How the LinkedIn Algorithm Works in 2026: A Technical Breakdown

The Three Stages of LinkedIn Distribution

Every piece of content on LinkedIn passes through three algorithmic stages before reaching its final audience size. Understanding these stages reveals why comment strategy is so effective.

Stage 1: Quality Classification

Within seconds of posting, LinkedIn's AI classifies your content into one of three categories: spam, low quality, or high quality. This classification is based on text analysis, image quality, link patterns, and your account history. Content flagged as spam is suppressed immediately. Low quality content receives minimal distribution. High quality content advances to Stage 2.

Factors that trigger high-quality classification include: original text (not copied), professional language, relevant hashtags (3 to 5 maximum), and a clean posting history without spam flags. Factors that trigger low-quality classification include: excessive links in the post body, engagement bait language ("like if you agree"), and posting frequency above 3 times per day.

Stage 2: Test Distribution

Content classified as high quality is shown to approximately 5 to 10% of your first-degree connections. LinkedIn monitors engagement during this test window, which typically lasts 60 to 90 minutes. The engagement signals it monitors are weighted differently:

The key insight: a single substantive comment that generates a reply is worth 20 times more than a like. This is why comment-first strategies are so powerful. Your comment does not just benefit your own visibility; it actively improves the distribution of the original post, which is why creators welcome high-quality comments.

Stage 3: Expanded Distribution

If the test distribution generates strong engagement signals, LinkedIn expands the audience progressively. First to second-degree connections (people connected to people who engaged), then to topic-interested users (people who follow relevant hashtags or have engaged with similar content), and finally to broader audiences if the content continues performing.

Each expansion cycle creates new test groups, and the engagement threshold increases at each level. Content that generates comments from second-degree connections receives particularly strong expansion signals because it demonstrates cross-network value.

How LinkedIn Ranks Comments

Comments on a LinkedIn post are not displayed chronologically. They are ranked by the algorithm based on several factors, which means the quality of your comment determines its visibility independently of when you posted it.

Comment Ranking Factors

1. Commenter's SSI score. Comments from users with high Social Selling Index scores receive a ranking boost. This creates a virtuous cycle: better comment visibility leads to more engagement, which improves your SSI, which improves future comment visibility.
2. Comment length and substance. Comments between 100 and 300 characters receive the optimal algorithmic treatment. Below 50 characters is considered low-value. Above 500 characters may be truncated and lose readability.
3. Engagement on the comment. Comments that receive likes and replies from other users are ranked higher. A comment that sparks a sub-conversation becomes highly visible.
4. Relationship to poster. Comments from first-degree connections of the poster receive a modest ranking boost. Comments from people who have previously engaged with the poster receive a larger boost.
5. Timing. Earlier comments have a slight advantage, but this is less significant on LinkedIn than on X. A high-quality comment posted 2 hours after the original post can still rank above a low-quality comment posted in the first minute.

For comment-first practitioners, the implication is clear: invest in your SSI score, write comments of 100 to 300 characters, and focus on sparking sub-conversations. For SSI optimisation tactics, see LinkedIn SSI Score Hacking.

The Dwell Time Revolution

Dwell time, the duration a user spends viewing content before scrolling, has become one of LinkedIn's most important ranking signals. This shift reflects a broader industry trend toward measuring attention quality rather than attention quantity.

Why dwell time matters: a user who spends 20 seconds reading a post and its comments is more engaged than a user who double-taps a like in half a second. LinkedIn uses dwell time to distinguish between content that genuinely holds professional interest and content that generates reflexive engagement (likes) without real attention.

Implications for Comment Strategy

Your comments increase dwell time for other users, which benefits the original post's distribution. A thoughtful comment that makes readers pause and think generates dwell time for the entire thread. This is why original posters actively welcome substantive comments: your comment makes their post perform better.

Comments that include specific data, frameworks, or counterpoints generate the most dwell time because they require readers to process new information. Generic agreement comments ("So true!") generate almost zero additional dwell time.

What the Algorithm Penalises

Understanding what the algorithm penalises is as important as understanding what it rewards.

• Engagement bait. Posts that explicitly ask for likes, comments, or shares ("Like if you agree, comment if you disagree") receive distribution penalties. Similarly, comments that are clearly prompted by engagement bait carry less weight.
• Coordinated engagement. LinkedIn detects engagement pod patterns: multiple accounts liking and commenting on each other's content in rapid, predictable sequences. Pod-driven engagement carries reduced algorithmic weight and can trigger manual review.
• Irrelevant commenting. Commenting on posts outside your professional domain with generic messages looks like spam to the algorithm. Stay in your lane and comment where your expertise is relevant.
• Excessive posting. More than 2 to 3 posts per day triggers a distribution penalty on all posts. This penalty does not apply to comments. You can comment 15 times per day without any algorithmic penalty.
• External links in comments. Comments containing external URLs receive reduced visibility. LinkedIn prefers keeping users on-platform. If you need to share a link, put it in a follow-up reply to your own comment, or share it via DM.

Practical Application for Reply Strategy

Knowing how the algorithm works changes how you approach every comment. Here is how to apply these mechanics to your daily workflow:

Reply Engine applies these checks automatically, generating comment suggestions that are pre-optimised for LinkedIn's algorithmic preferences. For the complete comment-first strategy, see The Comment-First LinkedIn Strategy.

Frequently Asked Questions

Summary