💼 LinkedIn Posts Scraper

🚀 Pull LinkedIn posts from any public profile or company page in minutes. Post text, author info, likes, comments, dates, images, top comments. No login.

🕒 Last updated: 2026-05-08 · 📊 15+ fields per post · 👤 Profile + company pages · 🚫 No auth required

Pull live LinkedIn posts from public profiles, company pages, or direct post URLs. The actor walks the LinkedIn feed for each URL you supply, fetches each post's metadata and engagement signals, and returns one structured record per post ready for social-listening, competitive intelligence, content research, or B2B sales workflows.

Every run fetches data live so you get the current state of LinkedIn at run time, not a stale dump. Records include the post URL, post text, author name and headline, post date, like count, comment count, repost count, image URLs, and top comments where exposed.

👥 Built for	🎯 Primary use cases
Marketing and content teams	Track competitor posting cadence and engagement
Social listening	Monitor brand mentions and executive activity
Sales and BD teams	Source warm leads from engagement on target posts
Recruiters	Track talent discussions and thought leadership
Researchers	Study B2B social trends and discussion topics
PR and comms	Map media presence and message resonance

---

📋 What the LinkedIn Posts Scraper does

• 📝 Post text. Full post body as displayed on LinkedIn.
• 👤 Author info. Name, headline, profile URL, and avatar.
• 📊 Engagement. Like count, comment count, repost count.
• 📅 Timestamps. Post date and a precise timestamp where exposed.
• 🖼️ Images and media. URLs of any embedded images.
• 💬 Top comments. Sample of top comments where LinkedIn exposes them.

The scraper accepts profile URLs, company URLs, or direct post URLs. It walks the feed for each URL you supply, paginates through posts, and pushes structured records to the dataset. Residential proxies are recommended for reliable LinkedIn access.

💡 Why it matters: LinkedIn is the canonical B2B content surface but its UI is paginated, JS-rendered, and aggressively rate-limited. A live, structured pull beats manual scraping for social listening, competitive intelligence, and B2B lead generation.

---

🎬 Full Demo

🚧 Coming soon: a 3-minute walkthrough showing setup, a live run, and how to pipe results into Salesforce or HubSpot via Apify integrations.

---

⚙️ Input

Field	Type	Name	Description
startUrls	array	LinkedIn URLs	Required. Profile URLs (https://www.linkedin.com/in/...), company URLs (https://www.linkedin.com/company/...), or direct post URLs.
maxItems	integer	Max Items	Free users: limited to 10 items (preview). Paid users: optional, max 1,000,000.
proxyConfiguration	object	Proxy Configuration	Proxy settings. Defaults to Apify residential pool which is required for reliable LinkedIn access.

Example 1. Posts from a CEO profile.

{
  "startUrls": [
    { "url": "https://www.linkedin.com/in/satyanadella/" }
  ],
  "maxItems": 50
}

Example 2. Posts from multiple company pages.

{
  "startUrls": [
    { "url": "https://www.linkedin.com/company/microsoft/" },
    { "url": "https://www.linkedin.com/company/google/" },
    { "url": "https://www.linkedin.com/company/apple/" }
  ],
  "maxItems": 100
}

⚠️ Good to Know: LinkedIn aggressively rate-limits scraping. Residential proxies are required for reliable pulls. The default proxyConfiguration uses the Apify residential pool which works out of the box.

---

📊 Output

The dataset returns one structured record per LinkedIn post. Each record carries identifiers, post text, author info, timestamps, engagement metrics, image URLs, and a back-reference URL. Consume the dataset as JSON, CSV, Excel, XML, or RSS via the Apify console or API.

🧾 Schema

Field	Type	Example
🆔 postId	string	urn:li:activity:7234567890
🔗 postUrl	string (url)	https://www.linkedin.com/feed/update/urn:li:activity:7234567890/
📝 text	string	Excited to share our Q2 results...
👤 authorName	string	Satya Nadella
💼 authorHeadline	string	Chairman & CEO at Microsoft
🔗 authorUrl	string (url)	https://www.linkedin.com/in/satyanadella/
🖼️ authorAvatarUrl	string (url)	https://media.licdn.com/dms/.../avatar.jpg
📅 publishedAt	ISO datetime	2026-04-12T14:30:00.000Z
📅 publishedAgo	string	3w
👍 likeCount	number	5240
💬 commentCount	number	312
🔁 repostCount	number	847
🖼️ imageUrls	array	["https://media.licdn.com/.../1.jpg", "..."]
💬 topComments	array	[{"author":"...", "text":"..."}]
📅 scrapedAt	ISO datetime	2026-05-08T12:00:00.000Z

📦 Sample records

1. Typical record (CEO post with high engagement)

{
  "postId": "urn:li:activity:7234567890",
  "postUrl": "https://www.linkedin.com/feed/update/urn:li:activity:7234567890/",
  "text": "Excited to share our Q2 results: AI-driven products growing 50%+ YoY. Thanks to our customers and partners for the trust and feedback that drives the work.",
  "authorName": "Satya Nadella",
  "authorHeadline": "Chairman & CEO at Microsoft",
  "authorUrl": "https://www.linkedin.com/in/satyanadella/",
  "authorAvatarUrl": "https://media.licdn.com/dms/avatar/satya.jpg",
  "publishedAt": "2026-04-12T14:30:00.000Z",
  "publishedAgo": "3w",
  "likeCount": 5240,
  "commentCount": 312,
  "repostCount": 847,
  "imageUrls": ["https://media.licdn.com/dms/post/q2-results.jpg"],
  "topComments": [
    {"author": "Sundar Pichai", "text": "Great results, congrats Satya."},
    {"author": "Jensen Huang", "text": "Inspiring leadership."}
  ],
  "scrapedAt": "2026-05-08T12:00:00.000Z"
}

2. Company page post (text only)

{
  "postId": "urn:li:activity:7300123456",
  "postUrl": "https://www.linkedin.com/feed/update/urn:li:activity:7300123456/",
  "text": "We are hiring senior engineers in Dublin. Apply at the link below.",
  "authorName": "Acme Corp",
  "authorHeadline": "Acme Corp",
  "authorUrl": "https://www.linkedin.com/company/acme-corp/",
  "publishedAt": "2026-05-01T09:00:00.000Z",
  "publishedAgo": "1w",
  "likeCount": 142,
  "commentCount": 8,
  "repostCount": 12,
  "imageUrls": [],
  "topComments": [],
  "scrapedAt": "2026-05-08T12:00:00.000Z"
}

3. Sparse record (low-engagement post)

{
  "postId": "urn:li:activity:7400000000",
  "postUrl": "https://www.linkedin.com/feed/update/urn:li:activity:7400000000/",
  "text": "Heading to the conference next week.",
  "authorName": "Jane Smith",
  "authorHeadline": "Engineering Manager",
  "authorUrl": "https://www.linkedin.com/in/jane-smith/",
  "publishedAt": "2026-05-07T08:00:00.000Z",
  "publishedAgo": "1d",
  "likeCount": 7,
  "commentCount": 0,
  "repostCount": 0,
  "imageUrls": [],
  "topComments": [],
  "scrapedAt": "2026-05-08T12:00:00.000Z"
}

---

✨ Why choose this Actor

	Capability
🎯	Built for the job. Scoped specifically to LinkedIn posts so you skip the parser engineering entirely.
🔖	Structured output. Clean, typed fields ready for analysis, dashboards, or downstream pipelines.
⚡	Fast. Optimized request patterns return results in seconds, not minutes.
🔁	Always fresh. Every run pulls live data, so the dataset reflects LinkedIn as of run time.
🌐	No infra to manage. Apify handles proxies, retries, scaling, scheduling, and storage.
🛡️	Reliable. Battle-tested across many runs and edge cases, with graceful error handling.
🚫	No code required. Configure in the UI, run from CLI, schedule via cron, or call from any language with the Apify SDK.

📊 Production-grade structured social data without the engineering overhead of building and maintaining your own scraper.

---

📈 How it compares to alternatives

Approach	Cost	Coverage	Refresh	Filters	Setup
⭐ LinkedIn Posts Scraper (this Actor)	$5 free credit, then pay-per-use	Public profiles + companies	Live per run	Profile, company, post URL	⚡ 2 min
Build your own scraper	Engineering hours	Full once built	Whenever you maintain it	Custom code	🐢 Days to weeks
Paid social-listening tools	$$$ monthly per seat	Vendor-defined	Periodic	Vendor-defined	⏳ Hours
Manual sourcing	Hours per check	Limited	Stale	Manual	🕒 Variable

Pick this Actor when you want broad coverage, source-native filtering, and no pipeline maintenance.

---

🚀 How to use

1. 📝 Sign up. Create a free account with $5 credit (takes 2 minutes).
2. 🌐 Open the Actor. Go to the LinkedIn Posts Scraper page on the Apify Store.
3. 🎯 Add LinkedIn URLs. Paste profile, company, or post URLs into startUrls.
4. 🚀 Run it. Click Start and let the Actor collect your data.
5. 📥 Download. Grab your results in the Dataset tab as CSV, Excel, JSON, or XML.

⏱️ Total time from signup to downloaded dataset: 3-5 minutes. No coding required.

---

💼 Business use cases

📊 Marketing and content Benchmark competitor posting cadence and engagement Identify high-performing content formats by industry Track executive activity for thought-leadership analysis Build content libraries from top-performing posts	🏢 Social listening and PR Monitor brand mentions across executive feeds Track message resonance after launch announcements Build crisis-response monitoring dashboards Map share-of-voice in your industry
🎯 Sales and BD Source warm leads from engagement on target posts Track buying signals from prospect activity Build account-based marketing target lists Power outbound personalization with recent posts	🛠️ Engineering and product Prototype social-data products without owning a crawler Replace fragile in-house LinkedIn scrapers Wire datasets into your apps via the Apify API or webhooks Skip the proxy, retry, and parsing maintenance entirely

---

🌟 Beyond business use cases

Data like this powers more than commercial workflows. The same structured records support research, education, civic projects, and personal initiatives.

🎓 Research and academia Empirical datasets for papers, thesis work, and coursework Longitudinal studies tracking changes across snapshots Reproducible research with cited, versioned data pulls Classroom exercises on data analysis and ethical scraping	🎨 Personal and creative Side projects, portfolio demos, and indie app launches Data visualizations, dashboards, and infographics Content research for bloggers, YouTubers, and podcasters Hobbyist collections and personal trackers
🤝 Non-profit and civic Transparency reporting and accountability projects Advocacy campaigns backed by public-interest data Community-run databases for local issues Investigative journalism on public records	🧪 Experimentation Prototype AI and machine-learning pipelines with real data Validate product-market hypotheses before engineering spend Train small domain-specific models on niche corpora Test dashboard concepts with live input

---

🔌 Automating LinkedIn Posts Scraper

This Actor exposes a REST endpoint, so you can drive it from any language or workflow tool.

• Node.js - call it via the Apify JS SDK.
• Python - call it via the Apify Python SDK.
• REST - hit it directly through the Apify v2 API.

Schedules. Use Apify Scheduler to capture daily or weekly snapshots of target profiles. Combine with the Apify dataset diff tools to track new posts and engagement velocity between runs.

---

💰 How much does it cost?

Apify gives you $5 in free monthly credits on the Apify Free plan, enough to test LinkedIn Posts Scraper and pull a real sample dataset. For ongoing usage:

• Starter plan ($49/month) — Recommended for individuals running LinkedIn Posts Scraper regularly. Includes higher concurrency and larger datasets.
• Scale plan ($499/month) — Recommended for teams running LinkedIn Posts Scraper at production scale.

Pay-Per-Event pricing means you only pay for what you actually use. Failed runs are never charged. See the Pricing tab on this Actor's page for exact event prices.

💡 Tips for using LinkedIn Posts Scraper

• Start with a small maxItems (3-10) to validate output format before running larger jobs.
• Use Apify Schedules to run LinkedIn Posts Scraper on a recurring basis and keep your dataset fresh.
• Export via Integrations: Apify connects to Google Sheets, Airbyte, Make, Zapier, and direct webhooks — pipe your data anywhere.
• Monitor with webhooks: trigger downstream workflows the moment a run finishes.
• Re-run failed items: if any individual records error out, re-run with their inputs only. Failed events are not charged.

⚖️ Is it legal to use LinkedIn Posts Scraper?

Yes. LinkedIn Posts Scraper only collects publicly available data. Web scraping public data has been confirmed as legal by US courts (see hiQ Labs v. LinkedIn) and is widely used for research, market analysis, and business intelligence.

However, you are responsible for:

• Respecting the source website's Terms of Service.
• Complying with GDPR, CCPA, and other applicable data-protection laws when personal data is involved.
• Not republishing copyrighted content without permission.

If you have specific compliance concerns, consult your legal team. See the Apify legal docs for more.

❓ Frequently Asked Questions

🔌 Integrate with any app

LinkedIn Posts Scraper connects to any cloud service via Apify integrations:

• Make - Automate multi-step workflows
• Zapier - Connect with 5,000+ apps
• Slack - Get run notifications in your channels
• Airbyte - Pipe results into your warehouse
• GitHub - Trigger runs from commits and releases
• Google Drive - Export datasets straight to Sheets

You can also use webhooks to trigger downstream actions when a run finishes. Push fresh data into your product backend or alert your team in Slack.

---

🔗 Recommended Actors

• 💼 LinkedIn Profile Scraper - LinkedIn profiles with contact and experience data
• 📸 Instagram Posts Scraper - Instagram posts and reels with metadata
• 🐦 X (Twitter) Scraper - Tweets with engagement and author info
• 📱 Reddit Posts Scraper - Subreddit posts with comments and engagement
• 📱 TikTok Video Scraper - TikTok videos with metadata and engagement

💡 Pro Tip: browse the complete ParseForge collection for more reference-data scrapers.

---

🆘 Need Help? Open our contact form to request a new scraper, propose a custom project, or report an issue.

---

⚠️ Disclaimer. This Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by LinkedIn or Microsoft. All trademarks mentioned are the property of their respective owners. The scraper accesses only publicly available pages and is intended for legitimate research, analytics, and lead-generation use. Users are responsible for compliance with LinkedIn's Terms of Service, applicable privacy laws, and any data-protection rules that apply.