LinkedIn Post Scraper

LinkedIn Post Scraper

The LinkedIn Post Scraper is a production-ready Apify actor that extracts public LinkedIn posts from company and personal profile URLs — fast, structured, and export-ready. It solves the pain of manual copy-paste by automating post capture (text, author details, timestamps, engagement, media, and more) so marketers, developers, analysts, and researchers can build real-time monitoring and insights pipelines at scale. Built as a LinkedIn post scraper tool and LinkedIn post extractor, it’s ideal for social listening, competitive analysis, and content strategy — with automation hooks for the LinkedIn post scraping API use cases.

What data / output can you get?

Use this LinkedIn post data scraper to collect structured post objects with engagement and metadata. Below are representative fields you’ll find in each dataset item.

Data field	Description	Example value
urn	Post URN (activity/share/ugcPost identifier)	urn:li:activity:7123456789012345678
url	Canonical URL of the post	https://www.linkedin.com/feed/update/urn:li:activity:7123456789012345678
text	Post text content	Launching our new AI features today! 🚀
postedAtTimestamp	Unix timestamp (ms)	1712345678000
postedAtISO	ISO-8601 datetime	2026-03-18T09:21:18.000Z
timeSincePosted	Human-readable time delta	2d
authorType	Author entity type	Company
authorFullName	Author’s display name	Google
authorProfileUrl	Source profile URL used to scrape posts	https://www.linkedin.com/company/google/
authorProfileId	Author profile identifier inferred from URL	google
authorHeadline	Author/company description (if available)	Leading search and AI innovations
type	Post type inferred from media	image
image	Primary image URL (if available)	https://media.licdn.com/dms/image/…
images	Array of image URLs (up to 5 per post)	["https://media.licdn.com/dms/image/…"]
numLikes	Reaction count estimate	128
numComments	Comment count estimate	14
commentsTruncated	Whether comments array is a subset	true
reactionsTruncated	Whether reactions array is a subset	true
comments	Array of extracted comment objects (sampled)	[{ "text": "Congrats!", … }]
reactions	Array of reaction objects (sampled)	[{ "type": "LIKE", "profile": { … } }]
author	Nested author profile object	{ "firstName": "Sundar", … }
attributes	Mentions detected in text/HTML	[{ "type": "PROFILE_MENTION", … }]
canReact / canPostComments / canShare	Interaction flags	true
allowedCommentersScope	Allowed commenters scope	ALL
rootShare	Root share flag	true
shareAudience	Post audience	PUBLIC

Notes:

• When input “rawData” is enabled, each post includes rawHtml with the underlying HTML snapshot.
• Exports are available via the Apify dataset in JSON by default and can be downloaded as CSV and Excel from the UI.

Key features

• ⚡️ Bold BFS discovery (deep crawl) — Turn on deepScrape to follow post URNs discovered on the page and collect more items per source for comprehensive coverage like a LinkedIn feed scraper.
• 📅 Date boundary control — Use scrapeUntil to filter out older posts and only include content from a chosen date onward.
• 🎯 Per-source limits — limitPerSource lets you define exactly how many posts to collect from each LinkedIn profile.
• 💬 Engagement & social signals — Captures numLikes, numComments, sampled comments and reactions, plus truncation flags to signal completeness like a LinkedIn post engagement scraper and LinkedIn post comments scraper.
• 🖼️ Media capture — Extracts image and images arrays from posts for creative analysis or downstream downloading with your own pipeline (works as a lightweight LinkedIn post downloader for images).
• 🧰 Raw HTML (advanced) — Enable rawData to include rawHtml for power users who need custom parsing or QA audit trails.
• 🌐 Proxy support — Optional proxyConfiguration for reliability and rate-limit avoidance on public pages.
• 🚀 Async engine & retries — Optimized with aiohttp (when available), bounded concurrency, retries, and backoff for resilient scraping.
• 💾 Export-ready — Results land in the dataset (CSV/JSON/Excel exports) and a full array is saved to the key-value store as linkedin_posts.json for easy retrieval and API automation.

How to use LinkedIn Post Scraper - step by step

1. Sign in to your Apify account and open the LinkedIn Post Scraper actor.
2. Paste one or more LinkedIn company or person profile URLs into urls (e.g., https://www.linkedin.com/company/google/).
3. Set limitPerSource to control how many posts to collect per URL (default is 10; minimum is 1).
4. (Optional) Set scrapeUntil to a date (YYYY-MM-DD) to include only posts from that date onward.
5. (Optional) Toggle deepScrape to true for richer discovery and engagement details; set rawData to true to embed rawHtml in each result.
6. (Optional) Configure proxyConfiguration for enhanced reliability at scale.
7. Click Start. As posts are found, they’re pushed to the run’s dataset. Logging shows live progress, including saved row counts and content snippets.
8. Download results from the Dataset tab in JSON, CSV, or Excel; also access the full array in the Key-value store as linkedin_posts.json.

Pro Tip: Orchestrate runs via the Apify API and connect outputs to n8n or Make.com to build automated pipelines for social listening, reporting, and content performance tracking using this LinkedIn post scraping API.

Use cases

Use case	Description
Marketing + content intelligence	Monitor competitor content and track engagement trends. Export structured post data for dashboards and briefing docs.
Social listening for brands	Aggregate recent public posts from company pages to analyze themes and sentiment at scale with a LinkedIn feed scraper workflow.
Sales/BD campaign research	Capture public posts from target accounts’ company pages to tailor outreach with recent announcements and thought leadership.
Recruitment & employer branding	Track hiring signals and culture content from company profiles to inform talent campaigns.
Academic & policy research	Build datasets of public posts for longitudinal analysis and topic modeling using the dataset and linkedin_posts.json archive.
API-driven enrichment	Pipe JSON output into data lakes or CRMs via Apify API and automation (n8n/Make.com) for repeatable enrichment with a LinkedIn post extractor.
Competitive benchmarking	Compare engagement across brands and time periods by collecting numLikes and numComments at defined intervals.

Why choose LinkedIn Post Scraper?

The scraper is engineered for precision, automation, and reliability on public LinkedIn posts.

• ✅ Accurate structured output: Consistent JSON fields with engagement, author metadata, and media links.
• ⚡ Scales with your workflow: Batch multiple sources, set strict limits, and enable deep discovery for broader coverage.
• 🧑‍💻 Developer friendly: Clean JSON, dataset exports (CSV/Excel), and automation via the Apify API for pipelines.
• 🔐 Safe and public-only: Designed for publicly available data; no login or cookies used by default.
• 🌐 Built-in resilience: Retries, backoff, and optional proxies reduce transient errors and throttling.
• 💰 Cost-effective automation: Export-ready, no browser automation overhead; perfect for repeatable jobs.
• 🔌 Integration-ready: Connect to n8n, Make.com, and internal systems for hands-free operations with a LinkedIn post scraping API approach.

Unlike brittle browser extensions, this production-grade LinkedIn posts scraping software runs on server infrastructure with controlled concurrency and robust error handling.

Is it legal / ethical to use LinkedIn Post Scraper?

Yes — when done responsibly. This actor targets publicly available LinkedIn content and does not access private data or authenticated pages.

Guidelines for compliant use:

• Collect only public information and respect platform terms and applicable laws (e.g., GDPR/CCPA).
• Avoid scraping private profiles or gated data.
• Use outputs for analysis and research; do not use data for spam or misuse.
• Consult your legal team for jurisdiction-specific requirements and enterprise compliance policies.

Input parameters & output format

Example input

{
  "urls": [
    "https://www.linkedin.com/company/google/"
  ],
  "limitPerSource": 10,
  "scrapeUntil": "2026-03-01",
  "deepScrape": true,
  "rawData": false,
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}

Parameter reference

• urls (array, required) — Add LinkedIn company or person profile URLs to scrape — one or many!
  • Default: none
• limitPerSource (integer) — How many posts to collect from each URL. Minimum 1.
  • Default: 10
• scrapeUntil (string) — Filter posts — only include content from this date onwards (YYYY-MM-DD).
  • Default: null
• deepScrape (boolean) — Scrape additional information. Recommended for richer engagement details.
  • Default: true
• rawData (boolean) — Include extra raw data in output (adds rawHtml). Turn off for cleaner results.
  • Default: false
• proxyConfiguration (object) — Optional proxy configuration for reliability and rate limit avoidance.
  • Default: null

Example output item (one post)

{
  "urn": "urn:li:activity:7123456789012345678",
  "text": "Launching our new AI features today! 🚀",
  "url": "https://www.linkedin.com/feed/update/urn:li:activity:7123456789012345678",
  "postedAtTimestamp": 1712345678000,
  "postedAtISO": "2026-03-18T09:21:18.000Z",
  "timeSincePosted": "2d",
  "isRepost": false,
  "authorType": "Company",
  "authorProfileUrl": "https://www.linkedin.com/company/google/",
  "authorProfileId": "google",
  "authorHeadline": "Leading search and AI innovations",
  "authorFullName": "Google",
  "image": "https://media.licdn.com/dms/image/…",
  "type": "image",
  "images": [
    "https://media.licdn.com/dms/image/…"
  ],
  "author": {
    "firstName": null,
    "lastName": null,
    "occupation": "Leading search and AI innovations",
    "id": "google",
    "publicId": "google",
    "trackingId": "ZXlKaGJHY2lPaUpJVXpJMU5pSXNJbXRwWkNJNklqRmpZemt4TVRrNU9XUm1NeUlzSW5OMVlpSTZJbVY0",
    "profileId": "google",
    "picture": "",
    "backgroundImage": ""
  },
  "authorName": "Google",
  "authorTitle": "Leading search and AI innovations",
  "attributes": [
    {
      "start": 0,
      "length": 6,
      "type": "PROFILE_MENTION",
      "profile": {
        "firstName": "google",
        "lastName": "",
        "occupation": "",
        "id": "user-0",
        "publicId": "google",
        "trackingId": "YnlKaGJHY2lPaUpJVXpJMU5pSXNJbXRwWkNJNklqRmpZemt4TVRrNU9XUm1NeUlzSW5OMVlpSTZJbVY0",
        "profileId": "user-0",
        "picture": "",
        "backgroundImage": ""
      }
    }
  ],
  "comments": [
    {
      "time": 1712349999000,
      "link": "https://www.linkedin.com/feed/update/urn:li:activity:7123456789012345678",
      "text": "Congrats!",
      "entities": [],
      "pinned": false,
      "originalLanguage": "English",
      "author": {
        "firstName": "Alex",
        "lastName": "Doe",
        "occupation": "",
        "id": "commenter-a1b2c3d4e5f6",
        "publicId": "alex-doe",
        "trackingId": "YWJjMTIz…",
        "profileId": "commenter-a1b2c3d4e5f6",
        "picture": "",
        "backgroundImage": "",
        "distance": "OUT_OF_NETWORK"
      }
    }
  ],
  "reactions": [
    {
      "type": "LIKE",
      "profile": {
        "firstName": "Taylor",
        "lastName": "Smith",
        "occupation": "",
        "id": "reactor-0f1e2d3c4b5a",
        "publicId": "taylor-smith",
        "trackingId": "c2hhMTIz…",
        "profileId": "ACoAA0f1e2d3",
        "picture": "",
        "backgroundImage": ""
      }
    }
  ],
  "numShares": 0,
  "numLikes": 128,
  "numComments": 14,
  "commentsTruncated": true,
  "commentsComplete": false,
  "reactionsTruncated": true,
  "canReact": true,
  "canPostComments": true,
  "canShare": true,
  "commentingDisabled": false,
  "allowedCommentersScope": "ALL",
  "rootShare": true,
  "shareAudience": "PUBLIC"
}

Notes:

• Fields such as authorFullName and authorHeadline may return “Unknown” or empty values if not present in the public metadata.
• When "rawData" is true, each item also includes "rawHtml".

Outputs are written to:

• Dataset: one item per post (exportable to JSON/CSV/Excel)
• Key-value store: linkedin_posts.json (full JSON array of posts)

FAQ

Do I need to log in to scrape LinkedIn posts?

No. This actor collects publicly available data from company and personal profile pages and does not require login or cookies. It’s designed for public content only.

Which LinkedIn URLs are supported?

Provide company or personal profile URLs in urls, such as https://www.linkedin.com/company/yourbrand/ or https://www.linkedin.com/in/username/. The actor will collect public posts from those sources.

Can it scrape comments and reactions?

Yes. The output includes numLikes, numComments, and sampled arrays of comments and reactions when detected. It also sets commentsTruncated and reactionsTruncated so you know if arrays are partial.

How many posts can I collect per source?

Use limitPerSource to specify how many posts to collect from each URL. Set any integer ≥ 1. The default is 10.

How do I filter by date?

Set scrapeUntil to a YYYY-MM-DD date. The actor will skip posts older than that timestamp, letting you focus on recent content.

Can I export the results to CSV or Excel?

Yes. Results are stored in the Apify dataset and can be downloaded as JSON, CSV, or Excel from the Apify UI. A full JSON array is also saved as linkedin_posts.json in the key-value store.

Does it support proxies?

Yes. Use proxyConfiguration to run with proxies for higher reliability and to avoid rate limits. The actor also includes retries and backoff strategies.

Can I automate this with an API or workflow tools?

Yes. Trigger runs and fetch datasets using the Apify API. You can also integrate with n8n or Make.com to build automated LinkedIn content scraper workflows — perfect for a LinkedIn post scraping API pipeline.

Closing thoughts

The LinkedIn Post Scraper is built to reliably extract structured, public LinkedIn posts for analysis, monitoring, and enrichment. With per-source limits, a date filter, deep discovery, engagement capture, proxy support, and export-ready datasets, it serves marketers, developers, analysts, and researchers alike. Use the Apify API to automate runs, connect to n8n/Make.com, and keep your dashboards up to date — start extracting smarter, structured post insights today.