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Can You Spot Google Updates with XmR Charts?

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Can You Spot Google Updates with XmR Charts?

Website traffic data normally looks like this:

Ups and downs, peaks and troughs.

If we’re doing our job properly, we generally expect traffic to trend upwards over time, but in any given month, it’s difficult to say whether a peak or a trough is worth paying attention to.

Did we do something great and trigger a new phase of growth? Did we benefit from a new Google update? Or is it just normal variation, part of the natural ebb and flow of people finding our website?

Or suppose you make a change to your content process—you pruned and redirected a bunch of old content—and then traffic dropped the next month. Was that drop caused by the change, or was it just a coincidence?

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I’ve been experimenting with a simple statistical tool designed to help answer these questions: XmR charts, also known as process control charts.

Here’s an XmR chart:

Can You Spot Google Updates with XmR ChartsCan You Spot Google Updates with XmR Charts

XmR charts are designed to tell you whether any single data point from a time series is likely to be caused by normal fluctuation (“routine variation”) or a sign that something happened and needs to be investigated (“exceptional variation”).

XmR charts consist of an X plot (named after the x-value, the “thing” we care about—like widgets produced or sales closed)…

1721658366 562 Can You Spot Google Updates with XmR Charts1721658366 562 Can You Spot Google Updates with XmR Charts

…and an MR plot (named after the moving range, basically the “gap” between each data point):

1721658366 191 Can You Spot Google Updates with XmR Charts1721658366 191 Can You Spot Google Updates with XmR Charts

In its simplest use, if you plot your data on the chart and it wiggles up and down around the central line, without crossing the upper and lower bounds—no problem! These ups and downs likely represent normal variation.

But any points that appear outside the upper or lower bounds (shown in red) should be treated as anomalies that need to be investigated.

In the X plot above, the time series seems to show routine variation until January 16th, when the first red out-of-bounds point appears.

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1721658366 804 Can You Spot Google Updates with XmR Charts1721658366 804 Can You Spot Google Updates with XmR Charts

The XmR chart suggests that something happened on the 16th to mess with our production process (for better or for worse). Our job is to investigate why.

Sidenote.

The line in the middle is the average value of the dataset; the upper and lower bounds represent 3-standard deviations away from the average (known as three-sigma). Any point that falls outside of these upper and lower bounds is very likely to be an anomaly, and not part of the original probability distribution.

There are other “signals” that the XmR chart can show you (like eight consecutive points on one side of the average line representing another type of exceptional variation)—but I will leave you to investigate those on your own time.

When I started reading about XmR charts, one obvious use came to mind: identifying the impact of Google algorithm updates.

If a site’s traffic tanks to zero, it’s easy to say “we were hit by a manual penalty.” But for smaller changes, like a few months’ consecutive traffic decline, it’s harder to work out the cause. Did we get caught out by a Google update? Is it seasonality? Or is it just a coincidence, with traffic likely to return to normal in the future?

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Here’s two years of monthly organic traffic data for the Ahrefs blog, pulled from Site Explorer and plotted on an XmR chart:

1721658366 298 Can You Spot Google Updates with XmR Charts1721658366 298 Can You Spot Google Updates with XmR Charts

Now… this is not particularly useful.

There are tons of data points outside the expected range (red), with very few sitting nearer the center line than the quartile limits (orange).

The XmR chart is supposed to show exceptional variation in a consistent process—but in this image, almost all of the data points suggest exceptional variation. What gives?

Process charts were designed around simple manufacturing processes, and they work very well when the expected output of a process is constant.

If your goal is manufacturing 10,000 widgets each and every week, an XmR chart will help you work out if that 5,600-widget month was a normal “blip” in routine operation, or caused by a real problem that needs to be investigated.

Website traffic is more complicated. There are tons of variables that impact traffic:

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  • the fluctuating search volume of each topic,
  • individual ranking positions,
  • new competing articles,
  • search features,
  • seasonality,
  • publishing frequency,
  • Google algorithm updates

That means that running an XmR analysis on a long series of traffic data probably won’t be very helpful. Your “blogging process” is not likely to remain stable for very long.

In my case, this particular two-year snapshot of data probably doesn’t come from a single, stable process—there may be multiple probability distributions hidden in there.

But we can make the analysis more useful.

The best practice for XmR charts is to limit the analysis to a period of time when you know the process was relatively static, and recalculate it when you suspect something has changed.

Looking at the Moving Range chart for this data below, large amounts of traffic variance happened in November and December. We should investigate possible causes. 1721658366 887 Can You Spot Google Updates with XmR Charts1721658366 887 Can You Spot Google Updates with XmR Charts

I know that our publishing frequency was fairly static (we definitely didn’t double our content output). Seasonality would cause a traffic drop, not a spike (we’re writing about SEO, not holiday gift guides).

But there was a big Google update at the start of December:

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1721658366 313 Can You Spot Google Updates with XmR Charts1721658366 313 Can You Spot Google Updates with XmR Charts
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If we work on the assumption that something happened to our blog process around this time—likely a change to traffic caused by the Google update—we can add a divider to our XmR chart.

Instead of trying to analyze our traffic as a single process, we can treat it as two processes, and calculate XmR charts separately:

1721658366 499 Can You Spot Google Updates with XmR Charts1721658366 499 Can You Spot Google Updates with XmR Charts

Now the first process looks stable (all black dots). The second process shows less extreme variation (red) too, but there’s still too much moderate variation (orange) to look stable. There may be another process lurking within.

And per a rule of thumb for analyzing XmR charts: “the duration of an XmR chart needs to be revisited when a ‘long-run’ of data remains above or below the Average line.” This trend begins in late summer (which is also around the time that Google announced another core update):

1721658366 566 Can You Spot Google Updates with XmR Charts1721658366 566 Can You Spot Google Updates with XmR Charts

We can add another divider at the start of this “long-run” of data to create three separate XmR analyses:

1721658366 941 Can You Spot Google Updates with XmR Charts1721658366 941 Can You Spot Google Updates with XmR Charts

In doing so, all three analyses seem stable, with no points of extreme variance. In other words, we seem to have done a good job at capturing three distinct processes happening within our traffic data.

From this analysis, there seems to be a good chance that our traffic was impacted by external factors around the time of two major Google updates.

Now… this is basically a post-hoc data torturing exercise. We can’t infer any causation from this analysis, and it’s entirely possible that other arbitrary divisions would yield similar results.

But that’s okay. These charts can’t give you definitive, concrete reasons why your traffic changed, but they can tell you where to look, and help you work out whether troubleshooting a traffic dip or spike is a good use of your time.

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The ultimate measure of a model’s usefulness is its ability to help you predict things. Will XmR charts help me do a better job running the Ahrefs blog in the future?

I think yes.

Assuming my “blog process” remains relatively stable—I publish at the same frequency, target the same topics, compete with the same competitors—I now have a set of “stable” data that I can use to provide extra context for future traffic numbers:

1721658366 754 Can You Spot Google Updates with XmR Charts1721658366 754 Can You Spot Google Updates with XmR Charts

In the months that follow, I can work out whether dips or spikes in our traffic are likely the result of normal variance, or whether something has changed that requires my attention—like a Google update.

If, for example, my traffic does this next month… 1721658366 36 Can You Spot Google Updates with XmR Charts1721658366 36 Can You Spot Google Updates with XmR Charts

…I know that—given this distribution—that traffic drop could well be normal, unexciting variance.

But if it does this…

1721658367 676 Can You Spot Google Updates with XmR Charts1721658367 676 Can You Spot Google Updates with XmR Charts

…there’s probably something else at work.

With extreme traffic changes you can usually “eyeball” traffic charts and guess what happened. But XmR charts are useful for more subtle variations, and there’s a chance I will be able to identify and act on just a single month’s worth of data. That’s pretty cool.

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Final thoughts

Troubleshooting traffic changes is a big challenge for SEOs and content marketers (and we’re working on a few ways to help you identify the signal amongst the noise of your traffic data).

In the meantime, I have found XmR charts an interesting tool in my toolkit, useful for contextualizing my monthly reporting numbers and justifying when I should (or shouldn’t) spend my energy troubleshooting a down month.

(At the very least, XmR charts might just give you the confidence necessary to say “get off my back” when that VP sends you a brusque 3AM email complaining about last month’s 8% traffic dip.)

Sidenote.

Thanks to Benyamin Elias, VP of Marketing at Podia, for introducing me to XmR charts.

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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

Featured Image by Shutterstock/pathdoc

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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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