LinkedIn is changing its algorithm in ways that should benefit people who post specific information about topics in which they have clear expertise.

The changes — first cited in an article by Jason Feifer, editor in chief of Entrepreneur magazine, come in response to complaints that too many posts focus on personal information that doesn’t provide insights to “get better at what I’m doing.”

The main changes to the algorithm include:

–Followers will see more of your posts.
–Non-followers will see more posts sharing “knowledge and advice.”

To meet that criteria, each post will be analyzed to see whether the item:

a) Speaks to a distinct audience.
b) Comes from an expert
c) Attracts “meaningful comments”
d) Has a perspective

Taken together the items are trying to encourage posts that make a specific point, are written by experts and generate engagement in the form of thoughtful comments.

The algorithm will also consider the expertise of the commenters. For example, in a post about marketing, long detailed comments from marketing professionals will count more.

The idea is to discourage posts that offer generic information and reward posts that come from a writer’s perspective and insight.

LinkedIn’s editor in chief Dan Roth and Alice Xiong, who leads search and discovery products at the firm, told Feifer the firm is using AI to help classify posts into categories.

Roth said that they’ve been encouraged by early results, such as an 80% reduction in people complaining about irrelevant content. LinkedIn has also seen a 10% increase in people viewing posts from people they follow.

The company has good reason to invest. LinkedIn said it recorded a 42% year-over-year increase in content shared since 2021 and a 27% increase in readership.

These are incredible numbers. It’s also worth remembering that LinkedIn is unique among social media in that it didn’t focus on ramping up user-generated content until 15 years after it was created.

Having built content algorithms in a previous job, I know that It’s always hard to know how tweaks in an algorithm will play out or predict unintended consequences.

The human is often the enemy as people try to trick algorithms. Roth cited an example: the previous algo rewarded the number, not quality of comments, so some people would form “engagement groups” to quickly comment on each other’s posts.

Changes that prioritize depth and expertise over clicks seem welcome.

Clicks are incredibly seductive, but any system that focuses on generic viral posts inevitably drives down the quality of content and undermines its product.

This change is a sign that LinkedIn is playing the long game.