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Bing Explains SEO For AI Search

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Bing Explains SEO For AI Search

AI search is inevitable so it’s vital for SEO to understand everything about it. An interview with Bing’s Fabrice Canel revealed interesting insights about this topic with some takeaways that offer some insights on the future of search.

Fabrice Canel is the Principal Product Manager at Bing and because of his position there he is in the position to know more about AI search from the search engine side, something we don’t get to see.

AI Search Clicks Are Valuable

Something of special interest for SEO professionals is his discussion about what host Jason Barnard calls the perfect click and what Fabrice referred to as qualified clicks.

I’ve noticed that contextual links are in some versions of Google SGE and is also a main feature of the search engine results pages (SERPs) of some of the newer AI search engines like Perplexity AI.

Bing AI search also shows citations to websites where users can dig deeper into the topic that is relevant to them at that moment.

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Fabrice talks about how these links to websites that are shown to users from AI search are more valuable than standard links from a regular search engine.

He uses the phrase “qualified clicks” to refer to traffic to websites that originate from from AI search.

Fabrice (at the 6 minute mark of the video):

“Bing is all about satisfying the end user and sometimes it’s all about exploring the web.

But sometimes it’s all about understanding the web and providing this kind of experience where at the end we can learn the clicks to the website having extremely qualified clicks.

And this is something we’ve seen where clearly when people are clicking…

And this translates to a benefit for the end user, for the website more, certainly more, than [from a] search engine, typical search engine.”

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What he’s saying is that there is more context in the interaction between users and Bing, which results in better answers and in turn better traffic, qualified clicks.

Why AI Search Clicks Are Better Than Normal Search Clicks

Fabrice explains in more detail why clicks from AI search are better than from a regular search engine.

He explains that user interaction provides Bing with more search query context, which in turn allows Bing to offer links to the exact site that offers the answers that the user is looking for.

Users provide so much query information that the click to the website is essentially a perfect click, the qualified click.

Fabrice answered:

“Yeah. So fundamentally, …we have abilities to do in Bing Chat what we don’t really have out of the box of in search.

It’s a little bit more time to really go deep in understanding the query, understanding what the query can return as results.

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So this is all about at the end being able to have an orchestration between the queries itself and the ranking and the profile of a user to really go deeper in understanding what the user is looking for and retrieving from the set of content.”

These interactions are so rich in data about what users want that it allows Bing to make their search even better.

And the better Bing understands user queries the better the traffic that it sends.

What’s important about that insight is that it can very much apply to traffic from other AI search engines.

Let’s take that idea a little further.

If AI search engines understand what is asked of them, then they are better able to provide the correct answers. That makes offering ten blue links no longer necessary.

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It very much resembles the interaction between humans, where one will ask the other something and receives a response.

Nobody needs to respond with ten answers, right? The same applies for AI search.

What’s extraordinary is that AI search not only helps users, but it allows Bing to become better in satisfying user queries.

Fabrice continued:

“…this new technology helps us to improve even faster and certainly better to satisfy even more users.

We see that the satisfaction of users at Bing has really improved even more in the last six months than before.

So this is continuous improvement of the technology.”

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AI Search Is Not About Keyword Matching

Fabrice next speaks about keywords in AI Search.

He says that the technology is not in any way about matching keywords (terms) in the query to keywords on a webpage.

He noted:

“… the technology has evolved.

This is not about …term matching, this is really understanding the context of a query, the context of a user to really retrieve the best content on the internet.”

The AI search experience again resembles a conversation between humans, where when you provide an answer to a question, using the keywords in the question is not something you consciously do, right? Your focus is on providing an answer.

AI search understands the full context of the question and answers it, just like you would.

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Ranking In AI Search – Role of Verbs And Keywords

Fabrice next says that keywords matter, not because the AI search engine is matching keywords to queries but rather, the keywords help Bing understand what the page is about.

This is an important insight. It reinforces one of the most important trends of the past several years about how SEOs need to be precise in communicating what a page is about.

Keywords matter to the extent that they tell the search engine what the page is about.

Fabrice explains [16:34 minute mark]:

“So the verbs of a user matter even more these days.

People want to say, I want to book a ticket to this thing and “book” maybe not really in the content of a page, but we know that it’s a booking activity.

So maybe this is all about retrieving the event itself where people then can book the concert.

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So think technology really improve, don’t think about keyword …and so on, think about satisfying the user for a set of queries that they think they will do.

…obviously, if a customer specify a verb, this is important, but if a customer …do not specify a verb, then this is all about understanding the context of this query in this specific chat, of the ability to understand what the session was all about.

Because maybe you want to search, give me a restaurant…

Maybe we will give a list of restaurants near you and then you can say, hey, I want a vegetarian restaurant. Okay?

And then you have a list of vegetarian restaurants, or give me a vegetarian one and give me one that can accommodate 20 people.

So again, you don’t repeat the restaurant [can’t understand], you just continue the chat experience and we have a full context of the full session and helping to reply [to] your question.

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And for search engine optimization, …It means at the end that you may care about keyword and you should care about keyword because we need to know that it’s a restaurant for vegetarian, we need to know that it can accommodate a large group of people.

But this is less about really matching this query. This is again, not really matching, this is matching what people are searching, looking for.”

What’s The Connection Between AI And Search Algorithms?

Jason Barnard next asks if the Bing chat algorithm and the search algorithm are the same.

Fabrice answers [19:35 minute mark]:

“This is an excellent question.

So first of all, in Bing Chat and search we benefit obviously of a big index.

It’s not, let’s say, a large language model store that you interact here.

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Here, we benefit from not only this new tech…, but also by having deep interaction with the index itself.

So mean that …we are doing multiple queries and retrieving from this query the best content on the Internet. It’s not a static set, it’s a dynamic set.

You benefit from having the latest content and index and we have technology for that to make sure that content can be indexed, latest content can be indexed in seconds.

But it’s really this kind of interaction with the best content on the Internet that we can retrieve and we do multiple queries to retrieve.

So overall I will share that the technology is the same, but in chat there is even more complex queries that are done to really retrieve the content, analyze the content.

Chat give us access to more time to do a little bit more things, understanding, also interacting deeper with the user via the chat experience and session, where we can also not only suggest text, suggest verbs that the customer can do to continue the discussion with a search engine to retrieve the best content on the Internet.”

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Will Ten Blue Links Disappear?

The ten blue links have been going away as a standard in traditional search engines for many years, more than a decade.

Where does the ten blue links paradigm fit into AI search?

Surprisingly, ten blue links still have a place in AI search.

Fabrice answered [30:55 minute mark]:

“Yeah, again, personally I do not believe that.

Again, don’t know if mindset of people evolve and they really prefer chat, why not?

But again, I still feel that there is a set of queries where the ten blue links are really satisfying the user today.

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And so this means again you query for specific query, navigational query.

You just certainly don’t want at least me, I don’t want to have an experience where there is asking me more questions.

No, no, I want to click this link, this is the link.

I know where I want to go. I don’t remember the domain names, but I want to go there.

And so this is kind of a directory address book where okay, I know this is perfect. Thank you. I’m done.

I am visiting the site now.

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And this is then you don’t really need a huge experience and you need really this navigational… And so blue links satisfy your need.”

The takeaway then is that there are certain contexts where users need the ten blue links and that it doesn’t make sense to drag the full chat experience into those kinds of queries.

Two Things To Do For LLM Search

Fabrice later discusses what SEOs should do for AI search.

He basically says to make it easy to get indexed because building an index on the LLM side can take years.

The first thing is to adopt IndexNow for incredibly fast indexing. On the AI side, the LLM can take months to years to be up to date.

Fabrice said:

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“…using IndexNow, you will get your content indexed in seconds.

…In LLM, it takes weeks, months, more likely years to build the new LLM tech.

So this is important for the SEO community because you have to do it right now, as soon as possible, to be a part of the next LLM version.”

The second thing that Fabrice suggested that the SEO community do is to make the content easily accessible by search engines.

Fabrice continued:

“Second is …have your content based on a basic template.

Don’t do the craziness things with plenty of Ajax calls to retrieve the content that the developer developing that says it will be great, but for a search engine it will be a disaster.

Machine learning is all about learning from a set of documents and then aligning to some judgment.

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The more basic you are, the more standardized you are, the better it is for the search engine.

And as part of this you really want to help the content to be understood by search engines, means not only add HTML tags, the appropriate HTML title to differentiate the headings from the paragraph. And so on.

But add structured data to help the index and help the LLM to really understand what this is all about. What your content is all about.

So all this information is leveraged, real time as soon as we call the page for the index.

LLM has a different lifecycle. …LLM are not built at the same lifecycle as building an index.

Building an index is real-time. In an LLM it takes weeks, months, more likely years, to build the new LLM tech.

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So this is important for the SEO community because you have to do it right now, as soon as possible to be a part of the next LLM version.

If you think of the old search engines, this is kind of the lifecycle that you need to target.

Fundamentally this about doing the right thing now, today.

And …doing the right things will benefit not only for search engine indexing, but also for LLMs.”

Bing Avoids Big Updates

Something interesting that Fabrice mentioned is that they try to avoid disruptive changes in rankings, which is different from the way Google’s core algorithm updates function. Instead, he described a process that is always changing.

Fabrice said:

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“At Bing we in general avoid this kind of big change. Because this is constantly ongoing, meaning there are always improvements.

The lifecycle of a Bing engineer is to dream of a relevance improvement, to go to work in the morning to be able to code and test this experiment and in the afternoon this engineer will start to get feedback.

And the next day it’s good, then they can start …testing and rolling out the change.

So this is multiple hundreds of experiments that are done in the course of a day to really test things.

The ones that are good go live.

So this is continuous improvement, it’s not waves of improvement as we may see often in other search engines.”

Understanding SEO For AI Search

Learning what LLM search is about is critical because AI search is upon us right now. It may be in beta status like Google SGE or it may still be evolving, like Bing, as users figure out for themselves how to best use AI search.

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As search professionals we need to get on board with certain ideas and practices:

  • Don’t think in terms of keyword matching but rather use keywords to help the content become easy to understand what it’s about.
  • Consider verbs that users may use to ask questions in order to better align your content to be relevant to their queries.
  • Links from AI search are qualified, they’re on target.
  • Use structured data.
  • Use IndexNow in order to help your content get indexed fast.
  • Avoid complex websites as best that you can.
  • Blue links are not entirely going away.

Watch the video:

How does Generative AI in Search Work and What is Coming in 2024

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How to Revive an Old Blog Article for SEO

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Step-by-Step: How to Optimize Old Blog Posts for SEO

Quick question: What do you typically do with your old blog posts? Most likely, the answer is: Not much.

If that’s the case, you’re not alone. Many of us in SEO and content marketing tend to focus on continuously creating new content, rather than leveraging our existing blog posts.

However, here’s the reality—Google is becoming increasingly sophisticated in evaluating content quality, and we need to adapt accordingly. Just as it’s easier to encourage existing customers to make repeat purchases, updating old content on your website is a more efficient and sustainable strategy in the long run.

Ways to Optimize Older Content 

Some of your old content might not be optimized for SEO very well, rank for irrelevant keywords, or drive no traffic at all. If the quality is still decent, however, you should be able to optimize it properly with little effort. 

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Refresh Content 

If your blog post contains a specific year or mentions current events, it may become outdated over time. If the rest of the content is still relevant (like if it’s targeting an evergreen topic), simply updating the date might be all you need to do.

Rewrite Old Blog Posts 

When the content quality is low (you might have greatly improved your writing skills since you’ve written the post) but the potential is still there, there’s not much you can do apart from rewriting an old blog post completely. 

This is not a waste—you’re saving time on brainstorming since the basic structure is already in place. Now, focus on improving the quality.

Delete Old Blog Posts 

You might find a blog post that just seems unusable. Should you delete your old content? It depends. If it’s completely outdated, of low quality, and irrelevant to any valuable keywords for your website, it’s better to remove it. 

Once you decide to delete the post, don’t forget to set up a 301 redirect to a related post or page, or to your homepage.

Promote Old Blog Posts 

Sometimes all your content needs is a bit of promotion to start ranking and getting traffic again. Share it on your social media, link to it from a new post – do something to get it discoverable again to your audience. This can give it the boost it needs to attract organic links too.

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Which Blog Posts Should You Update?

Deciding when to update or rewrite blog posts is a decision that relies on one important thing: a content audit. 

Use your Google Analytics to find out which blog posts used to drive tons of traffic, but no longer have the same reach. You can also use Google Search Console to find out which of your blog posts have lost visibility in comparison to previous months. I have a guide on website analysis using Google Analytics and Google Search Console you can follow.

If you use keyword tracking tools like SE Ranking, you can also use the data it provides to come up with a list of blog posts that have dropped in the rankings. 

Make data-driven decisions to identify which blog posts would benefit from these updates – i.e., which ones still have the chance to recover their keyword rankings and organic traffic. 

With Google’s helpful content update, which emphasizes better user experiences, it’s crucial to ensure your content remains relevant, valuable, and up-to-date.

How To Update Old Blog Posts for SEO

Updating articles can be an involved process. Here are some tips and tactics to help you get it right.

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Author’s Note: I have a Comprehensive On-Page SEO Checklist you might also be interested in following while you’re doing your content audit.

Conduct New Keyword Research

Updating your post without any guide won’t get you far. Always do your keyword research to understand how users are searching for your given topic. 

Proper research can also show you relevant questions and sections that can be added to the blog post you’re updating or rewriting. Make sure to take a look at the People Also Ask (PAA) section that shows up when you search for your target keyword. Check out other websites like Answer The Public, Reddit, and Quora to see what users are looking for too. 

Look for New Ranking Opportunities

When trying to revive an old blog post for SEO, keep an eye out for new SEO opportunities (e.g., AI Overview, featured snippets, and related search terms) that didn’t exist when you first wrote your blog post. Some of these features can be targeted by the new content you will add to your post, if you write with the aim to be eligible for it. 

Rewrite Headlines and Meta Tags

If you want to attract new readers, consider updating your headlines and meta tags. 

Your headlines and meta tags should fulfill these three things:

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  1. Reflect the rewritten and new content you’ve added to the blog post.
  2. Be optimized for the new keywords it’s targeting (if any).
  3. Appeal to your target audience – who may have changed tastes from when the blog post was originally made. 

Remember that your meta tags in particular act like a brief advertisement for your blog post, since this is what the user first sees when your blog post is shown in the search results page. 

Take a look at your blog post’s click-through rate on Google Search Console – if it falls below 2%, it’s definitely time for new meta tags. 

Replace Outdated Information and Statistics

Updating blog content with current studies and statistics enhances the relevance and credibility of your post. By providing up-to-date information, you help your audience make better, well-informed decisions, while also showing that your content is trustworthy.

Tighten or Expand Ideas

Your old content might be too short to provide real value to users – or you might have rambled on and on in your post. It’s important to evaluate whether you need to make your content more concise, or if you need to elaborate more. 

Keep the following tips in mind as you refine your blog post’s ideas:

  • Evaluate Helpfulness: Measure how well your content addresses your readers’ pain points. Aim to follow the E-E-A-T model (Experience, Expertise, Authoritativeness, Trustworthiness).
  • Identify Missing Context: Consider whether your content needs more detail or clarification. View it from your audience’s perspective and ask if the information is complete, or if more information is needed.
  • Interview Experts: Speak with industry experts or thought leaders to get fresh insights. This will help support your writing, and provide unique points that enhance the value of your content.
  • Use Better Examples: Examples help simplify complex concepts. Add new examples or improve existing ones to strengthen your points.
  • Add New Sections if Needed: If your content lacks depth or misses a key point, add new sections to cover these areas more thoroughly.
  • Remove Fluff: Every sentence should contribute to the overall narrative. Eliminate unnecessary content to make your post more concise.
  • Revise Listicles: Update listicle items based on SEO recommendations and content quality. Add or remove headings to stay competitive with higher-ranking posts.

Improve Visuals and Other Media

No doubt that there are tons of old graphics and photos in your blog posts that can be improved with the tools we have today. Make sure all of the visuals used in your content are appealing and high quality. 

Update Internal and External Links

Are your internal and external links up to date? They need to be for your SEO and user experience. Outdated links can lead to broken pages or irrelevant content, frustrating readers and hurting your site’s performance.

You need to check for any broken links on your old blog posts, and update them ASAP. Updating your old blog posts can also lead to new opportunities to link internally to other blog posts and pages, which may not have been available when the post was originally published.

Optimize for Conversions

When updating content, the ultimate goal is often to increase conversions. However, your conversion goals may have changed over the years. 

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So here’s what you need to check in your updated blog post. First, does the call-to-action (CTA) still link to the products or services you want to promote? If not, update it to direct readers to the current solution or offer.

Second, consider where you can use different conversion strategies. Don’t just add a CTA at the end of the post. 

Last, make sure that the blog post leverages product-led content. It’s going to help you mention your products and services in a way that feels natural, without being too pushy. Being subtle can be a high ROI tactic for updated posts.

Key Takeaway

Reviving old blog articles for SEO is a powerful strategy that can breathe new life into your content and boost your website’s visibility. Instead of solely focusing on creating new posts, taking the time to refresh existing content can yield impressive results, both in terms of traffic and conversions. 

By implementing these strategies, you can transform old blog posts into valuable resources that attract new readers and retain existing ones. So, roll up your sleeves, dive into your archives, and start updating your content today—your audience and search rankings will thank you!

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How Compression Can Be Used To Detect Low Quality Pages

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Compression can be used by search engines to detect low-quality pages. Although not widely known, it's useful foundational knowledge for SEO.

The concept of Compressibility as a quality signal is not widely known, but SEOs should be aware of it. Search engines can use web page compressibility to identify duplicate pages, doorway pages with similar content, and pages with repetitive keywords, making it useful knowledge for SEO.

Although the following research paper demonstrates a successful use of on-page features for detecting spam, the deliberate lack of transparency by search engines makes it difficult to say with certainty if search engines are applying this or similar techniques.

What Is Compressibility?

In computing, compressibility refers to how much a file (data) can be reduced in size while retaining essential information, typically to maximize storage space or to allow more data to be transmitted over the Internet.

TL/DR Of Compression

Compression replaces repeated words and phrases with shorter references, reducing the file size by significant margins. Search engines typically compress indexed web pages to maximize storage space, reduce bandwidth, and improve retrieval speed, among other reasons.

This is a simplified explanation of how compression works:

  • Identify Patterns:
    A compression algorithm scans the text to find repeated words, patterns and phrases
  • Shorter Codes Take Up Less Space:
    The codes and symbols use less storage space then the original words and phrases, which results in a smaller file size.
  • Shorter References Use Less Bits:
    The “code” that essentially symbolizes the replaced words and phrases uses less data than the originals.

A bonus effect of using compression is that it can also be used to identify duplicate pages, doorway pages with similar content, and pages with repetitive keywords.

Research Paper About Detecting Spam

This research paper is significant because it was authored by distinguished computer scientists known for breakthroughs in AI, distributed computing, information retrieval, and other fields.

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Marc Najork

One of the co-authors of the research paper is Marc Najork, a prominent research scientist who currently holds the title of Distinguished Research Scientist at Google DeepMind. He’s a co-author of the papers for TW-BERT, has contributed research for increasing the accuracy of using implicit user feedback like clicks, and worked on creating improved AI-based information retrieval (DSI++: Updating Transformer Memory with New Documents), among many other major breakthroughs in information retrieval.

Dennis Fetterly

Another of the co-authors is Dennis Fetterly, currently a software engineer at Google. He is listed as a co-inventor in a patent for a ranking algorithm that uses links, and is known for his research in distributed computing and information retrieval.

Those are just two of the distinguished researchers listed as co-authors of the 2006 Microsoft research paper about identifying spam through on-page content features. Among the several on-page content features the research paper analyzes is compressibility, which they discovered can be used as a classifier for indicating that a web page is spammy.

Detecting Spam Web Pages Through Content Analysis

Although the research paper was authored in 2006, its findings remain relevant to today.

Then, as now, people attempted to rank hundreds or thousands of location-based web pages that were essentially duplicate content aside from city, region, or state names. Then, as now, SEOs often created web pages for search engines by excessively repeating keywords within titles, meta descriptions, headings, internal anchor text, and within the content to improve rankings.

Section 4.6 of the research paper explains:

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“Some search engines give higher weight to pages containing the query keywords several times. For example, for a given query term, a page that contains it ten times may be higher ranked than a page that contains it only once. To take advantage of such engines, some spam pages replicate their content several times in an attempt to rank higher.”

The research paper explains that search engines compress web pages and use the compressed version to reference the original web page. They note that excessive amounts of redundant words results in a higher level of compressibility. So they set about testing if there’s a correlation between a high level of compressibility and spam.

They write:

“Our approach in this section to locating redundant content within a page is to compress the page; to save space and disk time, search engines often compress web pages after indexing them, but before adding them to a page cache.

…We measure the redundancy of web pages by the compression ratio, the size of the uncompressed page divided by the size of the compressed page. We used GZIP …to compress pages, a fast and effective compression algorithm.”

High Compressibility Correlates To Spam

The results of the research showed that web pages with at least a compression ratio of 4.0 tended to be low quality web pages, spam. However, the highest rates of compressibility became less consistent because there were fewer data points, making it harder to interpret.

Figure 9: Prevalence of spam relative to compressibility of page.

The researchers concluded:

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“70% of all sampled pages with a compression ratio of at least 4.0 were judged to be spam.”

But they also discovered that using the compression ratio by itself still resulted in false positives, where non-spam pages were incorrectly identified as spam:

“The compression ratio heuristic described in Section 4.6 fared best, correctly identifying 660 (27.9%) of the spam pages in our collection, while misidentifying 2, 068 (12.0%) of all judged pages.

Using all of the aforementioned features, the classification accuracy after the ten-fold cross validation process is encouraging:

95.4% of our judged pages were classified correctly, while 4.6% were classified incorrectly.

More specifically, for the spam class 1, 940 out of the 2, 364 pages, were classified correctly. For the non-spam class, 14, 440 out of the 14,804 pages were classified correctly. Consequently, 788 pages were classified incorrectly.”

The next section describes an interesting discovery about how to increase the accuracy of using on-page signals for identifying spam.

Insight Into Quality Rankings

The research paper examined multiple on-page signals, including compressibility. They discovered that each individual signal (classifier) was able to find some spam but that relying on any one signal on its own resulted in flagging non-spam pages for spam, which are commonly referred to as false positive.

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The researchers made an important discovery that everyone interested in SEO should know, which is that using multiple classifiers increased the accuracy of detecting spam and decreased the likelihood of false positives. Just as important, the compressibility signal only identifies one kind of spam but not the full range of spam.

The takeaway is that compressibility is a good way to identify one kind of spam but there are other kinds of spam that aren’t caught with this one signal. Other kinds of spam were not caught with the compressibility signal.

This is the part that every SEO and publisher should be aware of:

“In the previous section, we presented a number of heuristics for assaying spam web pages. That is, we measured several characteristics of web pages, and found ranges of those characteristics which correlated with a page being spam. Nevertheless, when used individually, no technique uncovers most of the spam in our data set without flagging many non-spam pages as spam.

For example, considering the compression ratio heuristic described in Section 4.6, one of our most promising methods, the average probability of spam for ratios of 4.2 and higher is 72%. But only about 1.5% of all pages fall in this range. This number is far below the 13.8% of spam pages that we identified in our data set.”

So, even though compressibility was one of the better signals for identifying spam, it still was unable to uncover the full range of spam within the dataset the researchers used to test the signals.

Combining Multiple Signals

The above results indicated that individual signals of low quality are less accurate. So they tested using multiple signals. What they discovered was that combining multiple on-page signals for detecting spam resulted in a better accuracy rate with less pages misclassified as spam.

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The researchers explained that they tested the use of multiple signals:

“One way of combining our heuristic methods is to view the spam detection problem as a classification problem. In this case, we want to create a classification model (or classifier) which, given a web page, will use the page’s features jointly in order to (correctly, we hope) classify it in one of two classes: spam and non-spam.”

These are their conclusions about using multiple signals:

“We have studied various aspects of content-based spam on the web using a real-world data set from the MSNSearch crawler. We have presented a number of heuristic methods for detecting content based spam. Some of our spam detection methods are more effective than others, however when used in isolation our methods may not identify all of the spam pages. For this reason, we combined our spam-detection methods to create a highly accurate C4.5 classifier. Our classifier can correctly identify 86.2% of all spam pages, while flagging very few legitimate pages as spam.”

Key Insight:

Misidentifying “very few legitimate pages as spam” was a significant breakthrough. The important insight that everyone involved with SEO should take away from this is that one signal by itself can result in false positives. Using multiple signals increases the accuracy.

What this means is that SEO tests of isolated ranking or quality signals will not yield reliable results that can be trusted for making strategy or business decisions.

Takeaways

We don’t know for certain if compressibility is used at the search engines but it’s an easy to use signal that combined with others could be used to catch simple kinds of spam like thousands of city name doorway pages with similar content. Yet even if the search engines don’t use this signal, it does show how easy it is to catch that kind of search engine manipulation and that it’s something search engines are well able to handle today.

Here are the key points of this article to keep in mind:

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  • Doorway pages with duplicate content is easy to catch because they compress at a higher ratio than normal web pages.
  • Groups of web pages with a compression ratio above 4.0 were predominantly spam.
  • Negative quality signals used by themselves to catch spam can lead to false positives.
  • In this particular test, they discovered that on-page negative quality signals only catch specific types of spam.
  • When used alone, the compressibility signal only catches redundancy-type spam, fails to detect other forms of spam, and leads to false positives.
  • Combing quality signals improves spam detection accuracy and reduces false positives.
  • Search engines today have a higher accuracy of spam detection with the use of AI like Spam Brain.

Read the research paper, which is linked from the Google Scholar page of Marc Najork:

Detecting spam web pages through content analysis

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New Google Trends SEO Documentation

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Google publishes new documentation for how to use Google Trends for search marketing

Google Search Central published new documentation on Google Trends, explaining how to use it for search marketing. This guide serves as an easy to understand introduction for newcomers and a helpful refresher for experienced search marketers and publishers.

The new guide has six sections:

  1. About Google Trends
  2. Tutorial on monitoring trends
  3. How to do keyword research with the tool
  4. How to prioritize content with Trends data
  5. How to use Google Trends for competitor research
  6. How to use Google Trends for analyzing brand awareness and sentiment

The section about monitoring trends advises there are two kinds of rising trends, general and specific trends, which can be useful for developing content to publish on a site.

Using the Explore tool, you can leave the search box empty and view the current rising trends worldwide or use a drop down menu to focus on trends in a specific country. Users can further filter rising trends by time periods, categories and the type of search. The results show rising trends by topic and by keywords.

To search for specific trends users just need to enter the specific queries and then filter them by country, time, categories and type of search.

The section called Content Calendar describes how to use Google Trends to understand which content topics to prioritize.

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Google explains:

“Google Trends can be helpful not only to get ideas on what to write, but also to prioritize when to publish it. To help you better prioritize which topics to focus on, try to find seasonal trends in the data. With that information, you can plan ahead to have high quality content available on your site a little before people are searching for it, so that when they do, your content is ready for them.”

Read the new Google Trends documentation:

Get started with Google Trends

Featured Image by Shutterstock/Luis Molinero

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