GLOSSARY · LINKEDIN MECHANICS
LinkedIn algorithm
The LinkedIn algorithm is the shorthand for the ranking systems that decide which posts appear in each member's feed and in what order. LinkedIn has described some of its approach publicly in engineering posts and product announcements: the feed scores content on predicted relevance to the member, uses signals such as reactions, comments, shares and dwell time, considers the relationship between the viewer and the author, and in recent years has said it prioritises content with knowledge and advice over viral or engagement-bait posts. The detailed weights are not published and change often, so most precise claims about them are guesses.
How it works in practice
What LinkedIn has said in public is enough to write sensibly without chasing tricks. The feed is personalised: it tries to show each member the posts they are most likely to find valuable, judged by what they and similar members have done before. Early signals matter because the system tests a post with part of the audience before deciding how widely to show it; comments and meaningful time spent are stronger signals than a passing like. The relationship between author and viewer matters, so people who have interacted with you before are more likely to see your next post. Since 2023 LinkedIn has stated that it is tuning the feed toward content that shares knowledge and expertise, that follows the author's known subjects, and that is more likely to be shown to people in the author's field.
It has also said it reduces distribution for engagement bait and for coordinated inauthentic activity. Everything else circulating as algorithm advice, such as exact best posting hours, penalties for external links, or ideal character counts, is at best inferred from someone's own data and at worst invented. The durable strategy is the one the public guidance points to: write things your audience learns from, stay on your subjects, reply to comments, and avoid gaming.
Example
A founder stops worrying about posting hour and link penalties, and instead keeps to three pillars, replies to every comment for an hour after posting, and watches who is reached. Over a quarter, the viewer breakdown shifts toward her target titles.
Common mistake
Rebuilding the content plan every time a post claims to have cracked the algorithm. The claims are unverifiable, the feed changes anyway, and the churn replaces the consistency that the public guidance actually rewards.