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Google December 2020 Core Update Insights

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Five search marketers contributed opinions on Google’s December 2020 Core Update. The observations offer interesting feedback on what may have happened.

In my opinion, Google updates have increasingly been less about ranking factors and more about improving how queries and web pages are understood.

Some have offered the opinion that Google is randomizing search results in order to fool those who try to reverse engineer Google’s algorithm.

I don’t share that opinion.

Certain algorithm features are hard to detect in the search results. It’s not easy to to point at a search result and say it is ranking because of the BERT algorithm or Neural Matching.

But it is easy to point to backlinks, E-A-T or user experience as reasons to explain why a site is ranking or not ranking if that’s what’s sticking out, even when the actual reason might be more related to BERT.

So the Search Engine Results Pages (SERPs) may appear confusing and random to those who are scrutinizing the SERPs looking for traditional old school ranking factors to explain why pages are ranking or why they lost rankings in an update.

Of course the Google Updates may appear to be inscrutable. The reasons why web pages rank have dramatically changed over the past few years because of technologies like natural language processing.

What if Google Updates and Nobody Sees What Changed?

It’s happened in the past that Google has changed something and the SEO community didn’t notice.

For example, when Google added an algorithm like BERT many couldn’t detect what had changed.

Now, what if Google added something like the SMITH algorithm? How would the SEO community detect that?

SMITH is described in a Google Research paper published in April 2020 and revised in October 2020. What SMITH does is make it easier to understand a long page of content, outperforming BERT.

Here is what it says:

“In recent years, self-attention based models like Transformers and BERT have achieved state-of-the-art performance in the task of text matching.

These models, however, are still limited to short text like a few sentences or one paragraph due to the quadratic computational complexity of self-attention with respect to input text length.

In this paper, we address the issue by proposing the Siamese Multi-depth Transformer-based Hierarchical (SMITH) Encoder for long-form document matching.

Our experimental results on several benchmark datasets for long-form document matching show that our proposed SMITH model outperforms the previous state-of-the-art models including hierarchical attention, multi-depth attention-based hierarchical recurrent neural network, and BERT.

Comparing to BERT based baselines, our model is able to increase maximum input text length from 512 to 2048.”

I’m not saying that Google has introduced the SMITH algorithm (PDF) or that it’s related to the Passages Algorithm.

What I am pointing out is that the December 2020 Core Update contains the quality of seemingly non-observable changes.

If Google added a new AI based feature or updated an existing feature like BERT, would the search marketing community be able to detect it? Probably not.

And it is that quality of non-observable changes that may indicate that what has changed might have something to do with how Google understands web queries and web pages.

If that is the case, then it may mean that instead of spinning wheels on the usual ranking factors that are easily observed (links from scraper sites, site speed, etc.), that it may be useful to step back and consider that it may be something more profound than the usual ranking factors that has changed.

Insights into Google December 2020 Core Update

I thank those who had time to contribute their opinions, they provided excellent information that may help you to put Google’s December Core Algorithm Update into perspective.

Dave Davies (@oohloo)
Beanstalk Internet Marketing

Dave puts this update in the context of what Google has said was coming soon to the algorithm and how that might play a role in the fluctuations.

Dave offered:

“The December 2020 Core Update was a unique one to watch roll out. Many sites we work with started with losses and ended with wins, and vice-versa.

So clearly it had something to do with a signal or signals that cascade. That is, where the change caused one result, but once that new calculation worked its way through the system, it produced another. Like PageRank recalculating, though this one likely had nothing to do with PageRank.

Alternatively, Google may have made adjustments on the fly, or made other changes during the rollout, but I find that less likely.

If we think about the timing, and how it ties to the rolling out of passage indexing and that it’s a Core Update, I suspect it ties to content interpretation systems and not links or signals along those lines.

We also know that Core Web Vitals are entering the algorithm in May of 2021 so there may be elements to support that in the update, but those would not be producing the impact we’ve all been seeing presently given that Web Vitals should technically be inert as a signal at this stage so at the very least, there would be more to the update than that.

As far as general community reaction, this one has been difficult to gauge past “it was big.” As one can expect in any zero-sum scenario, when one person is complaining about a loss, another is smiling all the way up the SERPs.

I suspect that before the end of January it’ll become clear exactly what they were rolling out and why. I believe it has to do with future features and capabilities, but I’ve been around long enough to know I could be wrong, and I need to watch closely.”

Steven Kang (@SEOSignalsLab)

Steven Kang, founder of the popular SEO Signals Lab Facebook group notes that nothing appears to stand out in terms of commonalities or symptoms between the winners and losers.

“This one seems to be tricky. I’m finding gains and losses. I would need to wait more for this one.”

Daniel K Cheung (@danielkcheung)
Team Lead, Prosperity Media

Daniel believes that it’s helpful to step back and view Google updates from the big picture view of the forest rather than the tree of the latest update, and to put these updates into the context of what we know is going on in Search.

One example is the apparent drop in reports of manual actions in Google Search Console. The implication is, does that mean Google is better at ranking sites where they belong, without having to resort to punitive manual actions?

This is how Daniel views the latest core algorithm update from Google:

“I think we as Search/Discoverability people need to stop thinking about Core Updates as individual events and instead look at Core Updates as a continuum of ongoing tests and ‘improvements’ to what we see in the SERPs.

So when I refer to the December core update, I want to stress that it is just one event of many.

For example, some affiliate marketers and analysts have found sites that were previously ‘hit’ by the May 2020 update to have recovered in the December rollout. However, this has not been consistent.

And again, here is the problem, we can’t talk about sites that have won or lost because it’s all about individual URLs.

So looking at pure visibility across an entire website doesn’t really give us any clues.

There are murmurs of 301 redirects, PBNs, low-quality backlinks and poor content being reasons why some sites have been pushed from page 1 to page 6-10 of the SERPs (practically invisible).

But these practices have always been susceptible to the daily fluctuations of the algorithm.

What’s been really interesting throughout 2020 is that there have been very few reports of manual penalties within GSC.

This has been eerily replaced with impression and click graphs jumping off a cliff without the site being de-indexed.

In my humble opinion, core updates are becoming less about targeting a specific selection of practices, but rather, an incremental opportunity for the algorithm to mature.

Now, I’m not saying that Google gets it right 100% of the time – the algorithm clearly doesn’t and I don’t think it ever will (due to humanity’s curiosity).”

Cristoph Cemper (@cemper)
CEO LinkResearchTools

Cristoph Cemper views the latest update as having an impact across a wide range of factors.

Here is what he shared:

“High level, Google is adjusting things that have a global impact in core updates.

That is:

a) Weight ratios for different types of links, and their signals

I think the NoFollow 2.0 rollout from Sept 2019 is not completed, but tweaked. I.e. how much power for which NoFollow in which context.

b) Answer boxes, a lot more. Google increases their own real estate

c) Mass devaluation of PBN link networks and quite obvious footprints of “outreach link building.”

Just because someone sent an outreach email doesn’t make a paid link more natural, even if it was paid with “content” or “exchange of services.”

Michael Martinez (@seo_theory)
Founder of SEOTheory

Michael Martinez offered these insights:

“Based on what I’ve seen in online discussions, people are confused and frustrated. They don’t really know what happened and few seem to have any theories as to why things changed.

In a general sense, it feels to me like Google rewrote a number of its quality policy enforcement algorithms.

Nothing specific in mind but other people’s sites I’ve looked at struck me as being okay, not great. Some of the sites in our portfolio went up, others went down.

Again, it just struck me as being about enforcement or algorithmic interpretation of signals mapped to their guidelines.

Not about punishing anything, but maybe about trying some different approaches to resolving queries.”

What Happened in Google December 2020 Core Update?

The perspectives on what happened in Google’s core algorithm update vary. What most observers seem to agree is that no obvious factors or changes seem to stand out.

And that’s an interesting observation because it could mean that something related to AI or Natural Language Processing was refined or introduced. But that’s just speculation until Google explicitly rules it out or in.

Searchenginejournal.com

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OpenAI Introduces ChatGPT Plus with Monthly Subscription of $20

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Open AI - Chat GPT

OpenAI, the leading artificial intelligence research laboratory, has launched a new product – ChatGPT Plus. The new product is an advanced version of its previous language model, ChatGPT, and is available for a monthly subscription of $20. The company aims to provide a more sophisticated and efficient conversational AI tool to its users through this new product.

ChatGPT Plus is a state-of-the-art language model that uses advanced deep learning algorithms to generate human-like responses to text inputs. The model has been trained on a massive corpus of text data, allowing it to generate coherent and contextually relevant responses. The model is designed to handle a wide range of conversational topics and can be integrated into various applications, such as chatbots, customer support systems, and virtual assistants.

One of the main advantages of ChatGPT Plus over its predecessor, ChatGPT, is its ability to generate responses in a more human-like manner. The model has been fine-tuned to incorporate more advanced language processing techniques, which enable it to better understand the context and tone of a conversation. This makes it possible for the model to generate more nuanced and appropriate responses, which can greatly improve the user experience.

In addition to its advanced language processing capabilities, ChatGPT Plus also offers improved performance in terms of response generation speed and efficiency. The model has been optimized to run on faster hardware and has been fine-tuned to generate responses more quickly. This makes it possible for the model to handle a larger volume of requests, making it an ideal solution for businesses with high traffic websites or customer support centers.

The monthly subscription fee of $20 for ChatGPT Plus makes it an affordable solution for businesses of all sizes. The company has designed the pricing model in such a way that it is accessible to businesses of all sizes, regardless of their budget. This makes it possible for small businesses to take advantage of advanced conversational AI technology, which can greatly improve their customer engagement and support.

OpenAI has also made it easy to integrate ChatGPT Plus into various applications. The company has provided a comprehensive API that allows developers to easily integrate the model into their applications. The API supports a wide range of programming languages, making it possible for developers to use the technology regardless of their preferred programming language. This makes it possible for businesses to quickly and easily incorporate conversational AI into their operations.

In conclusion, OpenAI’s launch of ChatGPT Plus is a significant development in the field of conversational AI. The new product offers advanced language processing capabilities and improved performance, making it an ideal solution for businesses of all sizes. The affordable pricing model and easy integration make it accessible to businesses of all sizes, and the advanced language processing capabilities make it possible for businesses to improve their customer engagement and support. OpenAI’s ChatGPT Plus is set to revolutionize the conversational AI industry and bring advanced technology within the reach of businesses of all sizes.

Visit OpenAI.com to read more and to get the latest news about ChatGPT.

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What can ChatGPT do?

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ChatGPT Explained

ChatGPT is a large language model developed by OpenAI that is trained on a massive amount of text data. It is capable of generating human-like text and has been used in a variety of applications, such as chatbots, language translation, and text summarization.

One of the key features of ChatGPT is its ability to generate text that is similar to human writing. This is achieved through the use of a transformer architecture, which allows the model to understand the context and relationships between words in a sentence. The transformer architecture is a type of neural network that is designed to process sequential data, such as natural language.

Another important aspect of ChatGPT is its ability to generate text that is contextually relevant. This means that the model is able to understand the context of a conversation and generate responses that are appropriate to the conversation. This is accomplished by the use of a technique called “masked language modeling,” which allows the model to predict the next word in a sentence based on the context of the previous words.

One of the most popular applications of ChatGPT is in the creation of chatbots. Chatbots are computer programs that simulate human conversation and can be used in customer service, sales, and other applications. ChatGPT is particularly well-suited for this task because of its ability to generate human-like text and understand context.

Another application of ChatGPT is language translation. By training the model on a large amount of text data in multiple languages, it can be used to translate text from one language to another. The model is able to understand the meaning of the text and generate a translation that is grammatically correct and semantically equivalent.

In addition to chatbots and language translation, ChatGPT can also be used for text summarization. This is the process of taking a large amount of text and condensing it into a shorter, more concise version. ChatGPT is able to understand the main ideas of the text and generate a summary that captures the most important information.

Despite its many capabilities and applications, ChatGPT is not without its limitations. One of the main challenges with using language models like ChatGPT is the risk of generating text that is biased or offensive. This can occur when the model is trained on text data that contains biases or stereotypes. To address this, OpenAI has implemented a number of techniques to reduce bias in the training data and in the model itself.

In conclusion, ChatGPT is a powerful language model that is capable of generating human-like text and understanding context. It has a wide range of applications, including chatbots, language translation, and text summarization. While there are limitations to its use, ongoing research and development is aimed at improving the model’s performance and reducing the risk of bias.

** The above article has been written 100% by ChatGPT. This is an example of what can be done with AI. This was done to show the advanced text that can be written by an automated AI.

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Google December Product Reviews Update Affects More Than English Language Sites? via @sejournal, @martinibuster

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Google’s Product Reviews update was announced to be rolling out to the English language. No mention was made as to if or when it would roll out to other languages. Mueller answered a question as to whether it is rolling out to other languages.

Google December 2021 Product Reviews Update

On December 1, 2021, Google announced on Twitter that a Product Review update would be rolling out that would focus on English language web pages.

The focus of the update was for improving the quality of reviews shown in Google search, specifically targeting review sites.

A Googler tweeted a description of the kinds of sites that would be targeted for demotion in the search rankings:

“Mainly relevant to sites that post articles reviewing products.

Think of sites like “best TVs under $200″.com.

Goal is to improve the quality and usefulness of reviews we show users.”

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Google also published a blog post with more guidance on the product review update that introduced two new best practices that Google’s algorithm would be looking for.

The first best practice was a requirement of evidence that a product was actually handled and reviewed.

The second best practice was to provide links to more than one place that a user could purchase the product.

The Twitter announcement stated that it was rolling out to English language websites. The blog post did not mention what languages it was rolling out to nor did the blog post specify that the product review update was limited to the English language.

Google’s Mueller Thinking About Product Reviews Update

Screenshot of Google's John Mueller trying to recall if December Product Review Update affects more than the English language

Screenshot of Google's John Mueller trying to recall if December Product Review Update affects more than the English language

Product Review Update Targets More Languages?

The person asking the question was rightly under the impression that the product review update only affected English language search results.

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But he asserted that he was seeing search volatility in the German language that appears to be related to Google’s December 2021 Product Review Update.

This is his question:

“I was seeing some movements in German search as well.

So I was wondering if there could also be an effect on websites in other languages by this product reviews update… because we had lots of movement and volatility in the last weeks.

…My question is, is it possible that the product reviews update affects other sites as well?”

John Mueller answered:

“I don’t know… like other languages?

My assumption was this was global and and across all languages.

But I don’t know what we announced in the blog post specifically.

But usually we try to push the engineering team to make a decision on that so that we can document it properly in the blog post.

I don’t know if that happened with the product reviews update. I don’t recall the complete blog post.

But it’s… from my point of view it seems like something that we could be doing in multiple languages and wouldn’t be tied to English.

And even if it were English initially, it feels like something that is relevant across the board, and we should try to find ways to roll that out to other languages over time as well.

So I’m not particularly surprised that you see changes in Germany.

But I also don’t know what we actually announced with regards to the locations and languages that are involved.”

Does Product Reviews Update Affect More Languages?

While the tweeted announcement specified that the product reviews update was limited to the English language the official blog post did not mention any such limitations.

Google’s John Mueller offered his opinion that the product reviews update is something that Google could do in multiple languages.

One must wonder if the tweet was meant to communicate that the update was rolling out first in English and subsequently to other languages.

It’s unclear if the product reviews update was rolled out globally to more languages. Hopefully Google will clarify this soon.

Citations

Google Blog Post About Product Reviews Update

Product reviews update and your site

Google’s New Product Reviews Guidelines

Write high quality product reviews

John Mueller Discusses If Product Reviews Update Is Global

Watch Mueller answer the question at the 14:00 Minute Mark

[embedded content]Searchenginejournal.com

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