Within 1 year, the LinkedIn Collective Articles project reached the mark of 10 million pages of expert content. Since September 2023, their loyal audience has increased by more than 270%.
The way the project has achieved its current results, and will likely continue to grow rapidly in the future, allows us to learn valuable lessons for creating our own SEO strategy that uses AI in conjunction with the expertise of real people.
Why Collective Articles Work
The idea behind the Collaborative Articles project is that people turn to the Internet to understand certain topics, but what is on the Internet is not always useful information from real experts in a particular field .
Typically, a person searches for information on Google, ends up on a site like Pikabu or Reddit, and reads what is posted there. But in this case, there is no certainty that this information belongs to an expert in a certain field, and not just an ordinary chatterbox (or CPA webmaster).
How can someone who is not an expert on a topic know that a stranger's message is credible and expert?
The solution was to hire LinkedIn experts to write articles on topics in which they are experts. As a result, such pages rank in Google and this benefits the expert, which in turn motivates the expert to write more content.
Collective Articles: How LinkedIn Conquers Google's Organic Search Results with AI and Live Experts
How LinkedIn Created 10 Million Pages of Expert Content
LinkedIn identifies experts in a specific field and asks them to write an essay on a given topic. Essay topics are generated using an artificial intelligence tool developed by LinkedIn's editorial team. These topics are then matched with subject matter experts identified using the LinkedIn Skills Graph.
The LinkedIn Skills Graph matches LinkedIn members with subject matter experts using a framework called Structured Skills, which uses machine learning models and natural language processing to identify related skills beyond those identified by community members themselves.
The matching uses skills found in user profiles, job descriptions and other textual data on the platform. As a starting point.
Next, artificial intelligence, machine learning and natural language processing are used to provide additional knowledge and experience that users can have.
LinkedIn identifies experts in a specific field and asks them to write an essay on a given topic. Essay topics are generated using an artificial intelligence tool developed by LinkedIn's editorial team. These topics are then matched with subject matter experts identified using the LinkedIn Skills Graph.
The LinkedIn Skills Graph matches LinkedIn members with subject matter experts using a framework called Structured Skills, which uses machine learning models and natural language processing to identify related skills beyond those identified by community members themselves.
The matching uses skills found in user profiles, job descriptions and other textual data on the platform. As a starting point.
Next, artificial intelligence, machine learning and natural language processing are used to provide additional knowledge and experience that users can have.
The Skills Graph documentation explains it all like this:
If a community member knows about artificial neural networks, then he knows something about deep learning, which means he knows something about machine learning.
…Our machine learning and artificial intelligence comb through vast amounts of data and suggest new skills and connections between them.
…Combined with natural language processing, we extract skills from many different types of text—with a high degree of confidence—to ensure high coverage and high accuracy when matching skills to our users…
If a community member knows about artificial neural networks, then he knows something about deep learning, which means he knows something about machine learning.
…Our machine learning and artificial intelligence comb through vast amounts of data and suggest new skills and connections between them.
…Combined with natural language processing, we extract skills from many different types of text—with a high degree of confidence—to ensure high coverage and high accuracy when matching skills to our users…
Experience, expertise, credibility and credibility
The strategy behind LinkedIn’s “Collective Articles” project is genius because it results in millions of pages of high-quality content from subject matter experts on millions of topics. This is probably why LinkedIn pages are getting more and more organic impressions in Google search.
LinkedIn is currently improving its project, adding features that should further improve the quality of pages:
Explanation from LinkedIn :
Based on feedback from our users via custom scoring mechanisms, we have focused our efforts on the ability to match articles and expert users. One of the new methods we use is embedding-based search (EBR). This method generates embeddings for members and articles in the same semantic space and uses approximate nearest neighbor search in this space to generate the best article matches for community members.
The strategy behind LinkedIn’s “Collective Articles” project is genius because it results in millions of pages of high-quality content from subject matter experts on millions of topics. This is probably why LinkedIn pages are getting more and more organic impressions in Google search.
LinkedIn is currently improving its project, adding features that should further improve the quality of pages:
- Evolution of the way questions are asked : LinkedIn recently introduced scenarios for experts to write essays that address real-world topics and questions.
- "Useless Information" Button : Collective articles have a button that readers can use to let LinkedIn know that a particular addition from an expert is not useful. From an SEO perspective, it is very interesting that LinkedIn views the "Dismiss" button through the paradigm of usefulness.
- Improved topic matching algorithms : LinkedIn has improved its algorithm for matching users to topics, called "Embedding Based Retrieval For Improved Matching," based on user feedback about the quality of topic matching.
Explanation from LinkedIn :
Based on feedback from our users via custom scoring mechanisms, we have focused our efforts on the ability to match articles and expert users. One of the new methods we use is embedding-based search (EBR). This method generates embeddings for members and articles in the same semantic space and uses approximate nearest neighbor search in this space to generate the best article matches for community members.
Key SEO Takeaways
LinkedIn’s “Collective Articles” is one of the best strategic content creation projects I’ve seen in a long time. What makes it revolutionary is that it uses AI and machine learning technologies along with human expertise to create expert and useful content that readers love and trust.
LinkedIn uses user engagement signals to improve the quality of experts invited to create articles and to identify articles that don't meet user needs.
The benefits of creating articles this way is that high-quality subject matter experts are promoted every time their article appears in Google search results, giving anyone promoting a service, product, or looking for clients or their next job the opportunity to demonstrate their skills, experience, and authority .