All APIs
$0.002 per reaction ($2.00 per 1,000 reactions)19 datapoints
LinkedIn Profile Reactions API
Monitor public LinkedIn profiles for reactions, along with full post details and social activities
Sample
Sample data
Here's a sample run of posts reacted to by several LinkedIn profiles — reaction type plus full post context — showing the exact schema and results you can expect.
| # | Reaction | Reactor URL | Reactor Profile ID | Reacted At | Post URL | Post ID | Post Text | Post Author | Post Author URL | Post Author Profile ID | Post Author Handle | Post Author Type | Post Author Headline | Post Date | Post Media URL | Post Media Type | Post Reactions | Post Comments | Post Shares |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Satya Nadella likes this | ACoAAAEkwwAB9KEc2TrQgOLEQ-vzRyZeCDyc6DQ | 2026-06-23T17:18:34.332Z | 7475235868983603200 | I had a chance to put down some thoughts on how AI is changing the way cloud systems behave. Spoiler alert: It's not just about coding agents adding yet more features, it's about understanding the complexity of these cloud native systems we have built over the last decade and continue to expand.
Reliability, security and quality, these matter more than any one feature and the complexity of understanding our interconnected infrastructure and services keep growing. Systems are more connected than ever and often the details of those connections are less understood. Loosely coupled systems are way more reliable, and sadly also harder to debug when something goes wrong. New research shows that 84% of organizations report rising cloud complexity, and for 69% of them, it’s already outpacing their operating model.
AI can do more than just write code. We’ve been working on an agent to help with operations also, and today, we’re announcing the GA of the Azure Copilot Observability Agent. It pulls together signals across logs, metrics, traces, and infrastructure so you can follow what’s happening. It makes it faster to get from “something broke” to “here’s why” and “here’s what to do next. Leveraging the intelligence of the cloud and the experience of delivering reliable services at scale, it democratizes insights and makes continuous improvement easier than ever. Agentic operations means faster time to understand, time to mitigate and time to root cause for everyone.
Proud of the work behind this, and excited to hear from customers on how they are using it to improve workflows.
https://lnkd.in/gHEmzpiF | Brendan Burns | ACoAABNiBmcB4Ebxim2tgom71rMA7rX1MRgkEIA | brendan-burns-487aa590 | profile | Corporate Vice President and Technical Fellow at Microsoft | 2026-06-23T17:18:34.332Z | IMAGE | 261 | 8 | 45 | ||||
2 | Satya Nadella likes this | ACoAAAEkwwAB9KEc2TrQgOLEQ-vzRyZeCDyc6DQ | 2026-06-24T20:11:42.609Z | 7475641828441079808 | Medical diagnostic yields can be improved by reanalyzing genomic data in rare disease cases. But normally this is a very burdensome manual process. Today in Nature Medicine, we present Talos, an open-source tool that in iterative monthly reanalysis reduces the burden to 1 variant per 200 cases. For example, applying this to a cohort of 4,735 undiagnosed individuals resulted in 241 diagnoses. https://lnkd.in/gPkKRiam | Peter Lee | ACoAAACmET4Bd_wNRJpYIpEc-8XxY2kOR5Cz0BM | peterlee4 | profile | President, Microsoft Science | 2026-06-24T20:11:42.609Z | IMAGE | 158 | 12 | 13 | ||||
3 | Satya Nadella likes this | ACoAAAEkwwAB9KEc2TrQgOLEQ-vzRyZeCDyc6DQ | 2026-06-11T02:56:03.683Z | 7470670156642402304 | Sumit Chauhan just hit 30 years at Microsoft. She runs Office. The thing hundreds of millions of people use every day. And somehow she’s still one of the most curious people in every room, asking better questions than the rest of us about where AI takes all of this.
Congrats, Sumit! 30 more? :) | Ryan Roslansky | ACoAAAAKXBwBikfbNJww68eYvcu2dqDYJhHbp4g | ryanroslansky | profile | Executive Vice President at Microsoft | 2026-06-11T02:56:03.683Z | — | IMAGE | 1,874 | 93 | 11 | |||
4 | Satya Nadella likes this | ACoAAAEkwwAB9KEc2TrQgOLEQ-vzRyZeCDyc6DQ | 2026-06-16T23:22:09.201Z | 7472790652116836353 | What if one of the most powerful forces for women’s health is simply being heard?
Enter Microsoft Dragon Copilot – an AI-powered clinical assistant designed to automate and streamline healthcare documentation through ambient listening.
I’m so proud to see it highlighted in a powerful new film produced for Microsoft by BBC Storyworks Commercial Productions that launches today.
It’s a testament to how technology offers the ability to close the gender health gap by making systems more representative and responsive to women’s needs.
Explore the full film here: aka.ms/WomensHealthShift | Alysa Taylor | ACoAAAIXOMwBEAaR4FoSiW5-Af6WlWh7k8vEKNY | alysa-taylor | profile | Chief Marketing Officer, Commercial Cloud & AI at Microsoft | 2026-06-16T23:22:09.201Z | VIDEO | 196 | 3 | 17 | ||||
5 | Satya Nadella likes this | ACoAAAEkwwAB9KEc2TrQgOLEQ-vzRyZeCDyc6DQ | 2026-06-16T18:50:41.808Z | 7472722337839128577 | My conversations with customers today are less about whether to adopt AI and more focused on how to apply it in a way that creates business growth. AI should amplify the unique intelligence of an organization so value compounds over time, while also providing the visibility and control needed to ensure ROI.
In my latest blog, I share how we are thinking about this at Microsoft—from a model-diverse approach to a control plane that helps organizations manage AI as it scales across the enterprise. Microsoft IQ brings context to data and embeds AI directly into the flow of work, while Agent 365 provides the trust layer to observe, govern, and secure agents and AI artifacts across the environment.
We have built this system to help our customers and partners move decisively from experimentation to real outcomes with confidence—scaling human ambition and driving measurable results with AI across every role, organization, and industry.
To read more, visit https://lnkd.in/gxH6d26R | Judson Althoff | ACoAAAFJLZwBzRXupAjfllXxLKCQnZyfnkuRDz8 | judsonalthoff | profile | CEO, Microsoft Commercial Business | 2026-06-16T18:50:41.808Z | IMAGE | 1,162 | 64 | 235 | ||||
6 | Satya Nadella likes this | ACoAAAEkwwAB9KEc2TrQgOLEQ-vzRyZeCDyc6DQ | 2026-06-16T18:50:53.248Z | 7472722385821978624 | Agriculture has always depended on clouds. Today, not all of them are in the sky.
Built on Microsoft Azure, the İmeceMobil app is helping farmers across Türkiye make smarter, data-driven decisions. By combining AI-powered insights, satellite imagery, hyper-local weather forecasts, and marketplace tools in one platform, İmeceMobil is helping farmers save time, reduce costs, and improve productivity.
Read more here: https://msft.it/6003vYTtd | Microsoft | 1035 | — | company | — | 2026-06-16T18:50:53.248Z | VIDEO | 391 | 20 | 71 | ||||
7 | Satya Nadella likes this | ACoAAAEkwwAB9KEc2TrQgOLEQ-vzRyZeCDyc6DQ | 2026-06-09T22:36:46.595Z | 7470242517620391936 | A Formula 1 win looks like one car and one driver. It's really 2,000 people at Mercedes-AMG PETRONAS F1 moving as one — aero overnight, strategy by morning, thousands of simulations lap by lap. That's where greatness is built. And where Microsoft lives. https://msft.it/6001v5C7P | Microsoft | 1035 | — | company | — | 2026-06-09T22:36:46.595Z | VIDEO | 1,123 | 23 | 166 | ||||
8 | Satya Nadella likes this | ACoAAAEkwwAB9KEc2TrQgOLEQ-vzRyZeCDyc6DQ | 2026-06-03T19:22:37.882Z | 7468019332183212033 | The agentic AI era is here.
From Taipei, Jensen Huang joined Satya Nadella at #MSBuild to show how NVIDIA and Microsoft are building it together, from Windows devices to AI factories at scale.
▶️ Watch the conversation: https://nvda.ws/4fWZKiM
🔗 Read the recap blog: https://nvda.ws/4uQ1Buy | NVIDIA | 3608 | — | company | — | 2026-06-03T19:22:37.882Z | VIDEO | 1,177 | 71 | 152 | ||||
9 | Satya Nadella likes this | ACoAAAEkwwAB9KEc2TrQgOLEQ-vzRyZeCDyc6DQ | 2026-06-02T19:14:47.936Z | 7467654973221269504 | Super excited to announce seven new world-class MAI models today. They represent what we consider a new era in AI designed to keep you in control and on the frontier.
First is our text foundation model, MAI-Thinking-1, exceptionally strong on reasoning and SWE tasks.
- It’s a 35B active parameter MoE with a 256K context window. Independent human raters on Surge prefer it for overall quality in blind side-by-sides versus Sonnet 4.6, and it’s achieved 97% on AIME 2025, the key measure of its general-purpose reasoning abilities.
- It's at 53% on SWE Bench Pro, placing it right alongside Opus 4.6 on one of the toughest coding benchmarks.
- And since we co-designed our models with our own silicon, MAI-Thinking-1 is optimized on our MAIA 200 chip. Benchmarking head-to-head against the GB200, we see 30% better performance per dollar as well as a 1.4x performance-per-watt gain when running our MAI models on the MAIA 200 end-to-end.
Next is MAI-Image-2.5 and its Flash variant. Two super strong models now at #2 on the leaderboards, surpassing the score of Nano Banana 2 on image editing.
Last for now is MAI-Code-1-Flash, our new inference efficient coding model, especially tuned for VS Code and GitHub Copilot CLI.
- Code-1-Flash achieves 51% on SWE Bench Pro, despite having just 5B parameters, putting it closer to Haiku in size but cheaper in cost.
All of this is the foundation for Microsoft Frontier Tuning. It lets you customize our models to create custom, company-specific agents that only you control. You can make our model, your model. Your data. Your agents. Your moat.
Early adopters are already seeing a difference. When we tuned our models for a market-leading organization's tasks, MAI delivered the highest win rate, outperforming GPT-5.5 on quality, while being 10x lower on cost.
Also really excited to be collaborating with the amazing team at Mayo Clinic to jointly train a new frontier AI model for healthcare.
Our announcements today mark another milestone on the road to humanist superintelligence. You can learn more and about our other new models in our latest blog: https://lnkd.in/gMs4TJxC | Mustafa Suleyman | ACoAAA9eyYUBh9JQIB19tmDanxKjRK9p8Nw4FrY | mustafa-suleyman | profile | CEO, Microsoft AI | 2026-06-02T19:14:47.936Z | — | IMAGE | 6,452 | 201 | 709 | |||
10 | Satya Nadella likes this | ACoAAAEkwwAB9KEc2TrQgOLEQ-vzRyZeCDyc6DQ | 2026-05-30T01:25:28.160Z | 7466298704019263488 | Twenty years ago, Microsoft broke ground on our first data center in Quincy, Washington. At a time when so many small towns have been shrinking, Quincy has grown, and the partnership behind that story is one worth telling. Watch how a farm town in Central Washington helped build the cloud, and built a thriving future of its own. https://lnkd.in/ePjWhyQu | Brad Smith | ACoAAAFqmHMBRvI7O6GyhdtaXPyuHZSa4aQUEnw | bradsmi | profile | Vice Chair and President at Microsoft Corporation | 2026-05-30T01:25:28.160Z | VIDEO | 1,029 | 53 | 101 |
Playground
Run this API
Inputs
Output limits
$0.002 per reaction
Billed per reactions returned — no row cap set
live
curl -X POST https://api.mindcase.co/v1/data/linkedin/profile-reactions/run \
-H "Authorization: Bearer mk_live_YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"params": {
"profiles": "",
"postedLimit": ""
}
}'Edit the form to update this live.
Overview
About LinkedIn Profile Reactions API
LinkedIn Profile Reactions extracts engagement data from public profiles to reveal who is interacting with specific posts. It returns structured data including the reactor name, the specific reaction type, reaction timestamp, post text, and post author details.
Growth engineers use LinkedIn Profile Reactions to map social graphs and identify active prospects. Sales teams use it to track who is engaging with competitor content or industry thought leaders to build high-intent lead lists.
Real-time dataSelf-servePay per resultZero setup time
Cost
Pricing
$0.002per reaction
$2.00 per 1,000 reactions
$2.00 per 1,000 reactions. Each reaction returned counts as one billable row. The total count scales based on the number of profiles provided and the max results limit set for each. Failed runs don't count.
Pay as you goNo subscriptionNo setup feeCancel anytime
Cost calculator
Total rows250 reactions
Total cost$0.500
Examples
Use cases
A few common ways teams put LinkedIn Profile Reactions API to work — copy a prompt below to try it yourself.
Lead generation tracking
Monitor who reacts to specific industry influencers to build targeted outreach lists based on recent social activity.
Get the latest 50 reactions from Satya Nadella's LinkedIn profile
Competitor audience mapping
Compare engagement across multiple executive profiles to identify shared followers and active community members.
Pull reactions for Reid Hoffman, Marc Benioff, and Brian Chesky
Engagement trend analysis
Analyze the types of reactions received on recent posts to gauge sentiment and audience composition.
Find the last 200 reactions from Bill Gates' LinkedIn
High-intent signal detection
Filter for specific reaction counts to find the most active users engaging with a profile over a set period.
Get 100 reactions from Mary Barra's profile
Get started
Ready to pull reactions?
Sign up to run live queries against LinkedIn Profile Reactions API via chat, form, or API.
FAQ
Frequently asked questions
- Does this include comments?
- No, LinkedIn Profile Reactions focuses on likes and other reactions. Use the LinkedIn Post Comments agent if you need to extract text-based replies and comment threads.
- How fresh is the data?
- Data is fetched live on every run. LinkedIn Profile Reactions does not use a cache, ensuring you see the most recent engagement available on the profile.
- Can I pull reactions from multiple profiles in one run?
- Yes, you can pass an array of multiple profile names. LinkedIn Profile Reactions will process each profile and return the combined reaction data in a single dataset.
- How do I integrate with Zapier, n8n, Google Sheets, or Airtable?
- LinkedIn Profile Reactions returns clean JSON and CSV output. Both formats work with every workflow tool. Pipe them in via a webhook step, file download, or by hitting the REST endpoint on a schedule. Same for Slack notifications, Notion databases, and other downstream systems.
- What fields are returned?
- Per reaction: the reactor (name + profile URL), the reaction type, whether it was on a post or a comment, and when it happened; plus full context of the post — URL, text, author (name, URL, type, headline), true publish date, and its reactions/comments/shares.
Related
More LinkedIn APIs
LinkedIn Post Comments API
Extract LinkedIn post comments and replies, including likes and reactions, from a list of post URLs
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LinkedIn Post Reactions API
Extract reactions from LinkedIn posts and comments, providing likes and appreciations
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LinkedIn Ads API
Extract LinkedIn ad details, ad copy, media URL, and call-to-action buttons from Ad Library URLs
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LinkedIn Profile Comments API
Extract LinkedIn profile comments and their social activities like likes and reactions, requiring no input
LinkedIn$0.002 per comment$2.00 per 1,000 comments
LinkedIn Companies API
Get LinkedIn company data — by company URL, or by searching with filters (location, size, industry). Returns the full company profile either way.
LinkedIn$0.004 per company$4.00 per 1,000 companies
LinkedIn Company Employees API
List LinkedIn company employees — name, role, location, seniority — from a list of company URLs
LinkedIn$0.004 per profile$4.00 per 1,000 profiles