LinkedIn Algorithm : How It Really Works

LinkedIn's algorithm underwent a major transformation in 2025, shifting from a logic based on social networks (who you know) to a logic based on interests (what interests you). This evolution explains why impressions declined 63-66% since 2023, while engagement per post increased 12-39%. Understanding how the algorithm works is essential for optimizing your content strategy.

The Major 2025 Change: Social Graph → Interest Graph

LinkedIn's 2025 algorithm represents a fundamental shift from its previous logic. The platform transitioned from optimizing for a "Social Graph" (who you know) to prioritizing an "Interest Graph" (what interests you). This change explains why impressions declined while engagement increased: the algorithm now reads your content, understands its meaning, and matches it to users interested in that topic—regardless of whether you're connected. [1] This transition means content quality and relevance now trump network size. A post on a niche topic can reach thousands of interested users, even if you only have 500 connections, while a generic post can remain limited to your immediate network, even with 10,000 connections.

The Three Major 2025 Changes

LinkedIn's 2025 algorithm introduced three major changes that fundamentally transform content strategy.

1. Profile Now Influences Post Rank

Your LinkedIn headline, About section, and experience directly influence your posts' distribution. A vague or misaligned profile reduces your content's reach, even if the content itself is excellent. [2] Examples:

• Vague profile: "Marketing Professional" → Reduced performance
• Optimized profile: "B2B SaaS Marketing | SEO Specialist | Helping Startups Scale to 7 Figures" → Better distribution

This optimization creates a feedback loop: better-optimized profiles generate better-performing posts, which attract followers with more targeted interests.

2. Dwell Time Significantly Outweighs Likes

According to Prominence Global, Posts with longer dwell times achieve dramatically better distribution and engagement. Research shows posts with 61+ seconds dwell time achieve 15.6% engagement rates, compared to just 1.2% for posts with 0-3 seconds dwell time. This fundamentally changes content strategy: hooking attention in the first line is now more critical than generating likes. [3] Dwell time engagement correlation:

• 0-3 seconds: 1.2% engagement rate → Limited distribution
• 11-30 seconds: Extended distribution, moderate engagement
• 61+ seconds: 15.6% engagement rate → Maximum distribution

3. Hashtags Are Strategic, Not Mandatory

According to LinkedIn, Unlike Instagram, hashtag abuse no longer helps. Posts using 1-3 relevant hashtags achieve 12.6% more engagement compared to hashtag-free posts, but using more than 5 hashtags creates diminishing returns. Niche hashtags (e.g., #Cybersecurity) achieve approximately 3x higher engagement than generic ones (#Business). [4] [5]

How Does the 4-Stage Distribution Process Work?

LinkedIn content distribution follows a predictable four-stage model. Understanding each stage allows you to optimize your strategy.

Stage 1: Quality Filtering

Posts are immediately classified as spam, low-quality, or high-quality. The classification analyzes text, formatting, links, hashtags, and posting patterns. Engagement bait (e.g., "Comment YES if you agree") is flagged for downranking. [6] Filtering criteria:

• Text and formatting quality
• Presence of external links
• Hashtag usage
• Posting patterns (frequency, timing)
• Engagement bait detection

Stage 2: Initial Audience Test

According to LinkedIn Engineering, Quality posts enter a 60-120 minute window where they're shown to a small sample of your network: your most engaged connections, people who recently engaged with similar content, and a random sample of followers. This window is critical—getting strong engagement here signals the algorithm to proceed to stage 3. [7] Test audience:

• Most engaged connections (interaction history)
• Users who engaged with similar content recently
• Random sample of followers
• Window: 60-120 minutes after publication

Stage 3: Engagement Scoring

LinkedIn measures early engagement signals with weighted importance: Engagement signal hierarchy:

• Comments (especially long and thoughtful ones): Most valuable signal, 15x heavier than likes [8]
• Shares: Strong signal indicating content deserves wider distribution [9]
• Saves: Signal of lasting value [10]
• Dwell time: Extended reading indicates quality, significantly outweighs likes (15.6% engagement at 61+ sec vs 1.2% at 0-3 sec) [11]
• Reactions: Helpful but less weighted than comments [12]

According to LinkedIn, Comments have 15x the algorithmic weight of likes. Posts that generate comment threads (back-and-forth conversations) trigger aggressive reach expansion. [8]

Stage 4: Extended Distribution

Posts scoring well in stage 3 break beyond immediate networks, reaching 2nd and 3rd-degree connections, hashtag followers, and topical interest groups. Strong posts can stay in distribution for days or weeks if engagement velocity remains high. [13] Extended distribution includes:

• 2nd-degree connections (your network's network)
• 3rd-degree connections (your network's network's network)
• Hashtag followers
• Thematic interest groups (based on Interest Graph)
• Prolonged distribution if sustained engagement

What Is the Role of Dwell Time vs Likes?

Dwell time—the duration users spend reading or interacting with your post—is the primary measure of content quality on LinkedIn in 2025. Posts with longer dwell times achieve dramatically better engagement rates and distribution. [3] According to Prominence Global, This shift emerged because LinkedIn aims to maximize time spent on platform, and high-dwell-time content achieves this. Posts with minimal dwell time (0-3 seconds) achieve only 1.2% engagement, while posts with 61+ seconds dwell time achieve 15.6% engagement—a 13x difference. [14]

Dwell Time Benchmarks

Research reveals clear engagement rate correlation with dwell time:

• 0-3 seconds: 1.2% engagement rate, limited distribution
• 11-30 seconds: Extended distribution, moderate engagement
• 31-60 seconds: Maximum distribution, high engagement
• 61+ seconds: 15.6% engagement rate, exceptional distribution

This represents a 13x difference in engagement between minimal and exceptional dwell times, demonstrating the critical importance of creating content that holds attention. [15]

Optimizing for Dwell Time

Practical tactics to increase dwell time: According to Buffer, 1. Hook Engineering: Your first line determines whether users expand the post or skip it. Curiosity-inducing openings ("Here's what nobody's saying about LinkedIn's algorithm...") outperform generic ones ("Check out this post..."). Questions in the first 5 seconds boost comments 32%. [16] According to ContentIn, 2. Formatting and White Space: Short paragraphs (2-3 lines), strategic emoji usage, and visual breaks reduce cognitive load and prevent abandonment. Studies show formatted content sustains 40% longer dwell time than wall-of-text posts. [17] 3. Actionable Content: Posts providing actionable insights or usable frameworks generate longer dwell time because users take time to understand and apply the information. 4. Personal Stories: Posts combining professional expertise with personal narrative sustain longer dwell time. Stories create emotional investment, causing users to read more carefully. 5. Contrarian Perspectives: Posts challenging conventional thinking consistently outperform agreement-seeking posts. Controversy extends dwell time as readers engage deeper.

Practical Implications for Creators

The algorithm's evolution has direct strategic consequences:

1. Profile Optimization Is Non-Negotiable

Your headline and About section are now part of your posts' algorithm rank. Vague profiles (e.g., "Marketing Professional") underperform specific ones (e.g., "B2B SaaS Marketing | SEO Specialist | Helping Startups Scale to 7 Figures"). This creates a feedback loop: better-optimized profiles generate better-performing posts, which attract followers with higher-intent interests. [2]

2. The First Hour Is Critical

According to Buffer, Responding to every comment within the first 2 hours is no longer optional—it's essential. Posts where creators reply to comments achieve approximately 30% higher engagement across their lifecycle. Buffer's analysis of 72,000 posts from 25,000 accounts confirms this pattern applies to 83% of accounts. [18] [19]

3. Avoid Engagement Pods and Artificial Engagement

LinkedIn's AI can now detect artificial engagement patterns and deprioritizes posts showing unnatural engagement velocity. Authentic, organic comments from relevant users matter; bot-driven likes hurt. [20]

4. Prioritize Comments Over Likes

According to LinkedIn, Comments have 15x the algorithmic weight of likes. Comment threads (back-and-forth conversations) trigger aggressive reach expansion. This hierarchy explains why creators focus on generating comments rather than likes: comments trigger extended distribution, while likes have minimal algorithmic effect. [8]

5. Use Hashtags Strategically

According to LinkedIn, Use 1-3 relevant, niche hashtags rather than generic ones. Niche hashtags (e.g., #Cybersecurity) outperform generic ones (#Business) by 28% in engagement. More than 5 hashtags create diminishing returns. [4] [5]

Glossary

Social Graph : Algorithmic model based on connections and relationships (who you know). Used by LinkedIn before 2025. Interest Graph : Algorithmic model based on interests and topics (what interests you). Used by LinkedIn since 2025. Dwell Time : Duration a user spends reading or interacting with a LinkedIn post. The algorithm weights this metric in a significantly higher weight (15.6% vs 1.2% engagement correlation) versus likes. Engagement Bait : Content specifically designed to generate artificial interactions (e.g., "Comment YES if you agree"). Detected and penalized by the algorithm. Comment Thread : Back-and-forth conversation in a post's comments. Triggers aggressive reach expansion. Engagement Velocity : Speed at which a post generates engagement after publication. High velocity signals quality content.

Methodology

The data presented in this article comes from:

• Primary sources: Official LinkedIn reports, LinkedIn engineering analyses, LinkedIn internal studies
• Secondary sources: Analytics tool analyses, creator case studies, company reports
• Period covered: Data primarily from 2024-2025
• Limitations: LinkedIn's algorithm constantly evolves. Some metrics may vary by region and content type.

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