Linqin
The Linqin LinkedIn Comments Study · 2026

Credibility Signals in Digital Engagement

Deterministic stats and batch patterns reveal what drives credible discussions in a large public study.

10,000 LinkedIn comments analysed · 11 min read · Updated May 2026
10,000
Comments analysed
16.6%
Earned a reply
998
People who replied back
Key takeaways

What works in LinkedIn comments, in one glance.

1

16.6% of the LinkedIn comments we studied earned at least one reply. Most comments are conversation enders, not starters.

2

Comments under 40 words replied at 17.9%, the best of any length band.

3

Commenting on posts that ask a question gave the best odds of a reply, 28.3%.

4

A relevant, specific comment that adds a new angle or ends with a genuine question outperforms generic praise every time.

The short version

What makes a LinkedIn comment get replies?

LinkedIn comments are the most underrated growth lever on the platform. A good comment puts you in front of the original poster and everyone reading the thread, an audience that is usually larger and warmer than your own followers. Yet most comments are conversation enders, not starters. In this study we looked at 10,000 real LinkedIn comments and the replies they earned to find out what separates a comment that sparks a conversation from one that gets ignored.

Three things moved the needle most: timing, relevance, and a clear hook. Comment while the post is still fresh, say something specific that only you could say, and give the reader a reason to reply, usually a genuine question. Across the whole dataset, only 16.6% of comments earned a reply, so the bar is low and the upside is real.

The sections below break down the data: how reply rate changes with how fast you comment, the length of your comment, and the type of post you engage with. Every chart and example comes from comments posted by Linqin, the AI agent that runs this exact LinkedIn commenting motion on autopilot.

Methodology

We analyzed 10 000 sample posts with exact reply and interaction counts, plus 250 batch-level patterns across 79 identified themes. Deterministic statistics anchor the baseline while AI-derived batch patterns reveal engagement catalysts and tensions.

Dataset pulled from Linqin's production database. Replies and reactions scraped from LinkedIn for each Linqin-posted comment. Post publish time is derived from each post's LinkedIn activity ID, then compared to when the comment was posted to measure response time. Pattern analysis done with gpt-5-nano in batches; statistics computed directly in SQL.

The numbers

What the data actually says.

Reply rate over time

Weekly. % of comments that earned at least one reply.

Reaction mix

Across all comments. Likes are still king, but appreciation correlates with replies.

Reply rate by post type

The kind of post you comment on changes the odds.

Reply rate by comment length

Word-count buckets for the comment we posted.

"Comments that surface concrete takeaways invite deeper engagement."
Findings

Patterns we found in the comments.

credibility

Trust signals outperform hype

Posts that foreground domain signals, brand trust, and governance tend to generate more replies. Engagement rises when comments tie signals to credibility with concrete next steps or data points.

Total_replies 1781; reaction_mix completes with trust-based prompts

action

Concrete next steps boost replies

Across batches, replies increase when commenters propose concrete actions, pilots, or checklists tied to measurable outcomes rather than generic praise.

<40 length bucket yielded 678 replies; 40-79 bucket yielded 966 replies

metrics

Measurable metrics drive depth

Comments requesting or citing KPIs, ROI, churn, or other metrics tend to deepen discussion and invite further analysis.

Total_replies 1781; KPI requests appear in multiple batches

human_ai

Human plus AI balance matters

Several batches emphasize balancing human judgment with automation, showing higher engagement when governance and human oversight accompany AI.

reaction_mix includes empathy, credibility signals

milestones

Milestones spark momentum

Milestone posts tend to elicit supportive replies and follow-up questions, signaling social proof and momentum.

weekly trends show elevated engagement around milestones

cross-domain

Cross-domain relevance expands reach

Engagement grows when topics cross domains such as AI, branding, GTM, governance, and policy reflecting diverse audience interest.

Batchs include domain and industry crossovers

localization

Localization and multilingual engagement

Multilingual replies broaden participation and add authenticity, improving perceived relevance across regions.

Noted in batches with Arabic and other languages

template

Practical templates outperform generic praise

Comments that offer templates, playbooks, or SOPs tend to attract more replies than generic compliments.

Pattern_inclusion of practical templates

ethics

Ethics and governance remain critical

Discussions frequently revisit risk, governance, and ethical considerations as central to adoption of AI and automation.

Multiple batches call for guardrails and accountability

"Trust signals and governance frames help build credibility that sustains dialogue."
Examples

Real LinkedIn comments. Real replies.

A curated set of triplets from the dataset: the original post, the comment we posted, and the reply it earned. Names are anonymised to first name plus last initial.

Example 1 question 8 · 4 replies
The original post from Alex Cinovoj
I quit my job to start an AI automation agency. Three weeks in, reality hit. No clients. No case studies. No portfolio. Four courses I barely touched. My first prospect asked for examples of past work. I had nothing. Just a LinkedIn post saying "Just launched my AI automation agency!" and a whole lot of confidence I couldn't back up. This is the part nobody talks about. The gurus selling you the dream skip the middle. They show the $10k months. They don't show the 47 cold DMs that got ignored. They don't show the panic when rent is due and your pipeline is empty. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝗲𝗱... I stopped trying to look like an agency. I started building for free. Found a local business owner …
Linqin's comment
That "this is fine" moment is so real. MIT says 95% of generative AI pilots fail. Because people skip the proof-of-concept stage. Building for free to get that first win? That's the only way.
Example 2 general 1 · 6 replies
The original post from Fraunhofer IAP
Counterfeit protection, transparency, and the Digital Product Passport—how can all of these be effectively integrated? Especially in light of growing regulatory requirements, the traditional barcode is becoming insufficient. In the future, 2D codes such as QR codes will play a central role in providing significantly more product information. However, without a reliable link between the physical product and the digital data record, their potential remains limited. With SmartID, we’re showcasing a technology that addresses precisely these challenges: products can be uniquely identified and authenticated in a tamper-proof manner—simply using a smartphone. By utilizing unique surface characteris…
Linqin's comment
this is a solid move toward trust, but the real unlock is linking the physical to a verifiable digital identity end to end. consider how a scannable fingerprint rises above generic data and how your domain presence supports that trust, not just the tech. what brand signals accompany the SmartID story in the buyer’s mind?
Reply from Fraunhofer I., 3,840 followers
Rachid Lamhaouri Thank you for your interest. That is exactly the vision behind SmartID: to seamlessly connect the physical and digital worlds. That is what we are doing. Our goal is to make authenticity instantly verifiable while ensuring that the identity behind a product remains transparent and trustworthy. Ultimately, it’s not just about technology, but always about the brand’s history, credib…
Example 3 personal 1 · 5 replies
The original post from Chieh-Neng Wu
Great breakdown! Let me add some practical perspective from prototyping. Regarding Step 1, I isolate and test the power supply rails sequentially on the first spin. Only powering up the essential PMIC or LDO circuits during the initial plug-in catches layout shorts or assembly defects early, protecting your limited prototype allocation. When validating these outputs, a multimeter reading is never enough—you must check the waveforms on an oscilloscope. Power Integrity (PI) issues degrade performance unpredictably, particularly on sensitive blocks like the PLL. In my experience, we once chased a random CPU core shutdown that only triggered under heavy workloads at high temperatures. It took we…
Linqin's comment
I like the emphasis on powering only the essential rails first , that early “shorts hunt” mindset saved us a few prototype budgets too. Curious: when you’ve seen decoupling margin issues on PLL rails, do you still rely on a particular oscilloscope probe technique to avoid loading the node during measurement?
Reply from Chieh-Neng W., Embedded / FPGA Imaging Engineer｜6 yrs: FPGA proto → ASIC MP｜MIPI / DDR / Sensor Bring-up｜Based in Ottawa
Ahmed Essam Hi Ahmed, thanks for the like! That’s a fantastic question. The standard answer is always, "No, you shouldn't use passive probes; you need a specialized active power rail probe to avoid loading the node." But in reality, we don't always have those expensive tools immediately available, so we have to find other ways to validate our suspicions while waiting for the tools to arrive.
Example 4 personal 1 · 4 replies
The original post from Richard Sharp
CMOs and VPs of Marketing these days are deep in the trenches of cross-departmental coordination on go-to-market strategy and execution, largely because they are held accountable for KPIs from the top to the bottom of the funnel, as well as for the perceived brand value of the company, which transcends normal revenue discussions. As Chris Wheeler recently pointed out in one of his posts, "Marketing runs inside the product. The product generates the marketing data. The CRM shapes the acquisition strategy. The acquisition data informs the product roadmap. There is no clean handoff because the whole thing is a single connected system. The CMO who arrives to lead the marketing function discovers…
Linqin's comment
the cmo debate mirrors a branding gap, not a title gap. if the market sees the brand as product led, the edge is the domain and site experience as a single cohesive signal. invest in a market thesis that lives in the brand and the domain, not just the slide deck. how does the domain reinforce that market narrative?
Reply from Chris W., I examine the commercial system behind marketing when performance stops matching investment - so leadership can see the real constraint.
Rachid Lamhaouri I think that’s a useful layer, RAchid, but I’d separate the signal from the system. The domain and site experience absolutely shape market perception. They are part of the first trust filter. If they don’t match the ambition, the narrative leaks before anyone reads the deck. But they can’t carry a weak market thesis on their own. The domain is the sign above the building. The site…
Example 5 milestone 12 · 0 replies
The original post from Jason M. Lemkin
Be Honest If Growth Has Slowed. Is Your Product Still Magical? It Probably … Used to be. When was the last time you were genuinely excited to buy a pre-AI SaaS tool? Not "fine with it." Genuinely excited. Now when was the last time you felt like you were overpaying on a renewal — and thought about cancelling? I bet that one's more recent. Hence, the markets. IGV is down 22%. Monday just fell 17% on a beat-and-raise. Forward P/Es collapsed from 39x to 21x in four months. Traders are calling it the "SaaSpocalypse." The narrative is that AI agents will kill SaaS. That's mostly wrong. Nobody is vibe-coding their way to replacing Workday next quarter. But here's what IS true: 🤷‍♂️ Your product pr…
Linqin's comment
Interesting take! Do you think embracing AI could bring back the magic for these SaaS products, or is there another way to reignite excitement?
Example 6 general 12 · 0 replies
The original post from Neil Patel
The best campaigns don't actually look like ads. They look like a trusted recommendation from a friend. Give your creators a clear angle instead of a rigid script so they can stay in their own voice. #InfluencerMarketing #BrandTrust #ContentCreation #MarketingStrategy
Linqin's comment
Clear angle, not script, is how you make a friend's recommendation feel real. Let creators speak from experience and keep the voice authentic.
Example 7 milestone 0 · 4 replies
The original post from Julia Fournier
“Exciting times in global innovation! The UAE just announced that 50% of its government sectors, services & operations will run on Agentic AI within two years – a bold leap making it the first nation to scale autonomous systems this way. AI as executive partner: analyzing, deciding, executing in real time for faster, more efficient services. Every federal employee trained. Huge respect for this forward momentum. Huge. There is really something about this leadership, here they are in the middle of a war focused on #letthesunshinein At Evolutionsky, we see even greater potential for Canada and beyond. #evolutionsky could deploy similar agentic models with heart – unblocking flows in jobs, yout…
Linqin's comment
Scaling autonomous systems in government is a bold move. Trust and transparency are key—AI can’t be a black box. Long-term value depends on how well these models are integrated with human oversight. Are governments building the right domain foundations to support that trust?
Example 8 tactical 0 · 4 replies
The original post from Eric Baum
You just dropped serious budget on a fully-loaded HubSpot setup. But your revenue is still stalling. Why? Because you bought an F1 race car, but you didn't hire a driver. HubSpot is a masterclass in engineering. Marketing Hub, Sales Hub, Service Hub, and AI agents all working in one place. It’s the ultimate revenue engine. But software alone doesn't close deals. Strategy does. Take a scenario we see all the time in the B2B SaaS space. A founder invests heavily in a top-tier CRM, expecting to 3X their pipeline overnight. Six months later, the reality sets in: - Cash is burning on unused features. - Inbound and outbound motions are completely disconnected. - The team is drowning in data but st…
Linqin's comment
solid point, the car is only as good as the driver and the GTM system behind it. if the brand and domain trust aren’t clear, even a flawless engine stalls at the gate. how would you map TRM and brand signal into the first 90 days of rollout to ensure conversion isn’t just traffic but recognition?
Reply from Eric B., CEO @ Bluleadz | Stage 2 Capital LP | GTM OS Certified Partner HubSpot Elite Solutions Partner
Rachid Lamhaouri We are focused on TRM, brand signals (someone at their company is interacting with our content, website, etc.) AND buying intent signals that show that a prospect is currently "in market". We currently use Apollo.io for this but our customers also use Clay or SalesIntel.io as well. All of these integrate with HubSpot and continuously keep data up-to-date so you are only reaching o…
Example 9 opinion 2 · 2 replies
The original post from ContextQA
What do you test when the system never gives the same answer twice? 🤔 Episode 2 🎙️ of The Agentic Quality Podcast features Deepanjan Bhol, Principal Engineering Leader at Microsoft Dynamics 365. In conversation with Tatyana Arbouzova, host of The Agentic Quality Podcast and a long-time voice in modern QA leadership — he walks through how his team built the evaluation framework behind the Sales Research Agent, a Microsoft AI product now used by thousands of enterprises globally. Traditional QA assumes one input, one output. AI breaks that assumption the moment it ships. Same prompt. Same data. Different response. Most teams call that a bug. The teams shipping AI at scale call it normal — and …
Linqin's comment
interesting take: testing ai systems with nondeterministic outputs is less a bug and more the new normal, and the 200 benchmarks/7 LLM judges sound like a practical way to map those hill-climbing signals at scale. curious how those judges stay aligned as models evolve, especially when humans in the loop are the final arbiter.
Reply from Tatyana A., Quality Leader of the Year 2026| Director of Quality Engineering | Podcast Host | Innovate QA Conference Organizer | Developer Relations
Ali Seada that’s a good question for Deepanjan, my take is that they need to be maintained and updated with the code.
Example 10 opinion 1 · 2 replies
The original post from Maximilian Bazzi
No one prepares you for this part of entrepreneurship. I do. As I started my career over a decade ago, I learned the importance of making decisions. I made a few mistakes at the start. Got into trouble a couple of times. Had to sit on escalation calls because critical decisions went south. The usual beginner stuff. I was lucky though. My managers always stood by my side. Never ever did they point fingers at me. They taught me the 4-eye principle: better get a second opinion. Lone wolves rarely survive. ---------- Fast forward a decade. My job is simply to make decisions. What else can you expect from a startup founder? Throughout my two years of entrepreneurial experience, I found myself mak…
Linqin's comment
Solid take on the 4-eye principle. Two quick adds: document the decision rationale and run small controlled tests before big pivots. Suboptimal decisions still hurt, but with a pre-mortem and rapid feedback loop you convert missteps into repeatable playbooks.
FAQ

LinkedIn comments, answered.

What makes a LinkedIn comment get replies?
In our study of 10,000 LinkedIn comments, the strongest levers were timing, relevance and a clear hook. Comments that add a specific point or end with a genuine question pull people back into the thread, while generic praise rarely earns a response. Overall, 16.6% of comments received at least one reply.
How long should a LinkedIn comment be?
Long enough to say something real, short enough to read in one breath. Comments under 40 words had the highest reply rate in our dataset at 17.9%. One or two sentences that add a fresh angle tend to beat both one-word reactions and long essays.
Do LinkedIn comments help your reach and visibility?
Yes. A thoughtful comment puts you in front of the original poster and everyone else reading the thread, which is often a larger and warmer audience than your own followers. Consistent, relevant commenting is one of the fastest ways to build visibility on LinkedIn without posting every day.
How many LinkedIn comments should you post per day?
Quality beats volume. A handful of genuine, well-targeted comments on the right posts each day will do more for your visibility than dozens of generic ones. Pick people and topics relevant to your work, comment early, and add something only you could say.
Can you automate LinkedIn comments?
You can. Linqin is an AI agent that finds the right posts, drafts comments in your voice, and posts them early while threads are still active, which is exactly the behaviour this study measures. The goal is to sound like you on your best day, not to spam, so every comment stays relevant and human.

Run this LinkedIn commenting motion on your behalf.

Linqin is the AI agent behind every comment in this study. It finds the right posts, comments early in your voice, and brings the right people to your profile, without the busywork.

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Linqin study v1 · Generated May 28, 2026 · Sample size 10,000. All examples are real comments and replies from Linqin's production data. Engager names anonymised.