LinkedIn’s Collaborative Articles features reached the milestone of 10 million pages of expert content in one year. The Collaborative Articles project has experienced a significant rise in weekly readership, rising by over 270% since September 2023.  How they reached these milestones and are planning to achieve even more results offer valuable lessons for creating an SEO strategy that uses AI together with human expertise.

Why Collaborative Articles Works

The intuition underlying the Collaborative Articles project is that people turn to the Internet to understand subject matter topics but what’s on the Internet is not always the best information from actual subject matter experts.

A person typically searches on Google and maybe lands on a site like Reddit and reads what’s posted but there’s no assurance that the information is by a subject matter expert or just the person with the biggest social media mouth. How does someone who is not a subject matter expert know that a post by a stranger is trustworthy and expert?

The solution to the problem was to leverage LinkedIn’s experts to create articles on topics they are expert in. The pages rank in Google and this turns into a benefit for the subject matter expert, which in turn motivates the subject matter expert to write more content.

How LinkedIn Engineered 10 Million Pages Of Expert Content

LinkedIn identifies subject matter experts and contacts them to write an essay on the topic. The essay topics are generated by an AI “conversation starter” tool developed by a LinkedIn editorial team. Those conversation topics are then matched to subject matter experts identified by LinkedIn’s Skills Graph.

The LinkedIn Skills Graph maps LinkedIn members to subject matter expertise through a framework called Structured Skills which uses machine learning models and natural language processing to identify related skills beyond what the members themselves identify.

The mapping uses skills found in members’ profiles, job descriptions, and other text data on the platform as a starting point from which they use AI, machine learning and natural language processing to expand on additional subject matter expertise the members may have.

The Skills Graph documentation explains:

“If a member knows about Artificial Neural Networks, the member knows something about Deep Learning, which means the member knows something about Machine Learning.

…our machine learning and artificial intelligence combs through massive amounts of data and suggests new skills and relations between them.

…Combined with natural language processing, we extract skills from many different types of text – with a high degree of confidence – to make sure we have high coverage and high precision when we map skills to our members…”

Experience, Expertise, Authoritativeness and Trustworthiness

The underlying strategy of LinkedIn’s Collaborative Articles project is genius because it results in millions of pages of high quality content by subject matter experts on millions of topics. That may be why LinkedIn’s pages have become more and more visible in Google search.

LinkedIn is now improving their Collaborative Articles project with features that are meant to improve the quality of the pages even more.

• Evolved how questions are asked:
  LinkedIn is now presenting scenarios to subject matter experts that they can respond to with essays that address real-world topics and questions.
• New unhelpful button:
  There is now a button that readers can use to offer feedback to LinkedIn that a particular essay is not helpful. It’s super interesting from an SEO viewpoint that LinkedIn is framing the thumbs down button through the paradigm of helpfulness.
• Improved Topic Matching Algorithms
  LinkedIn has improved how they match users to topics with what they refer to as “Embedding Based Retrieval For Improved Matching” which was created to address feedback from members about the quality of the topic to member matching.

LinkedIn explains:

“Based on feedback from our members through our evaluation mechanisms, we focused our efforts on our matching capabilities between articles and member experts. One of the new methods we use is embedding-based retrieval (EBR). This method generates embeddings for both members and articles in the same semantic space and uses an approximate nearest neighbor search in that space to generate the best article matches for contributors.”

Top Takeaways For SEO

LinkedIn’s Collaborative Articles project is one of the best strategized content creation projects to come along in a long while. What makes it not just genius but revolutionary is that it uses AI and machine learning technology together with human expertise to create expert and helpful content that readers enjoy and can trust.

LinkedIn is now using user interaction signals to improve the quality of the subject matter experts that are invited to create articles as well as to identify articles that do not meet the needs of users.

The benefits of creating articles is that the high quality subject matter experts are promoted every time their article ranks in Google, which offers anyone who is promoting a service, a product or looking for clients or the next job an opportunity to demonstrate their skills, expertise and authoritativeness.

Read LinkedIn’s announcement of the one-year anniversary of the project:

Unlocking nearly 10 billion years worth of knowledge to help you tackle everyday work problems

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