Post Comments Engagements Scraper Linkedin

Post Comments Engagements Scraper Linkedin

Post Comments Engagements Scraper Linkedin is a production-ready LinkedIn post comments scraper that extracts structured comment threads, reactions, and author details from LinkedIn posts at scale. It solves the manual, error-prone task of trying to scrape LinkedIn post comments by automating threaded comment extraction with reaction breakdowns and clean author metadata — ideal for marketers, developers, data analysts, and researchers. As a LinkedIn engagement scraper and LinkedIn post comments scraper, it helps you analyze discussions, export LinkedIn comments CSV/JSON, and power social listening or engagement analytics workflows at scale.

What data / output can you get?

This actor pushes one dataset item per post, containing an array of comment objects. Each comment includes author metadata, timestamps, reaction counts (with per-type breakdown), and nested replies.

Data field	Description	Example value
comment_id	Unique LinkedIn comment identifier (parsed from entity URN)	"1234567890"
text	Comment text, sanitized for whitespace	"Great insights, thanks for sharing!"
posted_at.timestamp	Milliseconds since epoch	1712581234567
posted_at.date	Local datetime string	"2026-04-08 09:20:34"
posted_at.relative	Relative time shorthand	"2h"
is_edited	Whether the comment was edited	false
is_pinned	Whether the comment is pinned	false
comment_url	Permalink to the comment (built if missing)	"https://www.linkedin.com/feed/update/urn%3Ali%3Aactivity%3A7289521182721093633?commentUrn=urn%3Ali%3Afsd_comment%3A%281234567890%2C...)"
author.name	Comment author name	"Jane Doe"
author.headline	Author headline/subtitle as shown by LinkedIn	"Marketing Manager at Acme"
author.profile_url	Author profile link	"https://www.linkedin.com/in/janedoe"
author.profile_picture	Best-resolution avatar URL (when available)	"https://media.licdn.com/.../profile.jpg"
stats.total_reactions	Total reactions count for the comment	5
stats.reactions	Map of reaction counts by type	{"like":4,"appreciation":1,"empathy":0,"interest":0,"praise":0}
stats.comments	Number of direct reply comments	1
replies	Nested reply comments (same structure), up to depth 2	[...]
post_input	Post ID parsed from the comment URN	"7289521182721093633"
totalComments	Total root-level comments reported for the post	47
postUrl	The post URL this comment belongs to (added to top-level comments)	"https://www.linkedin.com/feed/update/urn:li:activity:7289521182721093633/"

Notes:

• Output is available in Apify as JSON by default, with export to CSV or Excel available from the dataset viewer.
• Nested replies are included up to two levels deep. Reaction breakdown includes like, appreciation, empathy, interest, and praise types where present.

Key features

• 🔁 Batch scraping of multiple posts

  • Add multiple post URLs or numeric activity IDs and process them in one run. Perfect when you need to automate LinkedIn comments extraction across campaigns or content series.

• 🧵 Threaded replies with engagement stats

  • Extracts top-level comments and nested replies (up to depth 2) with posted timestamps (absolute and relative), pinned/edited flags, and per-type reactions. Ideal for a LinkedIn comment thread scraper and LinkedIn reactions and comments scraper use case.

• 🔃 Sort order control

  • Choose REVERSE_CHRONOLOGICAL (newest first) or RELEVANCE to align with your LinkedIn post engagement analytics tool needs.

• 🎯 Per-post limits for speed & scale

  • Control max comments per post with resultLimitPerPost (1–500). Useful to sample or to fully export LinkedIn post comments.

• 🛡️ Resilient proxy fallback

  • Automatically escalates from no proxy → Apify datacenter → residential proxy with retries on blocks/rate limits. Built for production-grade stability as a LinkedIn post engagement data extractor.

• 👩‍💻 Developer friendly

  • Clean, structured JSON designed for pipelines and easy to integrate with Python scripts or APIs. Great for a “LinkedIn comment scraper Python script” workflow.

• 💾 Export-ready

  • Download your dataset in JSON, CSV, or Excel from Apify — fast path to export LinkedIn comments CSV for analysis.

• ⚙️ Authenticated sessions supported

  • Optional LinkedIn session cookie (li_at) for logged-in access. The actor logs a clear warning when li_at is missing because LinkedIn requires authentication to fetch comments.

How to use Post Comments Engagements Scraper Linkedin - step by step

1. Create or log in to your Apify account.
2. Open the actor Post Comments Engagements Scraper Linkedin in the Apify Console.
3. Add your input:
   • startUrls: Paste LinkedIn post URLs or numeric activity IDs. Accepts:
     • Full URL, e.g., https://www.linkedin.com/feed/update/urn:li:activity:7289521182721093633/
     • Numeric ID, e.g., 7289521182721093633
4. Provide authentication (recommended):
   • liAt: Paste your LinkedIn session cookie value to enable authenticated access. Without li_at, LinkedIn requires authentication to fetch comments and the run will likely fail.
5. Configure options:
   • sortOrder: REVERSE_CHRONOLOGICAL or RELEVANCE.
   • resultLimitPerPost: Set max comments per post (1–500, default 100).
   • proxyConfiguration: Optional. If not set, the actor will escalate proxies automatically when blocked.
6. Start the run:
   • Click Start. The log will show page-by-page progress (e.g., “+X comments (total: Y)”). The actor applies short random delays and backoff on 429 to reduce rate limits.
7. Review & export results:
   • Go to the Run’s Dataset. Download as JSON, CSV, or Excel.

Pro Tip: Integrate the dataset with your analytics or CRM pipeline using Apify’s dataset exports to automate LinkedIn post commenters export and build engagement dashboards.

Use cases

Use case	Description
Marketing engagement analysis	Measure comment volume, reaction breakdowns, and reply depth to benchmark campaign performance using a LinkedIn post engagement analytics tool.
Social listening & VOC	Extract and analyze comment threads to track sentiment and themes with a LinkedIn comments extractor tool.
Competitive monitoring	Scrape LinkedIn post comments from competitors’ posts to compare audience reactions and discussion drivers.
Content optimization	Identify which topics spark replies/reactions and replicate high-engagement patterns.
Research & academic studies	Collect structured discourse data for studies on professional communities and industry narratives.
API/data pipeline integration	Use the structured JSON output with your existing ETL or a LinkedIn comment scraper Python script for downstream analytics.
Customer success & feedback	Mine product feedback in comments to prioritize fixes and feature requests efficiently.

Why choose Post Comments Engagements Scraper Linkedin?

Built for precision, stability, and automation, this actor focuses on clean, structured comment extraction with reaction analytics for decision-ready data.

• ✅ Accurate, structured threads: Captures author profiles, timestamps, and per-type reactions with nested replies.
• 📈 Scales with your workflow: Batch multiple posts with per-post limits up to 500 comments.
• 👩‍💻 Developer access: Clean JSON output that plugs into APIs, Python, and analytics tools.
• 🛡️ Reliable at scale: Automatic proxy fallback (none → datacenter → residential) with backoff for blocks and 429s.
• 💾 Export flexibility: Download JSON/CSV/Excel from the Apify dataset UI, perfect to export LinkedIn comments CSV for reporting.
• 🔐 Auth-ready: Supports authenticated runs via li_at when needed by LinkedIn.

Unlike fragile browser extensions or unstable bots, this production-grade LinkedIn data scraper for post comments emphasizes stability, clean output, and resilient network strategy.

Is it legal / ethical to use Post Comments Engagements Scraper Linkedin?

Yes — when done responsibly. You should only collect data in compliance with LinkedIn’s terms and applicable laws.

Guidelines:

• Access data you’re permitted to view and use; respect platform terms.
• Avoid collecting or using personal data in ways that violate privacy regulations (e.g., GDPR, CCPA).
• Use results for analysis and research, not spam or abuse.
• Consult your legal team for edge cases in your jurisdiction.

Input parameters & output format

Example input

{
  "startUrls": [
    "https://www.linkedin.com/feed/update/urn:li:activity:7289521182721093633/",
    "7289521182721093633"
  ],
  "liAt": "AQED...your_li_at_cookie...",
  "sortOrder": "REVERSE_CHRONOLOGICAL",
  "resultLimitPerPost": 100,
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}

Input parameters

• startUrls (array, required): Enter one or more LinkedIn post URLs or numeric activity IDs. One URL or ID per line. Default: none.
• liAt (string, optional): LinkedIn session cookie for authenticated scraping. Default: empty.
• pageNumber (integer, optional): The page number for comment pagination (1-based). Default: 1.
• sortOrder (string, optional): Choose REVERSE_CHRONOLOGICAL (default) or RELEVANCE.
• resultLimitPerPost (integer, optional): Max comments per post (1–500). Default: 100.
• proxyConfiguration (object, optional): Configure Apify proxy. By default, the actor starts without a proxy and falls back automatically when blocked.

Example output (one dataset item per post; each item is an array of comment objects)

[
  {
    "comment_id": "1234567890",
    "text": "Great insights, thanks for sharing!",
    "posted_at": {
      "timestamp": 1712581234567,
      "date": "2026-04-08 09:20:34",
      "relative": "2h"
    },
    "is_edited": false,
    "is_pinned": false,
    "comment_url": "https://www.linkedin.com/feed/update/urn%3Ali%3Aactivity%3A7289521182721093633?commentUrn=urn%3Ali%3Afsd_comment%3A%281234567890%2C...%29",
    "author": {
      "name": "Jane Doe",
      "headline": "Marketing Manager at Acme",
      "profile_url": "https://www.linkedin.com/in/janedoe",
      "profile_picture": "https://media.licdn.com/.../profile.jpg"
    },
    "stats": {
      "total_reactions": 5,
      "reactions": {
        "like": 4,
        "appreciation": 1,
        "empathy": 0,
        "interest": 0,
        "praise": 0
      },
      "comments": 1
    },
    "replies": [
      {
        "comment_id": "1234567891",
        "text": "Totally agree!",
        "posted_at": {
          "timestamp": 1712585234567,
          "date": "2026-04-08 10:27:14",
          "relative": "1h"
        },
        "is_edited": false,
        "is_pinned": false,
        "comment_url": "https://www.linkedin.com/feed/update/urn%3Ali%3Aactivity%3A7289521182721093633?commentUrn=urn%3Ali%3Afsd_comment%3A%281234567891%2C...%29",
        "author": {
          "name": "John Smith",
          "headline": "Founder",
          "profile_url": "https://www.linkedin.com/in/johnsmith",
          "profile_picture": "https://media.licdn.com/.../avatar.jpg"
        },
        "stats": {
          "total_reactions": 2,
          "reactions": {
            "like": 2,
            "appreciation": 0,
            "empathy": 0,
            "interest": 0,
            "praise": 0
          },
          "comments": 0
        },
        "replies": [],
        "post_input": "7289521182721093633",
        "totalComments": 47
      }
    ],
    "post_input": "7289521182721093633",
    "totalComments": 47,
    "postUrl": "https://www.linkedin.com/feed/update/urn:li:activity:7289521182721093633/"
  },
  {
    "comment_id": "1234567892",
    "text": "Helpful breakdown.",
    "posted_at": {
      "timestamp": 1712582234567,
      "date": "2026-04-08 09:37:14",
      "relative": "2h"
    },
    "is_edited": false,
    "is_pinned": false,
    "comment_url": "https://www.linkedin.com/feed/update/urn%3Ali%3Aactivity%3A7289521182721093633?commentUrn=urn%3Ali%3Afsd_comment%3A%281234567892%2C...%29",
    "author": {
      "name": "Alex Lee",
      "headline": "Data Analyst",
      "profile_url": "https://www.linkedin.com/in/alexlee",
      "profile_picture": "https://media.licdn.com/.../photo.jpg"
    },
    "stats": {
      "total_reactions": 3,
      "reactions": {
        "like": 3,
        "appreciation": 0,
        "empathy": 0,
        "interest": 0,
        "praise": 0
      },
      "comments": 0
    },
    "replies": [],
    "post_input": "7289521182721093633",
    "totalComments": 47,
    "postUrl": "https://www.linkedin.com/feed/update/urn:li:activity:7289521182721093633/"
  }
]

Notes:

• Each dataset item is an array of top-level comments for a single post. The postUrl field is added to each top-level comment. Nested replies follow the same structure but do not include postUrl.
• Some fields may be null/empty when not provided by LinkedIn (e.g., profile_picture).

FAQ

Do I need to provide a LinkedIn login or cookie?

Yes, you should provide your li_at cookie for authenticated access. The actor logs a warning when li_at is missing because LinkedIn requires authentication to fetch comments, and the run will likely fail without it.

How many comments can I extract per post?

Up to 500 per post via resultLimitPerPost. You can set any value between 1 and 500 depending on whether you want a sample or full thread.

Can it scrape replies as well as top-level comments?

Yes. It extracts threaded replies (nested) up to two levels deep, making it a reliable LinkedIn comment thread scraper.

Does it include reaction counts and types?

Yes. Each comment includes stats.total_reactions and a stats.reactions map with per-type counts (like, appreciation, empathy, interest, praise), plus the number of direct replies.

Can I sort by most recent or most relevant?

Yes. Use sortOrder with REVERSE_CHRONOLOGICAL (default) or RELEVANCE to control how comments are fetched.

Can I export the data to CSV or JSON?

Yes. Results are stored in an Apify dataset. You can download as JSON, CSV, or Excel directly from the dataset UI, enabling you to export LinkedIn comments CSV effortlessly.

How does it handle LinkedIn blocks or rate limits?

It starts without a proxy and automatically falls back to Apify datacenter and then residential proxy with retries if blocked or rate-limited. It also uses short random delays and exponential backoff on HTTP 429 responses.

Can I use it with my existing Python or API workflow?

Yes. The structured JSON output is ideal for pipelines and scripting, including a LinkedIn comment scraper Python script use case or integrating with your data platform via Apify’s dataset exports.

Closing CTA / Final thoughts

Post Comments Engagements Scraper Linkedin is built to extract structured LinkedIn post comments and engagement data at scale. With threaded replies, per-type reaction counts, robust proxy fallback, and clean JSON output, it’s ready for marketers, developers, data analysts, and researchers. Export to CSV/JSON/Excel, control sort order and limits, and integrate with your automation pipeline or Python scripts. Start extracting smarter LinkedIn engagement insights today and turn comment threads into actionable analytics.