# Linkedin Post Comments Scraper (No Cookie) (`datadoping/linkedin-post-comments-scraper`) Actor

For just $1.2 per 1,000 comments. Scrape all LinkedIn post related data including comments, stats, reactions, replies and media attachments
Note: If you're on free tier you can only scrape 4 posts and 100 comments per post per run (12 posts in total)

- **URL**: https://apify.com/datadoping/linkedin-post-comments-scraper.md
- **Developed by:** [Data Doping](https://apify.com/datadoping) (community)
- **Categories:** Social media, Developer tools, Automation
- **Features:** Limited permissions
- **Stats:** 568 total users, 74 monthly users, 100.0% runs succeeded, 24 bookmarks
- **User rating**: 4.78 out of 5 stars

## Pricing

from $1.20 / 1,000 comments

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

🔍 What is LinkedIn Post Comments Scraper?

LinkedIn Post Comments Scraper automates the process of gathering publicly available comments from LinkedIn ppost. Simply provide one or more LinkedIn post URLs or URNs, and the scraper will fetch comprehensive comment details such as:

**Comment Content:**

- Comment text and content
- Comment URLs and timestamps
- Commenter information (name, headline, profile picture)
- Comment statistics (likes, replies, reactions)

**Engagement Data:**

- Total reaction counts
- Individual reaction types (like, appreciation, empathy, interest, praise)
- Reply counts
- Comment timestamps (both absolute and relative)
- Pinned comment status

**Pagination Support:**

- Configurable maximum comments per profile

All extracted data is limited strictly to public information—nothing private or sensitive is collected.

💡 Why Scrape LinkedIn Profile Comments?

LinkedIn comments provide valuable insights for professionals and businesses:

🔎 **Engagement Analysis**: Analyze what types of comments generate the most engagement and responses.

🧠 **Community Insights**: Understand community sentiment and discussion patterns around specific topics.

📈 **Social Listening**: Monitor conversations and reactions to industry content and trends.

🤝 **Relationship Building**: Identify active community members and potential networking opportunities.

📊 **Content Strategy**: Research what content generates meaningful discussions and engagement.

🎯 **Brand Monitoring**: Track mentions and sentiment around your brand or industry topics.

⚖️ Is it Legal to Scrape LinkedIn Comments?

We prioritize ethical scraping practices.

This scraper only extracts information that users have chosen to display publicly on their LinkedIn profiles. It does not access private data, restricted content, or information behind login walls. All data collection follows LinkedIn's robots.txt guidelines and respects rate limits.

🚀 How to Use

1. **Input Format**: Provide LinkedIn usernames or full profile URLs
   - Username: `123456789`
   - URL: `https://linkedin.com/post/example-post`

2. **Comments Limit**: Set maximum number of comments to scrape per profile (minimum 10)

3. **Error Handling**: Invalid post URNs, or malformed URLs will be logged as errors in the dataset

4. **Freemium Limits**: Free users are limited to 4 profiles per run with maximum 100 comments per profile

📊 Output Data Structure

The scraper returns comprehensive comment data including:

- **Comment Content**: Text, URLs, timestamps, and metadata
- **Author Info**: Name, headline, profile picture, and profile URL
- **Engagement**: Reaction counts, replies, and interaction data

🔧 Technical Features

- **Error Handling**: Comprehensive error reporting for failed requests
- **Resume Capability**: Can resume interrupted scraping sessions
- **Concurrent Processing**: Multiple workers for efficient data collection

⚠️ Important Notes

- Only LinkedIn posts are supported (other URLs will return errors)
- All data is publicly available information only
- Free tier limited to 4 profiles per run with maximum 100 comments per profile
- Premium users have no limits
- Minimum 10 comments per profile (configurable maximum)

# Actor input Schema

## `posts` (type: `array`):

List of LinkedIn post URLs or URNs to scrape comments from. Enter one URL/URN per line.

## `sort_by` (type: `string`):

Choose how to sort the comments before scraping.

## `max_comments` (type: `integer`):

Maximum number of comments to scrape from each post. Minimum is 10 comments. Default is 10.

## Actor input object example

```json
{
  "posts": [
    "https://www.linkedin.com/posts/laurenfogel_eventmarketing-linkedinads-b2bmarketing-activity-7325159387629531137-NUF5"
  ],
  "sort_by": "Most relevant",
  "max_comments": 50
}
```

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "posts": [
        "https://www.linkedin.com/posts/laurenfogel_eventmarketing-linkedinads-b2bmarketing-activity-7325159387629531137-NUF5"
    ],
    "sort_by": "Most relevant",
    "max_comments": 50
};

// Run the Actor and wait for it to finish
const run = await client.actor("datadoping/linkedin-post-comments-scraper").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "posts": ["https://www.linkedin.com/posts/laurenfogel_eventmarketing-linkedinads-b2bmarketing-activity-7325159387629531137-NUF5"],
    "sort_by": "Most relevant",
    "max_comments": 50,
}

# Run the Actor and wait for it to finish
run = client.actor("datadoping/linkedin-post-comments-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "posts": [
    "https://www.linkedin.com/posts/laurenfogel_eventmarketing-linkedinads-b2bmarketing-activity-7325159387629531137-NUF5"
  ],
  "sort_by": "Most relevant",
  "max_comments": 50
}' |
apify call datadoping/linkedin-post-comments-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,datadoping/linkedin-post-comments-scraper"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/7SAmV0nweGtJh8lRa/builds/PhZOMaL4jo8Dm6NVk/openapi.json