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What Is Latent Semantic Indexing and Why It Doesn’t Matter for SEO via @martinibuster

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Many claims are made for Latent Semantic Indexing (LSI) and “LSI Keywords” for SEO.

Some even say that Google relies on “LSI keywords” for understanding webpages.

This has been discussed for nearly twenty years and the evidence-based facts have been there the entire time.

This Is Latent Semantic Indexing

Latent semantic indexing (also referred to as Latent Semantic Analysis) is a method of analyzing a set of documents in order to discover statistical co-occurrences of words that appear together which then give insights into the topics of those words and documents.

Two of the problems (among several) that LSI sets out to solve are the issues of synonymy and polysemy.

Synonymy is a reference to how many words can describe the same thing.

A person searching for “flapjack recipes” is equal to a search for “pancake recipes” (outside of the UK) because flapjacks and pancakes are synonymous.

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Polysemy refers to words and phrases that have more than one meaning. The word jaguar can mean an animal, automobile, or an American football team.

LSI is able to statistically predict which meaning of a word represents by statistically analyzing the words that co-occur with it in a document.

If the word “jaguar” is accompanied in a document by the word “Jacksonville,” it is statistically probable that the word “jaguar” is a reference to an American football team.

By understanding how words occur together, a computer is better able to answer a query by correctly associating the right keywords to the search query.

The patent for LSI was filed on September 15, 1988. It’s an old technology that came years before the internet as we know it existed.

LSI is not new nor is it cutting edge.

It is important to understand that in 1988, LSI was advancing the state of the art of simple text matching.

LSI preceded the internet and was created during a time when Apple computers looked like this:

image of an Apple Macintosh SE computer from 1988

image of an Apple Macintosh SE computer from 1988

LSI was created when a popular business computer (IBM AS/400) looked like this:

Image of an IBM AS400 computer from 1988

Image of an IBM AS400 computer from 1988

LSI is a technology that goes way back.

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Just like computers from 1988, the state of the art in Information Retrieval has come a long way over the past 30+ years.

LSI is Not Practical for the Web

A major shortcoming of using Latent Semantic Indexing for the entire web is that the calculations done to create the statistical analysis have to be recalculated every time a new webpage is published and indexed.

This shortcoming is mentioned in a 2003 (non-Google) research paper about using LSI for detecting email spam (Using Latent Semantic Indexing to Filter Spam PDF).

The research paper notes:

“One issue with LSI is that it does not support the ad-hoc addition of new documents once the semantic set has been generated. Any update to any cell value will change the coefficient in every other word vector, as SVD uses all linear relations in its assigned dimensionality to induce vectors that will predict every text samples in which the word occurs…”

I asked Bill Slawski about the unsuitability of LSI for search engine information retrieval and he agreed, saying:

“LSI is an older indexing approach developed for smaller static databases. There are similarities with newer technologies such as the use of word vectors or word2Vec.

One of the limitations of LSI is that if new content is added to a corpus that indexing for the entire corpus is required, which makes it of limited usefulness for a quickly changing corpus such as the Web.”

Is There a Google LSI Keywords Research Paper?

Some in the search community believe Google uses “LSI Keywords” in their search algorithm as if LSI is still a cutting-edge technology.

To prove it, some refer to a 2016 research paper called, Improving Semantic Topic Clustering for Search Queries with Word Co-occurrence and Bigraph Co-clustering (PDF).

That research paper is absolutely not an example of Latent Semantic Indexing. It’s a completely different technology.

In fact, that research paper is so not about LSI (a.k.a. Latent Semantic Analysis) that it cites a 1999 LSI research paper ([5] T. Hofmann. Probabilistic latent semantic indexing. …1999) as part of an explanation of why LSI is not useful for the problem the authors are trying to solve.

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Here’s what it says:

“Latent dirichlet allocation (LDA) and probabilistic latent semantic analysis (PLSA) are widely used techniques to unveil latent themes in text data. …These models learn the hidden topics by implicitly taking advantage of document level word co-occurrence patterns.

Short texts however – such as search queries, tweets or instant messages – suffer from data sparsity, which causes problems for traditional topic modeling techniques.”

It’s a mistake to use the above research paper as proof that Google uses LSI as an important ranking factor. The paper is not about LSI and it’s not even about analyzing webpages.

It’s an interesting research paper from 2016 about data mining short search queries in order to understand what they mean.

That research paper aside, we know that Google uses BERT and neural matching technologies to understand search queries in the real world.

Long story short: the use of that research paper to make a definitive statement about Google’s ranking algorithm is sketchy all around.

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Does Google Use LSI Keywords?

In search marketing, there are two kinds of trustworthy and authoritative data:

  1. Factual ideas that are based on public documents like research papers and patents.
  2. SEO ideas that are based on what Googlers have revealed.

Everything else is mere opinion.

It’s important to know the difference.

Google’s John Mueller has been straightforward about debunking the concept of LSI Keywords.

Noted search patent expert Bill Slawski has also been outspoken about the notion of Latent Semantic Indexing and SEO.

Bill’s statements on LSI are based on a deep knowledge of Google’s algorithms, which he has shared in fact-based articles (like här och här).

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Bill Slawski Tweets His Informed Opinion on Latent Semantic Indexing

Why Google Is Associated with Latent Semantic Analysis

Despite there not being any proof in terms of patents and research papers that LSI/LSA are important ranking-related factors, Google is still associated with Latent Semantic Indexing.

One reason for this is Google’s 2003 acquisition of a company called Applied Semantics.

Applied Semantics had created a technology called Circa. Circa was a semantic analysis algorithm that was used in AdSense and also in Google AdWords.

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Enligt Google’s press release:

“Applied Semantics is a proven innovator in semantic text processing and online advertising,” said Sergey Brin, Google’s co-founder and president of Technology. “This acquisition will enable Google to create new technologies that make online advertising more useful to users, publishers, and advertisers alike.

Applied Semantics’ products are based on its patented CIRCA technology, which understands, organizes, and extracts knowledge from websites and information repositories in a way that mimics human thought and enables more effective information retrieval. A key application of the CIRCA technology is Applied Semantics’ AdSense product that enables web publishers to understand the key themes on web pages to deliver highly relevant and targeted advertisements.”

Semantic Analysis & SEO

The phrase “Semantic Analysis” was a hot buzzword in the early 2000s, perhaps partially driven by Ask Jeeves’ semantic search technology.

Google’s purchase of Applied Semantics accelerated the trend of associating Google with Latent Semantic Indexing, despite there being no credible evidence.

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Thus, by 2005 the search marketing community was making unsubstantiated statements such as this:

“For several months I’ve noticed changes in website rankings on Google and it was clear something had changed in their algorithm.

One of the most important changes is the likelihood that Google is now giving more weight to Latent Semantic Indexing (LSI).

This should come as no surprise considering Google purchased Applied Semantics in April 2003 and has reportedly been serving up their AdSense ads using latent semantic indexing.”

The SEO myth that Google uses LSI Keywords quite possibly originated from the popularity of phrases like “Semantic Analysis,” “Semantic Indexing” and “Semantic Search” having become SEO buzzwords, given life by Ask Jeeves’ semantic search technology and Google’s purchase of semantic analysis company Applied Semantics.

The Facts About Latent Semantic Indexing

LSI is a very old method of understanding what a document is about.

It was patented in 1988, well before the internet as we know it existed.

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The nature of LSI makes it unsuitable for applying across the entire internet for purposes of information retrieval.

There are no research papers that explicitly show that latent semantic indexing is an important feature of Google search ranking.

The facts presented in this article show that this has been the case since the early 2000s.

Rumors of Google’s use of LSI and LSA surfaced in 2003 after Google acquired Applied Semantics, the company that produced the contextual advertising product AdSense.

Yet Googlers have affirmed multiple times that Google uses no such thing as LSI Keywords.

Let me say it again louder for those at the back: There is no such thing as LSI Keywords.

Considering the overwhelming amount of evidence, it is reasonable to assert that it is a fact that the concept of LSI Keywords is false.

The facts also indicate that LSI is not an important part of Google’s ranking algorithms.

Regarded in the light of recent advancements in AI, natural language processing, and BERT, the idea that Google would prominently use LSI as a ranking feature is literally beyond belief and ridiculous.

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Google Updates Structured Data Guidance To Clarify Supported Formats

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Google Updates Structured Data Guidance To Clarify Supported Formats

Google updated the structured data guidance to better emphasize that all three structured data formats are acceptable to Google and also explain why JSON-LD is is recommended.

The updated Search Central page that was updated is the Supported Formats section of the Introduction to structured data markup in Google Search webpage.

The most important changes were to add a new section title (Supported Formats), and to expand that section with an explanation of supported structured data formats.

Three Structured Data Formats

Google supports three structured data formats.

  1. JSON-LD
  2. Microdata
  3. RDFa

But only one of the above formats, JSON-LD, is recommended.

According to the documentation, the other two formats (Microdata and RDFa) are still fine to use. The update to the documentation explains why JSON-LD is recommended.

Google also made a minor change to a title of a preceding section to reflect that the section addresses structured data vocabulary

The original section title, Structured data format, is now Structured data vocabulary and format.

Google added a section title the section that offers guidance on Google’s preferred structured data format.

This is also the section with the most additional text added to it.

New Supported Formats Section Title

The updated content explains why Google prefers the JSON-LD structured data format, while confirming that the other two formats are acceptable.

Previously this section contained just two sentences:

“Google Search supports structured data in the following formats, unless documented otherwise:

Google recommends using JSON-LD for structured data whenever possible.”

The updated section now has the following content:

“Google Search supports structured data in the following formats, unless documented otherwise.

In general, we recommend using a format that’s easiest for you to implement and maintain (in most cases, that’s JSON-LD); all 3 formats are equally fine for Google, as long as the markup is valid and properly implemented per the feature’s documentation.

In general, Google recommends using JSON-LD for structured data if your site’s setup allows it, as it’s the easiest solution for website owners to implement and maintain at scale (in other words, less prone to user errors).”

Structured Data Formats

JSON-LD is arguably the easiest structured data format to implement, the easiest to scale, and the most straightforward to edit.

Most, if not all, WordPress SEO and structured data plugins output JSON-LD structured data.

Nevertheless, it’s a useful update to Google’s structured data guidance in order to make it clear that all three formats are still supported.

Google’s documentation on the change can be read here.

Featured image by Shutterstock/Olena Zaskochenko



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Ranking Factors & The Myths We Found

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Ranking Factors & The Myths We Found

Yandex is the search engine with the majority of market share in Russia and the fourth-largest search engine in the world.

On January 27, 2023, it suffered what is arguably one of the largest data leaks that a modern tech company has endured in many years – but is the second leak in less than a decade.

In 2015, a former Yandex employee attempted to sell Yandex’s search engine code on the black market for around $30,000.

The initial leak in January this year revealed 1,922 ranking factors, of which more than 64% were listed as unused or deprecated (superseded and best avoided).

This leak was just the file labeled kernel, but as the SEO community and I delved deeper, more files were found that combined contain approximately 17,800 ranking factors.

When it comes to practicing SEO for Yandex, the guide I wrote two years ago, for the most part, still applies.

Yandex, like Google, has always been public with its algorithm updates and changes, and in recent years, how it has adopted machine learning.

Notable updates from the past two-three years include:

  • Vega (which doubled the size of the index).
  • Mimicry (penalizing fake websites impersonating brands).
  • Y1 update (introducing YATI).
  • Y2 update (late 2022).
  • Adoption of IndexNow.
  • A fresh rollout and assumed update of the PF filter.

On a personal note, this data leak is like a second Christmas.

Since January 2020, I’ve run an SEO news website as a hobby dedicated to covering Yandex SEO and search news in Russia with 600+ articles, so this is probably the peak event of the hobby site.

I’ve also spoken twice at the Optimization conference – the largest SEO conference in Russia.

This is also a good test to see how closely Yandex’s public statements match the codebase secrets.

In 2019, working with Yandex’s PR team, I was able to interview engineers in their Search team and ask a number of questions sourced from the wider Western SEO community.

You can read the interview with the Yandex Search team here.

Whilst Yandex is primarily known for its presence in Russia, the search engine also has a presence in Turkey, Kazakhstan, and Georgia.

The data leak was believed to be politically motivated and the actions of a rogue employee, and contains a number of code fragments from Yandex’s monolithic repository, Arcadia.

Within the 44GB of leaked data, there’s information relating to a number of Yandex products including Search, Maps, Mail, Metrika, Disc, and Cloud.

What Yandex Has Had To Say

As I write this post (January 31st, 2023), Yandex has publicly stated that:

the contents of the archive (leaked code base) correspond to the outdated version of the repository – it differs from the current version used by our services

And:

It is important to note that the published code fragments also contain test algorithms that were used only within Yandex to verify the correct operation of the services.

So, how much of this code base is actively used is questionable.

Yandex has also revealed that during its investigation and audit, it found a number of errors that violate its own internal principles, so it is likely that portions of this leaked code (that are in current use) may be changing in the near future.

Factor Classification

Yandex classifies its ranking factors into three categories.

This has been outlined in Yandex’s public documentation for some time, but I feel is worth including here, as it better helps us understand the ranking factor leak.

  • Static factors – Factors that are related directly to the website (e.g. inbound backlinks, inbound internal links, headers, and ads ratio).
  • Dynamic factors – Factors that are related to both the website and the search query (e.g. text relevance, keyword inclusions, TF*IDF).
  • User search-related factors – Factors relating to the user query (e.g. where is the user located, query language, and intent modifiers).

The ranking factors in the document are tagged to match the corresponding category, with TG_STATIC and TG_DYNAMIC, and then TG_QUERY_ONLY, TG_QUERY, TG_USER_SEARCH, and TG_USER_SEARCH_ONLY.

Yandex Leak Learnings So Far

From the data thus far, below are some of the affirmations and learnings we’ve been able to make.

There is so much data in this leak, it is very likely that we will be finding new things and making new connections in the next few weeks.

These include:

  • PageRank (a form of).
  • At some point Yandex utilized TF*IDF.
  • Yandex still uses meta keywords, which are also highlighted in its documentation.
  • Yandex has specific factors for medical, legal, and financial topics (YMYL).
  • It also uses a form of page quality scoring, but this is known (ICS score).
  • Links from high-authority websites have an impact on rankings.
  • There’s nothing new to suggest Yandex can crawl JavaScript yet outside of already publicly documented processes.
  • Server errors and excessive 4xx errors can impact ranking.
  • The time of day is taken into consideration as a ranking factor.

Below, I’ve expanded on some other affirmations and learnings from the leak.

Where possible, I’ve also tied these leaked ranking factors to the algorithm updates and announcements that relate to them, or where we were told about them being impactful.

MatrixNet

MatrixNet is mentioned in a few of the ranking factors and was announced in 2009, and then superseded in 2017 by Catboost, which was rolled out across the Yandex product sphere.

This further adds validity to comments directly from Yandex, and one of the factor authors DenPlusPlus (Den Raskovalov), that this is, in fact, an outdated code repository.

MatrixNet was originally introduced as a new, core algorithm that took into consideration thousands of ranking factors and assigned weights based on the user location, the actual search query, and perceived search intent.

It is typically seen as an early version of Google’s RankBrain, when they are indeed two very different systems. MatrixNet was launched six years before RankBrain was announced.

MatrixNet has also been built upon, which isn’t surprising, given it is now 14 years old.

In 2016, Yandex introduced the Palekh algorithm that used deep neural networks to better match documents (webpages) and queries, even if they didn’t contain the right “levels” of common keywords, but satisfied the user intents.

Palekh was capable of processing 150 pages at a time, and in 2017 was updated with the Korolyov update, which took into account more depth of page content, and could work off 200,000 pages at once.

URL & Page-Level Factors

From the leak, we have learned that Yandex takes into consideration URL construction, specifically:

  • The presence of numbers in the URL.
  • The number of trailing slashes in the URL (and if they are excessive).
  • The number of capital letters in the URL is a factor.
Screenshot from author, January 2023

The age of a page (document age) and the last updated date are also important, and this makes sense.

As well as document age and last update, a number of factors in the data relate to freshness – particularly for news-related queries.

Yandex formerly used timestamps, specifically not for ranking purposes but “reordering” purposes, but this is now classified as unused.

Also in the deprecated column are the use of keywords in the URL. Yandex has previously measured that three keywords from the search query in the URL would be an “optimal” result.

Internal Links & Crawl Depth

Whilst Google has gone on the record to say that for its purposes, crawl depth isn’t explicitly a ranking factor, Yandex appears to have an active piece of code that dictates that URLs that are reachable from the homepage have a “higher” level of importance.

Yandex factorsScreenshot from author, January 2023

This mirrors John Mueller’s 2018 statement that Google gives “a little more weight” to pages found more than one click from the homepage.

The ranking factors also highlight a specific token weighting for webpages that are “orphans” within the website linking structure.

Clicks & CTR

In 2011, Yandex released a blog post talking about how the search engine uses clicks as part of its rankings and also addresses the desires of the SEO pros to manipulate the metric for ranking gain.

Specific click factors in the leak look at things like:

  • The ratio of the number of clicks on the URL, relative to all clicks on the search.
  • The same as above, but broken down by region.
  • How often do users click on the URL for the search?

Manipulating Clicks

Manipulating user behavior, specifically “click-jacking”, is a known tactic within Yandex.

Yandex has a filter, known as the PF filter, that actively seeks out and penalizes websites that engage in this activity using scripts that monitor IP similarities and then the “user actions” of those clicks – and the impact can be significant.

The below screenshot shows the impact on organic sessions (сессии) after being penalized for imitating user clicks.

Image Source: Russian Search NewsImage from Russian Search News, January 2023

User Behavior

The user behavior takeaways from the leak are some of the more interesting findings.

User behavior manipulation is a common SEO violation that Yandex has been combating for years. At the 2020 Optimization conference, then Head of Yandex Webmaster Tools Mikhail Slevinsky said the company is making good progress in detecting and penalizing this type of behavior.

Yandex penalizes user behavior manipulation with the same PF filter used to combat CTR manipulation.

Dwell Time

102 of the ranking factors contain the tag TG_USERFEAT_SEARCH_DWELL_TIME, and reference the device, user duration, and average page dwell time.

All but 39 of these factors are deprecated.

Yandex factorsScreenshot from author, January 2023

Bing first used the term Dwell time in a 2011 blog, and in recent years Google has made it clear that it doesn’t use dwell time (or similar user interaction signals) as ranking factors.

YMYL

YMYL (Your Money, Your Life) is a concept well-known within Google and is not a new concept to Yandex.

Within the data leak, there are specific ranking factors for medical, legal, and financial content that exist – but this was notably revealed in 2019 at the Yandex Webmaster conference when it announced the Proxima Search Quality Metric.

Metrika Data Usage

Six of the ranking factors relate to the usage of Metrika data for the purposes of ranking. However, one of them is tagged as deprecated:

  • The number of similar visitors from the YandexBar (YaBar/Ябар).
  • The average time spent on URLs from those same similar visitors.
  • The “core audience” of pages on which there is a Metrika counter [deprecated].
  • The average time a user spends on a host when accessed externally (from another non-search site) from a specific URL.
  • Average ‘depth’ (number of hits within the host) of a user’s stay on the host when accessed externally (from another non-search site) from a particular URL.
  • Whether or not the domain has Metrika installed.

In Metrika, user data is handled differently.

Unlike Google Analytics, there are a number of reports focused on user “loyalty” combining site engagement metrics with return frequency, duration between visits, and source of the visit.

For example, I can see a report in one click to see a breakdown of individual site visitors:

MetrikaScreenshot from Metrika, January 2023

Metrika also comes “out of the box” with heatmap tools and user session recording, and in recent years the Metrika team has made good progress in being able to identify and filter bot traffic.

With Google Analytics, there is an argument that Google doesn’t use UA/GA4 data for ranking purposes because of how easy it is to modify or break the tracking code – but with Metrika counters, they are a lot more linear, and a lot of the reports are unchangeable in terms of how the data is collected.

Impact Of Traffic On Rankings

Following on from looking at Metrika data as a ranking factor; These factors effectively confirm that direct traffic and paid traffic (buying ads via Yandex Direct) can impact organic search performance:

  • Share of direct visits among all incoming traffic.
  • Green traffic share (aka direct visits) – Desktop.
  • Green traffic share (aka direct visits) – Mobile.
  • Search traffic – transitions from search engines to the site.
  • Share of visits to the site not by links (set by hand or from bookmarks).
  • The number of unique visitors.
  • Share of traffic from search engines.

News Factors

There are a number of factors relating to “News”, including two that mention Yandex.News directly.

Yandex.News was an equivalent of Google News, but was sold to the Russian social network VKontakte in August 2022, along with another Yandex product “Zen”.

So, it’s not clear if these factors related to a product no longer owned or operated by Yandex, or to how news websites are ranked in “regular” search.

Backlink Importance

Yandex has similar algorithms to combat link manipulation as Google – and has since the Nepot filter in 2005.

From reviewing the backlink ranking factors and some of the specifics in the descriptions, we can assume that the best practices for building links for Yandex SEO would be to:

  • Build links with a more natural frequency and varying amounts.
  • Build links with branded anchor texts as well as use commercial keywords.
  • If buying links, avoid buying links from websites that have mixed topics.

Below is a list of link-related factors that can be considered affirmations of best practices:

  • The age of the backlink is a factor.
  • Link relevance based on topics.
  • Backlinks built from homepages carry more weight than internal pages.
  • Links from the top 100 websites by PageRank (PR) can impact rankings.
  • Link relevance based on the quality of each link.
  • Link relevance, taking into account the quality of each link, and the topic of each link.
  • Link relevance, taking into account the non-commercial nature of each link.
  • Percentage of inbound links with query words.
  • Percentage of query words in links (up to a synonym).
  • The links contain all the words of the query (up to a synonym).
  • Dispersion of the number of query words in links.

However, there are some link-related factors that are additional considerations when planning, monitoring, and analyzing backlinks:

  • The ratio of “good” versus “bad” backlinks to a website.
  • The frequency of links to the site.
  • The number of incoming SEO trash links between hosts.

The data leak also revealed that the link spam calculator has around 80 active factors that are taken into consideration, with a number of deprecated factors.

This creates the question as to how well Yandex is able to recognize negative SEO attacks, given it looks at the ratio of good versus bad links, and how it determines what a bad link is.

A negative SEO attack is also likely to be a short burst (high frequency) link event in which a site will unwittingly gain a high number of poor quality, non-topical, and potentially over-optimized links.

Yandex uses machine learning models to identify Private Blog Networks (PBNs) and paid links, and it makes the same assumption between link velocity and the time period they are acquired.

Typically, paid-for links are generated over a longer period of time, and these patterns (including link origin site analysis) are what the Minusinsk update (2015) was introduced to combat.

Yandex Penalties

There are two ranking factors, both deprecated, named SpamKarma and Pessimization.

Pessimization refers to reducing PageRank to zero and aligns with the expectations of severe Yandex penalties.

SpamKarma also aligns with assumptions made around Yandex penalizing hosts and individuals, as well as individual domains.

Onpage Advertising

There are a number of factors relating to advertising on the page, some of them deprecated (like the screenshot example below).

Yandex factorsScreenshot from author, January 2023

It’s not known from the description exactly what the thought process with this factor was, but it could be assumed that a high ratio of adverts to visible screen was a negative factor – much like how Google takes umbrage if adverts obfuscate the page’s main content, or are obtrusive.

Tying this back to known Yandex mechanisms, the Proxima update also took into consideration the ratio of useful and advertising content on a page.

Can We Apply Any Yandex Learnings To Google?

Yandex and Google are disparate search engines, with a number of differences, despite the tens of engineers who have worked for both companies.

Because of this fight for talent, we can infer that some of these master builders and engineers will have built things in a similar fashion (though not direct copies), and applied learnings from previous iterations of their builds with their new employers.

What Russian SEO Pros Are Saying About The Leak

Much like the Western world, SEO professionals in Russia have been having their say on the leak across the various Runet forums.

The reaction in these forums has been different to SEO Twitter and Mastodon, with a focus more on Yandex’s filters, and other Yandex products that are optimized as part of wider Yandex optimization campaigns.

It is also worth noting that a number of conclusions and findings from the data match what the Western SEO world is also finding.

Common themes in the Russian search forums:

  • Webmasters asking for insights into recent filters, such as Mimicry and the updated PF filter.
  • The age and relevance of some of the factors, due to author names no longer being at Yandex, and mentions of long-retired Yandex products.
  • The main interesting learnings are around the use of Metrika data, and information relating to the Crawler & Indexer.
  • A number of factors outline the usage of DSSM, which in theory was superseded by the release of Palekh in 2016. This was a search algorithm utilizing machine learning, announced by Yandex in 2016.
  • A debate around ICS scoring in Yandex, and whether or not Yandex may provide more traffic to a site and influence its own factors by doing so.

The leaked factors, particularly around how Yandex evaluates site quality, have also come under scrutiny.

There is a long-standing sentiment in the Russian SEO community that Yandex oftentimes favors its own products and services in search results ahead of other websites, and webmasters are asking questions like:

Why does it bother going to all this trouble, when it just nails its services to the top of the page anyway?

In loosely translated documents, these are referred to as the Sorcerers or Yandex Sorcerers. In Google, we’d call these search engine results pages (SERPs) features – like Google Hotels, etc.

In October 2022, Kassir (a Russian ticket portal) claimed ₽328m compensation from Yandex due to lost revenue, caused by the “discriminatory conditions” in which Yandex Sorcerers took the customer base away from the private company.

This is off the back of a 2020 class action in which multiple companies raised a case with the Federal Antimonopoly Service (FAS) for anticompetitive promotion of its own services.

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Google uppdaterar Search Console Video Indexing Report

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Google Updates Search Console Video Indexing Report

Google’s updated Search Console Video indexing report now includes daily video impressions and a sitemap filter feature.

  • Google has updated the Search Console Video indexing report to provide more comprehensive insights into video performance in search results.
  • The updated report includes daily video impressions, which are grouped by page, and a new sitemap filter feature to focus on the most important video pages.
  • These updates are part of Google’s ongoing efforts to help website owners and content creators understand and improve the visibility of their videos in search results.



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