# Linkedin Post Reactions Scraper (No Cookie) (`datadoping/linkedin-post-reactions-scraper-no-cookie`) Actor

For $1.2 per 1000 reactions. Scraper Likes, Praise, Empathy, Appreciation and Interest of any Linkedin Post using URL or URN.

Note: Freemium users are limited to scrape only 4 posts and 100 reactions per run (12 posts in total)

- **URL**: https://apify.com/datadoping/linkedin-post-reactions-scraper-no-cookie.md
- **Developed by:** [Data Doping](https://apify.com/datadoping) (community)
- **Categories:** Automation, Social media, Lead generation
- **Stats:** 394 total users, 27 monthly users, 100.0% runs succeeded, 10 bookmarks
- **User rating**: 5.00 out of 5 stars

## Pricing

from $1.20 / 1,000 reactions

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 Reactions Scraper?

LinkedIn Post Reactions Scraper automates the process of gathering publicly available reactions from LinkedIn posts. Simply provide one or more LinkedIn post URLs and specify the reaction type filter, and the scraper will fetch comprehensive reaction details such as:

**Reaction Content:**

- Reaction types (Like, Praise, Empathy, Appreciation, Interest)
- Reactor information (name, profile picture, profile URL)
- Reaction timestamps and metadata

**Engagement Data:**

- Individual reaction types and counts
- Reactor profile details and professional information
- Reaction distribution across different types

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

💡 Why Scrape LinkedIn Post Reactions?

LinkedIn reactions provide valuable insights for professionals and businesses:

🔎 **Engagement Analysis**: Analyze which posts get the most reactions and understand audience engagement patterns.

🧠 **Audience Insights**: Understand how your audience responds to different types of content and identify engaged followers.

📈 **Social Listening**: Monitor how your content or industry topics are being received across LinkedIn.

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

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

🎯 **Brand Monitoring**: Track how your brand content is being received and reacted to.

⚖️ Is it Legal to Scrape LinkedIn Reactions?

We prioritize ethical scraping practices.

This scraper only extracts information that users have chosen to display publicly on their LinkedIn posts. 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 post URLs
   - URL: `https://www.linkedin.com/posts/linkedin_add-job-preferences-activity-7208418413906464769-P2hn`

2. **Reaction Type Filter**: Choose specific reaction type or "ALL" to get all reactions
   - Options: ALL, LIKE, PRAISE, EMPATHY, APPRECIATION, INTEREST

3. **Reactions Limit**: Set maximum number of reactions to scrape per post (minimum 10, default 100)

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

5. **Freemium Limits**: Free users are limited to 4 posts per run with maximum 100 reactions per post (12 posts in total)

📊 Output Data Structure

The scraper returns comprehensive reaction data including:

- **Reaction Content**: Reaction types and metadata
- **Reactor Info**: Name, profile picture, and profile URL
- **Engagement**: Reaction distribution 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 posts per run with maximum 100 reactions per post (12 posts in total)
- Premium users have no limits
- Minimum 10 reactions per post (configurable maximum)

### Input Schema

```json
{
  "post_urls": [
    "https://www.linkedin.com/posts/linkedin_add-job-preferences-activity-7208418413906464769-P2hn",
    "https://www.linkedin.com/posts/0987654321"
  ],
  "reaction_type": "ALL",
  "max_reactions": 100
}
```

# Actor input Schema

## `post_urls` (type: `array`):

List of LinkedIn post URLs to scrape reactions from. Enter one URL per line.

## `reaction_type` (type: `string`):

Type of reactions to scrape. Choose 'ALL' to get all reaction types, or select a specific reaction type.

## `max_reactions` (type: `integer`):

Maximum number of reactions to scrape from each post. Minimum is 10 reactions. Default is 100.

## Actor input object example

```json
{
  "post_urls": [
    "https://www.linkedin.com/posts/linkedin_add-job-preferences-activity-7208418413906464769-P2hn"
  ],
  "reaction_type": "ALL",
  "max_reactions": 100
}
```

# 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 = {
    "post_urls": [
        "https://www.linkedin.com/posts/linkedin_add-job-preferences-activity-7208418413906464769-P2hn"
    ],
    "reaction_type": "ALL",
    "max_reactions": 100
};

// Run the Actor and wait for it to finish
const run = await client.actor("datadoping/linkedin-post-reactions-scraper-no-cookie").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 = {
    "post_urls": ["https://www.linkedin.com/posts/linkedin_add-job-preferences-activity-7208418413906464769-P2hn"],
    "reaction_type": "ALL",
    "max_reactions": 100,
}

# Run the Actor and wait for it to finish
run = client.actor("datadoping/linkedin-post-reactions-scraper-no-cookie").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 '{
  "post_urls": [
    "https://www.linkedin.com/posts/linkedin_add-job-preferences-activity-7208418413906464769-P2hn"
  ],
  "reaction_type": "ALL",
  "max_reactions": 100
}' |
apify call datadoping/linkedin-post-reactions-scraper-no-cookie --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,datadoping/linkedin-post-reactions-scraper-no-cookie"
        }
    }
}
```

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/hN8x2I2pW8aAoTs55/builds/TjbHAJi9SgP5KNPWo/openapi.json