LinkedIn Post Comments Scraper

LinkedIn Post Comments Scraper

The LinkedIn Post Comments Scraper is a purpose-built LinkedIn comments scraper that extracts structured comments and nested replies from LinkedIn posts — complete with reaction counts and author details. It solves the pain of manual copy-paste by automating LinkedIn comments data extraction at scale. Built for marketers, developers, analysts, and researchers, this LinkedIn post comments extractor lets you scrape LinkedIn post comments across multiple posts and stream results into an Apify dataset for analysis or automation.

What data / output can you get?

Below is a snapshot of the structured fields the actor saves to the dataset when you scrape LinkedIn post comments. Fields reflect the real JSON keys pushed by the actor.

Data field	Description	Example value
postUrl	Source post URL submitted in input	https://www.linkedin.com/feed/update/urn:li:activity:7289521182721093633/
comment_id	Parsed LinkedIn comment ID	"706543210987654321"
text	Plaintext content of the comment	"Great update — thanks for sharing!"
posted_at.timestamp	Unix ms timestamp when the comment was posted	1713871200000
posted_at.date	Local datetime string for readability	"2024-04-23 15:20:00"
posted_at.relative	Relative time (s/m/h/d/w/y)	"2d"
is_edited	Whether the comment was edited	false
is_pinned	Whether the comment was pinned	false
comment_url	Permalink to the comment (generated if missing)	"https://www.linkedin.com/feed/update/urn:li:activity:7289521182721093633?commentUrn=urn:li:fsd_comment:(706543210987654321,…)"
author.name	Comment author’s display name	"Jane Doe"
author.headline	Author headline (Full mode)	"Product Manager at Acme"
author.profile_url	Link to author profile	"https://www.linkedin.com/in/janedoe/"
author.profile_picture	Author profile photo URL (Full mode)	"https://media.licdn.com/dms/image/..."
stats.total_reactions	Total reactions on the comment	12
stats.reactions	Object with reaction type counts	{"like": 9, "appreciation": 1, "empathy": 0, "interest": 2, "praise": 0}
stats.comments	Number of replies to this comment	3
replies	Nested array of reply objects (same structure)	[ { …reply… } ]
post_input	ID parsed from the post URN	"7289521182721093633"
totalComments	Total available root comments (from paging)	145

Notes:

• Replies are included when you enable the Scrape Replies setting. The nested reply objects use the same structure as top-level comments.
• Author headline and profile_picture are included only when Profile Scraper Mode is set to “full”.
• You can download the dataset in common formats such as JSON, CSV, or Excel directly from Apify for easy LinkedIn post comments export.

Key features

• 💬 Bold reactions & replies capture Collect top-level comments and, when enabled, nested replies — plus structured reactions (like, appreciation, empathy, interest, praise) and total reaction counts for each comment. Ideal for a LinkedIn reactions and comments scraper workflow.

• 📦 Batch scraping across multiple posts Paste an array of LinkedIn post URLs or activity IDs into startUrls to scrape LinkedIn post comments in bulk. This is a LinkedIn comments crawler designed for throughput.

• 📅 Smart date filtering Use postedLimit to keep only comments from the last 24h, week, month, 3 months, 6 months, or year. Helpful for time-bounded analysis and campaign tracking.

• 👤 Flexible profile detail modes Choose profileScraperMode: short (no profile picture/headline; lower cost) or full (include author.profile_picture and author.headline).

• 🔒 Proxy-aware and resilient Built-in rate-limit mitigation and proxy escalation: random delays between requests, exponential backoff on 429s, and automatic fallback from direct connection to datacenter and then residential proxies when needed.

• ⚙️ Developer-friendly via Apify Results stream to an Apify dataset for easy integration with the Apify API, webhooks, Make.com, or custom LinkedIn comment scraper Python pipelines. Perfect for automating LinkedIn comments scraping scripts and exports.

• 🧠 Real-time pagination handling Efficiently paginates through comments using LinkedIn’s internal GraphQL with dynamic query ID discovery and de-duplication, ensuring clean, structured output.

• 🍪 Auth-supported for reliability Add your li_at cookie for authenticated access. Without it, LinkedIn requires login to fetch comments and the run will likely fail — the actor logs a clear warning.

How to use LinkedIn Post Comments Scraper - step by step

1. Sign in to Apify Create or log in to your Apify account so you can run the actor and access the dataset.

2. Open the actor Find “LinkedIn Post Comments Scraper” in your Apify console and click Try it.

3. Add post inputs In startUrls, paste one or more LinkedIn post URLs or IDs (activity:123…, ugcPost:123…). Each post will be processed separately.

4. Set limits and filters

   • resultLimitPerPost: Choose how many comments to collect per post (1–500; default 10).
   • postedLimit: Keep only recent comments (e.g., "week") or "any" for all.

5. Configure replies and profile detail

   • scrapeReplies: Turn on to include nested replies.
   • profileScraperMode: "short" for basic author fields or "full" for headline and profile picture.

6. Provide authentication (recommended)

   • liAt: Paste your LinkedIn li_at session cookie for authenticated access. The actor logs: without li_at, LinkedIn requires authentication and the run will likely fail.

7. Optional: Proxy configuration Use proxyConfiguration if you want to supply Apify Proxy settings. The actor can also escalate automatically to datacenter and then residential proxy if needed.

8. Run and monitor Click Start. Watch logs for progress (pages fetched, items saved). The actor automatically handles pagination and pushes items as they’re collected.

9. Download results Open the run’s Dataset to preview results and export LinkedIn post comments to CSV, JSON, or Excel. Integrate via the Apify API for automation.

Pro Tip: Chain this run in a Make.com/n8n workflow or call the Apify API from your LinkedIn comment scraper Python scripts to automate end-to-end LinkedIn post comments download and enrichment.

Use cases

Use case	Description
Marketing campaign analysis	Measure engagement on campaign posts by extracting comments, replies, and reactions; quantify sentiment and community response.
Community management	Aggregate feedback and questions from post threads to prioritize responses and track recurring themes.
Product research	Mine comments for feature requests and pain points for roadmap planning and VOC reports.
Academic research	Collect structured discussion data for social media discourse analysis and longitudinal studies.
Sales intelligence	Identify warm leads commenting on thought-leadership posts; export to CSV/JSON for CRM enrichment.
API pipeline & Python	Use the Apify API with this LinkedIn comments scraping script in Python to automate data ingestion to warehouses.
Competitive monitoring	Track competitor post reactions and comment volume over time to benchmark engagement.

Why choose LinkedIn Post Comments Scraper?

The LinkedIn Post Comments Scraper focuses on precision, automation, and resilient collection of discussions from LinkedIn posts.

• 🎯 Accurate, structured output: Clean JSON with stable keys for postUrl, comment_id, author, stats, replies, and more.
• 💬 Reactions and replies: Goes beyond basic text to include reaction counts and nested replies when enabled.
• 📈 Built for scale: Accepts multiple posts at once and paginates reliably with de-duplication.
• 🧩 Developer access: Stream results to an Apify dataset and integrate via API in your data pipelines or Python scripts.
• 🔄 Automation-ready: Ideal for scheduled runs and workflow orchestration with webhooks or integration platforms.
• 🛡️ Robust & proxy-aware: Handles rate limits with backoff and automatic proxy escalation (direct → datacenter → residential).
• 💰 Cost control: Choose profileScraperMode to optimize data depth and cost per run.

Compared to extension-based scrapers and unstable tools, this production-ready LinkedIn comment scraping tool uses server-side collection, structured outputs, and resilient networking to minimize failures and ensure consistency.

Is it legal / ethical to use LinkedIn Post Comments Scraper?

Yes — when used responsibly. This tool is designed to collect data available to your session and relies on your li_at cookie for authenticated access when needed.

Guidelines:

• Scrape only data you are permitted to access and that’s visible to your account.
• Follow applicable laws and regulations (e.g., GDPR, CCPA) and respect LinkedIn’s terms.
• Avoid collecting or sharing sensitive personal data; use results for legitimate analysis and research.
• Consult your legal team for edge cases and compliance requirements in your jurisdiction.

Input parameters & output format

Example JSON input

{
  "startUrls": [
    "https://www.linkedin.com/feed/update/urn:li:activity:7289521182721093633/",
    "urn:li:activity:7289521182721093633",
    "activity:7289521182721093633"
  ],
  "resultLimitPerPost": 50,
  "postedLimit": "week",
  "scrapeReplies": true,
  "profileScraperMode": "full",
  "liAt": "AQED...your_li_at_cookie_here",
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"]
  }
}

Parameters

• startUrls (array, required, default: []): Paste one or more LinkedIn post URLs or activity/ugcPost IDs. Each post is scraped separately.
• resultLimitPerPost (integer, optional, default: 10): How many comments to collect per post. Minimum 1, maximum 500.
• postedLimit (string, optional, default: "any"): Filter by posted time. One of: "any", "24h", "week", "month", "3months", "6months", "year".
• scrapeReplies (boolean, optional, default: false): When true, nested replies under each comment are included.
• profileScraperMode (string, optional, default: "short"): "short" = fewer fields; "full" = include author profile picture and headline.
• liAt (string, optional, default: ""): LinkedIn Cookie (li_at). Recommended. Without it, LinkedIn requires authentication to fetch comments and the run will likely fail.
• proxyConfiguration (object, optional, prefill: {"useApifyProxy": false}): Optional proxy settings. Leave default for direct connection; the actor can escalate proxies automatically if needed.

Example JSON output

{
  "postUrl": "https://www.linkedin.com/feed/update/urn:li:activity:7289521182721093633/",
  "comment_id": "706543210987654321",
  "text": "Great update — thanks for sharing!",
  "posted_at": {
    "timestamp": 1713871200000,
    "date": "2024-04-23 15:20:00",
    "relative": "2d"
  },
  "is_edited": false,
  "is_pinned": false,
  "comment_url": "https://www.linkedin.com/feed/update/urn:li:activity:7289521182721093633?commentUrn=urn:li:fsd_comment:(706543210987654321,...)",
  "author": {
    "name": "Jane Doe",
    "headline": "Product Manager at Acme",
    "profile_url": "https://www.linkedin.com/in/janedoe/",
    "profile_picture": "https://media.licdn.com/dms/image/..."
  },
  "stats": {
    "total_reactions": 12,
    "reactions": {
      "like": 9,
      "appreciation": 1,
      "empathy": 0,
      "interest": 2,
      "praise": 0
    },
    "comments": 2
  },
  "replies": [
    {
      "comment_id": "706543210987654399",
      "text": "Agree — looking forward to the next release.",
      "posted_at": {
        "timestamp": 1713874800000,
        "date": "2024-04-23 16:20:00",
        "relative": "2d"
      },
      "is_edited": false,
      "is_pinned": false,
      "comment_url": "https://www.linkedin.com/feed/update/urn:li:activity:7289521182721093633?commentUrn=urn:li:fsd_comment:(706543210987654399,...)",
      "author": {
        "name": "John Smith",
        "headline": "Engineer",
        "profile_url": "https://www.linkedin.com/in/johnsmith/",
        "profile_picture": null
      },
      "stats": {
        "total_reactions": 3,
        "reactions": {
          "like": 3,
          "appreciation": 0,
          "empathy": 0,
          "interest": 0,
          "praise": 0
        },
        "comments": 0
      },
      "replies": [],
      "post_input": "7289521182721093633",
      "totalComments": 145
    }
  ],
  "post_input": "7289521182721093633",
  "totalComments": 145
}

Notes:

• Fields author.headline and author.profile_picture are null when profileScraperMode is "short".
• replies is empty when scrapeReplies is false.
• stats.reactions is an object keyed by reaction types such as like, appreciation, empathy, interest, and praise.

FAQ

Do I need to log in or provide cookies?

✅ Yes, in practice you should provide your li_at cookie. The actor logs a warning that LinkedIn requires authentication to fetch comments and the run will likely fail without li_at. Paste your li_at from your browser into the liAt field for reliable results.

How many comments per post can I collect?

You control this via resultLimitPerPost, with a maximum of 500 per post. The actor paginates through comments and stops when it reaches your limit or the thread ends.

Can it extract replies to comments?

✅ Yes. Enable scrapeReplies to include nested replies in the replies array for each comment. When off, only top-level comments are returned.

Which reaction types are included?

The stats.reactions object aggregates counts for reaction types such as like, appreciation, empathy, interest, and praise. stats.total_reactions is the sum across types.

Can I scrape multiple posts in a single run?

✅ Yes. Provide an array in startUrls with multiple LinkedIn post URLs or IDs. The actor processes each post separately and streams results to the dataset.

How do I filter by recent comments only?

Use postedLimit. Choose from "any", "24h", "week", "month", "3months", "6months", or "year". The actor filters both comments and replies against this cutoff.

How do I export the results?

Open the run’s Dataset in Apify to preview and download. You can export LinkedIn post comments to CSV, JSON, or Excel, or pull data programmatically via the Apify API for automation.

Does it support Python or API-based automation?

💻 Yes. Access your dataset via the Apify API and integrate with your LinkedIn comment scraper Python scripts or no-code tools for automated LinkedIn post comments download and analysis.

What proxy settings should I use?

You can leave proxyConfiguration at defaults for a direct connection or enable Apify Proxy. The actor can automatically escalate to datacenter and then residential proxies if rate-limited.

Closing CTA / Final thoughts

The LinkedIn Post Comments Scraper is built to extract structured comment threads — with reactions and author context — from LinkedIn posts at scale. By combining bulk inputs, reply capture, date filtering, and resilient networking, it helps marketers, developers, analysts, and researchers automate LinkedIn comments data extraction with confidence. Export results from the dataset, integrate via the Apify API, or embed in your LinkedIn comments scraping script for end-to-end automation. Start extracting smarter, richer engagement insights from your LinkedIn posts today.

What are other Linkedin scraping tools?

If you want to scrape specific Linkedin data, you can use any of the dedicated scrapers below for faster and more targeted results.

Scraper Name	Scraper Name
LinkedIn Ads Scraper	Linkedin Phone Number Scraper
Linkedin B2b Email Scraper	LinkedIn Post Comments Engagements Scraper
Linkedin B2b Lead Scraper	Linkedin Post Reactions Scraper
Linkedin B2b Phone Number Scraper	LinkedIn Post Scraper
LinkedIn Company About Scraper	LinkedIn Posts URL (Profile)
LinkedIn Company Employees Scraper	Linkedin Profile And Company Posts Scraper
Linkedin Company Employees Scraper	Linkedin Profile Email Scraper
Linkedin Company Profile Scraper	Linkedin Profile Lead Scraper
LinkedIn Company Scraper	Linkedin Profile Phone Number Scraper
Linkedin Email Scraper	Linkedin Profile Post Scraper
Linkedin Jobs Scraper	LinkedIn Profile Scraper
Linkedin Lead Scraper	LinkedIn Search Jobs Scraper
Linkedin Open Profile Status