Is the LinkedIn Algorithm Random?

If you have read the recent rant posts about the LinkedIn algorithm, LinkedIn has become a source of immense frustration. You spend hours crafting a thoughtful, deeply researched article on industry trends, only to see it languish with a handful of views. Meanwhile, a shallow post from a well-connected VP hits tens of thousands of impressions.

It feels random. It feels unfair. It feels like the algorithm is broken.

But what if it isn't broken at all? What if it's doing exactly what it was designed to do, just not according

to the rules we've learned from Facebook, Instagram, or TikTok? Most users implicitly judge LinkedIn like TikTok, X, or Instagram: they expect the algorithm to rank posts purely on engagement or content quality. But, recent analysis and reverse-engineering of the platform show something different: LinkedIn’s algorithm doesn't primarily rank posts based on their standalone quality. It ranks people based on their professional identity, consistency, and network behavior.

The confusion stems from applying a "content-first" mental model to a "people-first" professional network. On other platforms, the goal is to serve you the most entertaining or arresting piece of content right now. On LinkedIn, the goal is to connect you with the most relevant professional voices over the long term.

The Context

To understand the current state of LinkedIn, we must first understand a significant shift in the platform's environment. Data from late 2024 and early 2025 shows a clear trend: organic reach for individual posts is down, while the value of deep engagement is up.

Shrinking Organic Visibility

Average organic reach for posts has seen a notable decline, with some reports indicating a drop of over 60% compared to previous years.

The Rise of Paid Content

Space in the feed is increasingly being allocated to sponsored content, squeezing out organic posts. This makes the remaining organic slots far more competitive.

Quality Over Quantity of Engagement

While reach is down, engagement per impression is often up for high-quality interactions. The algorithm now heavily penalizes low-effort activity like one-word comments ("Agreed!", "Thanks!") and AI-generated responses, Devaluing them by as much as 5-7x. Conversely, thoughtful, multi-sentence comments that spark a dialogue are given immense weight.

This new environment means you can no longer rely on a single "home run" post to build your presence. Instead, you must build a trustworthy, consistent professional profile that the algorithm wants to recommend.

How the LinkedIn Algorithm Works

LinkedIn’s feed uses a multi-stage, machine-learning ranking pipeline. Each time a member opens the feed, LinkedIn scores tens of thousands of candidate updates and ranks the most relevant at the top, using hundreds of features. Recent engineering posts describe a “two-pass” architecture: a first-stage model quickly selects candidate posts from FollowFeed, then a second, heavier set of models re-ranks those candidates for each member.

Basically, the LinkedIn algorithm works less like a content curator and more like a professional matchmaker. Before it decides to show your post to thousands of people, it asks a fundamental question: "Who is this person, and have they earned the right to be heard on this topic?"

Your visibility is determined by a cumulative "reputation score" built over time through thousands of small actions.

These models predict several quantities for every potential member–post pair: probabilities of clicking, reacting, commenting, sharing, and also expected dwell time or probability of “long dwell.” LinkedIn combines those predictions into a single value function that decides how high an update should appear for that specific member. In other words, the ranking score is not a universal property of the post; it is always “this post by this person for this specific viewer.”

Ranking Pillar	What The Algorithm Looks For	Why It Matters
1. Professional Identity & Relevance	Is your profile complete? Does your headline, "About" section, and work history clearly define your niche? Do you consistently post about topics relevant to your stated expertise?	The algorithm needs to categorize you to know who to show your content to. A confused signal (e.g., a marketing manager posting about crypto one day and baking the next) leads to suppressed reach.
2. Consistency & Reliability	Do you show up regularly? Are you posting 3-5 times a week, or disappearing for a month? Are you a reliable source of information in your field?	Like a colleague, the algorithm trusts people who show up consistently. Sporadic activity signals a lack of commitment and makes you a riskier recommendation.
3. Network Behavior & Reciprocity	Are you just broadcasting, or are you participating? Do you reply to comments on your own posts? Do you leave thoughtful comments on others' posts? Who are you engaging with?	LinkedIn is a social network. The system rewards "good citizens" who create value for others through interaction, not just those who use it as a megaphone.

In short: A mediocre post from a highly-ranked person will often outperform a brilliant post from a poorly-ranked person.

Dwell Time

A critical shift came when LinkedIn formally integrated dwell time—how long people actually spend on a post—into feed ranking. Engineering teams have described models predicting the probability of a short dwell and using that as a negative ranking signal; posts that people quickly skip are suppressed, while those that hold attention are rewarded.

Because explicit actions (likes, comments, shares) are sparse, time-based signals provide dense labels for every impression, making them far more useful for training ranking models. External analyses and LinkedIn-focused marketing studies now treat dwell time as the dominant ranking factor, effectively overtaking basic counts of likes and comments. This is also why “quiet” consumption—people reading without reacting—is still powerful fuel for a creator’s future reach.

How LinkedIn Differs from Other Social Algorithms

Superficially, every major platform optimizes some version of “engagement,” but the abstraction layer differs:

Entertainment feeds (TikTok, Instagram Reels, YouTube Shorts) mostly learn a mapping from post/video → probability of watch time, rewatch, like, or follow, with the creator’s identity often treated as a feature but not the primary organizing unit.

LinkedIn’s feed explicitly builds personalized models per member, drawing heavily from their professional context, company graph, and interest graph, and heavily weighting dwell time and professional relevance over raw virality.

Ranking People: Author and Member Level Signals

LinkedIn’s own engineering blogs talk explicitly about per-member and per-update models using generalized linear mixed models (GLMix) and similar techniques. These models effectively maintain parameters for individual members and/or content sources, so the system learns differently for each person and author over time.

Network graph and relationship strength

LinkedIn uses both the social graph (who you’re connected to) and an “interest graph” (entities and topics you follow or frequently engage with). Updates from close connections, coworkers, people in the same company or industry, and authors whose content you’ve spent time with in the past naturally get preferential treatment.

Profile authority and professional context

LinkedIn’s “Knowledge Graph” encodes information about companies, roles, industries, and skills, which feeds into relevance models. A senior data leader posting about analytics is more likely to be considered relevant on that topic than a junior generalist, even if raw post engagement starts similar, because their profile and graph signal authority in that space.

Historical performance and track record

Models track how often a creator’s past content generated long dwell time, comments, and meaningful conversations, and they use those signals when scoring new posts from that creator. LinkedIn’s 2019–2025 engineering posts explicitly mention “timely feedback to content creators” and “audience builders” as optimization targets, meaning the algorithm is tuned to reward accounts that repeatedly spark quality interactions.

Behavior as a participant, not just a broadcaster

LinkedIn optimizes for “contributions” (shares, comments, and reactions) as both outcome and signal. Users who regularly comment thoughtfully, reply to others, and sustain conversations effectively train the system to see them as valuable participants, increasing the baseline distribution of their own posts.

Ranking Posts: Content Still Matters, But Second

Post-level signals still play a meaningful role; they just sit downstream of who is posting and who is seeing it. LinkedIn’s official and third‑party breakdowns highlight several content-level factors:

Topical and Professional relevance

Content that educates, informs, or advances careers in the viewer’s industry is explicitly prioritized over pure entertainment.

Freshness and recency

Like other feeds, LinkedIn blends relevance with timeliness, prioritizing more recent updates in the candidate set before ranking. Time windows for distribution are especially important in the first few hours, when early dwell time and interactions help the model infer whether a post deserves wider reach.

Format and Linking

Text, images, documents, carousels, and video each perform differently. Native, in-feed formats—especially text plus image or document/carousel posts—tend to generate higher engagement than posts that send people offsite. Including an external link in an original post can reduce reach by 25–35%, because the algorithm prefers content that keeps users on LinkedIn.

Engagement on the specific post

Comments, reactions, shares, and “see more” clicks are all modeled as engagement events, but comments and long dwell appear to be weighted far more heavily than quick reactions.

The 3-Stage Ranking Process

When you hit "Post," your content doesn't just go out to everyone. It enters a rigorous, three-stage filtering process designed to protect the user experience.

Stage 1: Immediate Quality Filter

An AI instantly scans your post, image, and links. It's looking for obvious spam, low-quality clickbait, or policy violations. If flagged, your post dies here. If it passes, it moves to the next stage.

Stage 2: The "Golden Hour" Test

Your post is shown to a small, select sample of your first-degree connections—people the algorithm knows usually engage with your content. This is the most critical phase. The system watches closely: Do they stop scrolling to read it (dwell time)? Do they click "see more"? Do they like, share, or, most importantly, comment?

Stage 3: Network & Relevance Ranking

Having passed the initial test, the algorithm now looks at your personal ranking signals to decide how widely to distribute the post beyond your immediate circle. It looks at your subject matter expertise, your relationship with potential viewers, and the topical relevance to their interests to find the perfect audience match.

Evidence That People Are Being Ranked

Several independent datasets show how heavily outcomes concentrate in a relatively small set of creators, and how strongly reach scales with the creator, not just the content.

A 2025 analysis of 5.5k U.S.-based LinkedIn influencers found that 71.36% had an average engagement rate between 0–1%. This indicates a heavy long‑tail where a minority of creators capture outsized attention—consistent with a system that keeps amplifying accounts with strong historical performance and networks.

Data from LinkedIn creator campaigns in Q4 2025 show that average reach (impressions) increases meaningfully with follower brackets—from roughly 1k average reach for creators under 10k followers up to nearly 9k for creators with 30–50k followers—while comment rates actually drop after about 30k. This suggests that once a creator builds a strong baseline “author score,” the algorithm reliably distributes new posts widely even when engagement-per-impression deteriorates.

As follower counts grow, engagement rates decline, yet those large accounts still dominate total impressions. Again, this is characteristic of a people‑ranking system that protects distribution for “proven” creators even when any single post underperforms.

Follower range (personal profiles)	Avg engagement rate	“Great” engagement rate
0–1,000	8–12%	25%+
1,000–5,000	5–8%	20%+
5,000–10,000	3–6%	15%+
10,000–50,000	2–4%	12%+
50,000–100,000	1–3%	10%+
100,000+	0.5–2%	8%+

Personal profiles outperform Company pages

One detailed 2025 breakdown notes that personal profiles typically achieve 3–5x more organic reach than company pages under similar follower counts. Benchmarks from other studies show company pages often hovering around 1–5% engagement, with larger enterprises at the low end. That gap widens if company content relies heavily on external links, which suppress reach.

The lesson is clear: On LinkedIn, you cannot growth-hack your way to sustained visibility with clever copywriting tricks alone. You have to earn it by being a consistent, clearly defined, and contributing member of your professional community. Build your reputation first, and post reach will follow.

Seen this way, the path to consistent reach is less about “beating the algorithm” with a single viral post and more about steadily training it to trust you as a valuable node in the professional network.