LinkedIn Post Search Scraper

The ultimate LinkedIn Post Scraper and Search Extractor on Apify. Scrape and extract LinkedIn posts in bulk using keywords, LinkedIn search URLs, or specific filters. Extract full post text, reactions, comments, reposts, media attachments, and author details — all structured, paginated, and ready for analysis.

Why Use This LinkedIn Post Scraper?

• Bulk extraction — Collect thousands of LinkedIn posts from any search query in a single run.
• Rich structured data — Get post text, timestamps, engagement stats (likes, comments, reposts), author info, and attached media.
• Advanced Flexible Input — Search by keywords, pass a full LinkedIn search URL, or use advanced filters like content type, date posted, author industry, and more.
• Automatic Pagination — No manual scrolling or page handling; the scraper fetches all posts up to your specified limit automatically.
• Export-ready — Download extracted LinkedIn posts as JSON, CSV, Excel, XML, or any format supported by Apify.

Input: How to Scrape LinkedIn Posts

You can configure the actor to scrape posts using either keywords, a LinkedIn search URL, or specific filters.

Search Parameters

Field	Type	Required	Default	Description
keywords	string	No*	—	Keywords to search for in LinkedIn posts
searchUrl	string	No*	—	Full LinkedIn search URL. Overrides keywords, filters, and sort options if provided
maxPosts	integer	No	100	Maximum number of posts to collect
profileScrapingMode	string	No	short	Author data detail level: short (basic info from search results) or main (full profile/company scrape for each author)

* At least one of keywords or searchUrl must be provided.

Filters & Sort Options

Refine your LinkedIn post search with these advanced filters:

Field	Type	Default	Description
sortBy	string	relevance	Sort order: relevance (Top match) or date_posted (Latest)
datePosted	string	—	past-24h, past-week, or past-month
contentType	string	—	videos, photos, jobs, liveVideos, articles, documents, collaborativeArticles
fromMember	string[]	—	Filter by specific member IDs
fromOrganization	string[]	—	Filter by specific company/organization IDs
mentionsMember	string[]	—	Filter by mentioned member IDs
mentionsOrganization	string[]	—	Filter by mentioned company/organization IDs
authorIndustry	string[]	—	Filter by author's industry IDs
authorCompany	string[]	—	Filter by author's company IDs
authorJobTitle	string	—	Filter by author's job title keywords

Usage Examples

1. Search LinkedIn Posts by Keywords with Filters

{
    "keywords": "machine learning",
    "maxPosts": 50,
    "sortBy": "date_posted",
    "datePosted": "past-week"
}

2. Scrape Posts Using a LinkedIn Search URL

{
    "searchUrl": "https://www.linkedin.com/search/results/content/?keywords=nodejs&sortBy=date_posted",
    "maxPosts": 200
}

Output — LinkedIn Post Data Structure

The scraper produces a dataset where each item is a detailed LinkedIn post structure:

Field	Type	Description
urn	object	LinkedIn URNs for the post (activity_urn, share_urn, ugcPost_urn)
full_urn	string	Full LinkedIn activity URN identifier
posted_at	object	Post date and time — includes date (ISO format), relative (human-readable), and timestamp (Unix ms)
text	string	Full text content of the LinkedIn post
url	string	Direct permalink to the post on LinkedIn
post_type	string	Type of post (e.g. regular)
author	object	Author details — first_name, last_name, headline, username, profile_url, profile_picture
stats	object	Engagement metrics — total_reactions, like, support, love, insight, celebrate, funny, comments, reposts
media	object | null	Attached media — type (video, image, etc.), url, thumbnail. null if no media is attached

Sample Output Data

[
    {
        "urn": {
            "activity_urn": "7420485585376620544",
            "share_urn": null,
            "ugcPost_urn": "7420485424273416192"
        },
        "full_urn": "urn:li:activity:7420485585376620544",
        "posted_at": {
            "date": "2026-01-29 11:54:39",
            "relative": "1 week ago • Visible to anyone on or off LinkedIn",
            "timestamp": 1769687679069
        },
        "text": "So much of dev work happens in the context of a larger team...",
        "url": "https://www.linkedin.com/posts/...",
        "post_type": "regular",
        "author": {
            "first_name": "Satya",
            "last_name": "Nadella",
            "headline": "Chairman and CEO at Microsoft",
            "username": "satyanadella",
            "profile_url": "https://www.linkedin.com/in/satyanadella",
            "profile_picture": "https://media.licdn.com/dms/image/..."
        },
        "stats": {
            "total_reactions": 1749,
            "like": 1518,
            "support": 13,
            "love": 50,
            "insight": 83,
            "celebrate": 82,
            "funny": 3,
            "comments": 154,
            "reposts": 174
        },
        "media": {
            "type": "video",
            "url": "https://dms.licdn.com/playlist/vid/...",
            "thumbnail": "https://media.licdn.com/dms/image/..."
        }
    }
]

Top Use Cases for LinkedIn Post Scraper Tool

• LinkedIn Content Analysis & Strategy — Analyze posting frequency, popular topics, and content strategies of industry leaders, influencers, or competitors.
• Engagement Benchmarking — Compare LinkedIn reaction counts, comment rates, and repost ratios across specific keywords to benchmark social media performance and ROI.
• B2B Lead Generation — Identify and extract prospects who actively post about specific topics, tools, or industries on LinkedIn, empowering targeted outreach.
• Social Listening & Trend Tracking — Monitor what key voices in your industry are discussing to spot emerging trends, pain points, and market shifts early.
• Sentiment Analysis & Natural Language Processing (NLP) — Feed scraped LinkedIn post datasets into NLP pipelines to gauge public sentiment around specific brands, products, or events.
• Influencer Research & Discovery — Evaluate potential LinkedIn influencer partnerships by efficiently analyzing their historical content output, engagement metrics, and audience reach.
• Competitive Intelligence — Track how competitor executives and companies communicate product launches, strategy shifts, or hiring activity directly on their LinkedIn feeds.
• AI & ML Training Data Collection — Build high-quality datasets of professional, industry-specific text content for fine-tuning Large Language Models (LLMs) and custom AI applications.