustworthiness score” without including the popularity factor. Websites that frequently provide incorrect information are devalued. Websites that publish information in line with the general consensus are rewarded. This also reduces the likelihood that websites that attract attention through Fake News will gain visibility on Google.

Summary: Possible E-A-T evaluation factors

That was a lot of information. So that you don’t lose the overview, we have created an overview graphic of the various possible factors for an E-A-T evaluation.

This is a shortened and translated version of the original blogpost “18 E-A-T Bewertungs-Faktoren für das Ranking bei Google”.