SEO
Chatbots And AI Search Engines Converge: Key Strategies For SEO
A lot is happening in the world of search right now, and for many, keeping pace with these changes can be overwhelming.
The rise of chatbots and AI assistants – like ChatGPT and its new model GPT-4o, along with Google’s rollout of AI Overviews and Search Generative Experience (SGE) – is blurring the lines between chatbots and search engines.
New AI-first entrants, such as Perplexity and You.com, also fragment the search space.
While this causes some confusion and necessitates that marketers pivot and optimize for multiple types of “engines,” it also presents a whole new array of opportunities for SEO pros to optimize for both traditional and AI-driven search engines in a new multisearch universe.
This evolution raises a broader question – perhaps for another day – about redefining what we call SEO to encompass terms like Artificial Intelligence Optimization (AIO) and Generative Engine Optimization (GEO).
Currently, every naming convention seems subject to change, which is something to consider as I write this article.
Either way, this evolution opens up tremendous opportunities for disruption in the overall search landscape.
What Is A Chatbot Or AI Assistant?
At the most basic level, chatbots use natural language processing (NLP) and large language models (LLMs) that are trained to extract data from online information, sources, and specific datasets. They then classify and fine-tune text and visual outputs based on a user’s prompt or question.
Chatbots are often used within specific applications or platforms, such as customer service websites, messaging apps, or ecommerce sites. They are designed to address specific queries or tasks within these defined contexts.
Right now, we see many crossovers between LLM-based chatbots and search engines. Rapid developments in these areas can cause confusion.
In this article, we’ll focus on the development of AI models in chatbots and their relation to search, with an inferred reference between chatbots and AI assistants.
The Evolution Of Chatbots And AI Models
Since ChatGPT emerged in November 2022, we’ve seen a significant boom in chatbots and AI assistants. Now, generative AI allows users to interact directly with AI and engage in human-like conversations to ask questions and complete various tasks.
For example, these AI tools can assist with SEO tasks, create content, compose emails, write essays, and even handle coding and programming tasks.
As they evolve, chatbots become multimodal (MMLLMs), improving capabilities beyond text to include images, audio, and more.
For those interested in digging deeper into these models, the 2024 AI Index Report from Stanford University is a great resource for SEJ readers.
While many chatbots and AI models serve similar purposes, they also have distinct applications and use cases, such as content creation, image generation, and voice recognition.
Here are a few examples with some interesting differentiators and points:
- ChatGPT: Conversational AI for research, ideation, text, image content, and more.
- Google Gemini and Gemma: Uses Google’s LLM to connect and find sources within Google.
- Microsoft Bing: Uses ChatGPT for conversational web search in Bing.
- Anthropic Claude: Various AI models for content generation, images, and coding.
- Stability AI: Suite of models and AI assistants for text, image, audio, and coding.
- Meta Llama3: Utilizes Facebook’s social graph, its own Llama 3 model, and real-time data from Google.
- Microsoft’s Copilot: AI assistant for business creativity and productivity apps.
- Amazon LLM and Codewhisperer: Enhances customer and employer experiences.
- Perplexity AI: Provides quick answers, sources of information, and citations.
Perplexity AI (which I will touch on later in this article) acts more like a search engine than many other chatbots and AI assistants.
Beyond their primary use cases, many companies are making their models available to a wider audience and broader ecosystems, allowing users to customize their own AI assistants.
For example, Amazon’s Bedrock enables AWS customers to use Anthropic and other LLMs, including Amazon’s own model, to create custom AI agents. Companies like Lonely Planet, Coda, and United Airlines are already using it.
On May 13, OpenAI launched its new flagship model, GPT-4. This model is a combination of AI technologies, bringing together what OpenAI calls “text, vision, and audio.” It also opens up access to its application programming interface (API), allowing developers to build their own applications.
All of this convergence has a lot of people wondering.
What’s The Difference Between Chatbots And Search Engines?
The first thing to note is that both chatbots and search engines are designed to provide information.
Search engines and some chatbot models share many similarities, which means their definitions can blur, and the relationships between them converge and collide.
However, at the moment (but it is changing), there is still a distinct difference between the two:
Search Engines
- Search engines are better for exploring a wide range of topics.
- They provide diverse perspectives from multiple sources.
Chatbots
- Chatbots are better for quick answers, task completion, and personalized interactions.
- They enhance the efficiency of the average searcher, making them much more effective at finding information.
As more overlays and overlaps occur, the definitions of what constitutes a chatbot, an AI assistant, and a search engine may need to be redefined.
How Chatbots And Search Engines Work Together
Conversational search is a key area where search engines increasingly integrate chatbot features to provide a more interactive search experience.
You can ask questions in natural language, and the search engine may respond with direct answers or engage in a dialogue to refine your query.
Chatbots and AI assistants often utilize search engine technology to access information from the web, enhancing their ability to provide accurate and comprehensive answers.
This integration allows chatbots to go beyond their programmed knowledge base and tap into a broader range of information.
Here are a few examples:
- Google: Integrates its own chatbot features into its search engine through SGE, providing direct answers and engaging in conversational search for some queries.
- Bing: Incorporates a chatbot called “Bing Chat” that uses ChatGPT, conversational AI, and search technology to answer questions and provide information.
- YouChat: A search engine that provides conversational responses to queries and allows for follow-up questions.
- Meta: Utilizes its social graph and Google’s real-time data in its chatbot/AI assistant.
- Perplexity AI: A chatbot that functions like a search engine, focusing on informational sources, sites, and citations.
These examples illustrate how the lines between chatbots and search engines are blurring. Thousands more instances show this convergence, highlighting the evolving landscape of digital search and AI.
How “Traditional” Search Engines Are Evolving As AI-First Entrants Arrive
The rise of generative AI and chatbots has caused significant upheaval in the traditional search space.
Traditional search engines are evolving into “answer engines.” This transformation is driven by the need to provide users with direct, conversational responses rather than just a list of links.
The line between chatbot engines and AI-led search engines is becoming increasingly blurred.
While AI in search is not a new concept, the introduction of generative AI and chatbots has necessitated a seismic shift in how search engines operate. For the first time, users can interact with AI in a conversational way, prompting giants like Google and Microsoft to adapt.
On May 14 at Google IO, Google announced the roll-out of AI Overviews as it integrates AI features into its search engine. It is also making upgrades to SGE.
The ultimate goal is to enhance its ability to provide direct answers and engage in conversational search. This evolution signifies Google’s commitment to maintaining its leadership in the search space by leveraging AI to meet user expectations.
In a recent interview on Wired Magazine titled It’s the End of Google Search As We Know It, Google Head of Search, Liz Reid, was clear that:
“AI Overviews like this won’t show up for every search result, even if the feature is now becoming more prevalent.”
As my co-founder, Jim Yu, states in the same article:
“The paradigm of search for the last 20 years has been that the search engine pulls a lot of information and gives you the links. Now the search engine does all the searches for you and summarizes the results and gives you a formative opinion.”
Beyond Google, we are seeing a rise in new, AI-driven search engines like Perplexity, You.com, and Brave, which act more like traditional search engines by providing informational sources, sites, and citations.
These platforms leverage generative AI to deliver comprehensive answers and facilitate follow-up questions, challenging the dominance of established players.
Meta is also entering the fray by utilizing its social graph and real-time data from Google in its AI assistant, further contributing to the convergence of search and AI technologies.
At the same time, according to Digiday, TikTok is starting to reward what it calls “search value.”
Going forward, it’s important to remember that people have diverse needs, and we turn to different platforms for specific purposes.
Just as we go to Amazon for products, Yelp for restaurant suggestions, and YouTube for videos, the rise of AI will only amplify this trend. Each search engine will find its niche, leveraging its strengths to cater to particular user requirements.
ChatGPT is an intriguing case that stands out not for its research capabilities but for its prowess in content creation. While it excels in crafting high-quality content, its research functionalities fall short.
Effective research relies on real-time data, which platforms like ChatGPT currently lack. As we move forward, we expect to see search engines specialize even further, each excelling in specific areas based on its unique strengths and features.
What Does It All Mean For Marketers?
This fast-moving landscape and the convergence of search and AI presents both challenges and opportunities for marketers.
Optimizing for one engine is no longer sufficient; it’s essential to target multiple platforms – each with unique users, demographics, and intents.
Here’s how marketers can adapt and thrive in this dynamic environment.
Optimizing For Different Platforms
- Strength: Dominates the traditional search space with a vast user base and comprehensive data sources.
- Tip: Focus on core technical SEO, including schema markup and mobile optimization. Google’s Search Generative Experience means direct answers are becoming more prevalent, so structured data and high-quality content are vital.
Perplexity AI
- Strength: Provides detailed citations and emphasizes source material, driving referral traffic back to original sites.
- Tip: Ensure your content is authoritative and well-cited. Being a reliable source will increase the likelihood of your site being referenced, which can drive traffic and enhance brand trust.
ChatGPT
- Strength: Excels in conversational AI, making it suitable for quick answers and personalized interactions.
- Tip: Create engaging, concise content that answers common questions directly. Utilize conversational language in your SEO strategy to match the style of ChatGPT interactions.
Key Strategies For Marketers
From optimizing technical SEO to harnessing the power of semantic understanding and creativity, these strategies provide a roadmap for success in the era of AI-driven search.
Core Technical SEO
Basics like site speed, mobile-friendliness, and proper schema markup remain crucial. Ensuring your site is technically sound helps all search engines index and rank your content effectively.
Semantic Understanding
Search engines and conversational AI are increasingly focused on semantic search. Optimize for natural language queries and long-tail keywords to match user intent more accurately.
Content And Creativity
High-quality, creative content is more important than ever. Unique, valuable content that engages users will stand out in both traditional and AI-driven search results.
Expanded Role Of SEO
SEO now encompasses content creation, branding, public relations, and AIO. Marketers who can adapt to these roles will be more successful in the evolving search landscape.
Be The Source That Gets Cited
Ensure your content is authoritative and well-researched. Being a primary source will increase the likelihood of citations that drive traffic and enhance credibility.
Get Predictive
Anticipate follow-up questions and provide comprehensive answers. This will not only improve user experience but also increase the chances of your content being highlighted in AI-driven search results.
Brand Authority
Focus on areas where your brand excels. AI search engines prioritize authoritative sources, so build and maintain your reputation in key areas to stay competitive.
The Best Content That Provides The Best Experience Wins
Ultimately, the quality of your content will determine your success. Invest in creating the best possible user experience, from engaging visuals to informative text.
Key Takeaways
Today, search encompasses a dual purpose: It can serve as a standalone assistant-based application or integrate into search engines for AI-led conversational experiences.
This fusion presents marketers with a unique opportunity to elevate their brands by creating accurate and authoritative content that positions them as trusted sources in their respective fields.
Ranking on the first page and being recognized as the go-to source cited by AI engines is no less important than 10 or 20 years ago but is exponentially more difficult.
The good news is that whether it’s Google’s AI engine or newcomers like Perplexity, brands that establish themselves as authorities in their niche stand to benefit immensely.
Marketers need to embrace creativity and collaboration across omnichannel teams. Ensure that your website is visible and accessible to all types of engines, whether traditional or AI-driven.
I’d like to leave you with a few questions to consider as you find your way forward in this complex environment. Pardon the pun, but no one has all the right answers yet.
- Are chatbots morphing into search engines?
- How do social platforms differentiate as younger generations look to them as search engines?
- How would you define a search engine?
- Who will win the race for user loyalty – traditional search engines infused with AI or new entrants built on generative AI from the beginning?
- How would you redefine your role as an SEO – are you AI first?
While you consider that, stay proactive and adaptable and position yourself and your company to leverage the diversity and complexity of the search ecosystem to your advantage. In a world of ChatGPT, chatbots, and AI in search, you’re not optimizing for one channel, such as Google or Bing.
Successful optimization in this multifaceted landscape calls for a holistic approach. It’s not about keyword rankings or click-through rates; it’s about unraveling the intricacies of each platform and adjusting your strategies accordingly.
This means optimizing your content for conversational search, tapping into the capabilities of AI to tailor user experiences, and seamlessly integrating across different channels and devices.
Leverage the strengths of each platform to amplify your message by use case and engage with your audience on a deeper level, and you’ll ultimately drive more meaningful results for your business.
More resources:
Featured Image: Memory Stockphoto/Shutterstock
SEO
How to Revive an Old Blog Article 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.
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.
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.
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:
- Reflect the rewritten and new content you’ve added to the blog post.
- Be optimized for the new keywords it’s targeting (if any).
- 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.
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!
SEO
How Compression Can Be Used To Detect Low Quality Pages
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.
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:
“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:
“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.
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.
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:
- 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
SEO
New Google Trends SEO Documentation
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:
- About Google Trends
- Tutorial on monitoring trends
- How to do keyword research with the tool
- How to prioritize content with Trends data
- How to use Google Trends for competitor research
- 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.
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
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