# Linkedin User Posts Spider (`getdataforme/linkedin-user-posts-spider`) Actor
The Linkedin User Posts Spider efficiently extracts comprehensive data from LinkedIn user posts, including text and metadata. It offers high-quality, scalable data collection with a user-friendly interface, supporting multiple output formats like JSON, CSV, and Excel....
- **URL**: https://apify.com/getdataforme/linkedin-user-posts-spider.md
- **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community)
- **Categories:** AI, Automation, E-commerce
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet
## Pricing
from $9.00 / 1,000 results
This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
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.
Actors are written with capital "A".
## 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.
The best way to integrate Actors is as follows.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).
For usage examples, see the [API](#api) section below.
For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).
# README
---
## Linkedin User Posts Spider
### Introduction
The Linkedin User Posts Spider is a powerful tool designed to extract valuable data from LinkedIn user posts. It enables users to gather insights and information efficiently, making it an essential asset for businesses and researchers looking to leverage social media analytics.
### Features
- **Comprehensive Data Extraction**: Captures detailed post content, including text, images, and metadata.
- **High-Quality Data**: Ensures reliable and accurate data collection from LinkedIn profiles.
- **Efficient Performance**: Optimized for speed and scalability, handling large volumes of data seamlessly.
- **User-Friendly Interface**: Easy to configure with clear input parameters and straightforward operation.
- **Versatile Output Formats**: Supports multiple export formats such as JSON, CSV, and Excel for flexible data use.
### Input Parameters
| Parameter | Type | Required | Description | Example |
|----------------|--------|----------|-----------------------------------------------------------------------------|------------------------------|
| `user_id` | String | Yes | The LinkedIn user ID whose posts are to be extracted. | `"qRnUvNQIk3ySF2epZ"` |
| `run_id` | String | No | Unique identifier for the run, used for tracking purposes. | `"eClilVgGZ0hxA0E6H"` |
### Example Usage
#### Input JSON
```json
{
"user_id": "qRnUvNQIk3ySF2epZ",
"run_id": "eClilVgGZ0hxA0E6H"
}
````
#### Output JSON
```json
{
"items": [
{
"commentary": {
"text": {
"text": "Day 2 of our Hackathon & Project Showcase reminded me why events like this matter, collaboration, learning, and pushing limits together. As the emcee, seeing everyone work with such dedication was truly inspiring. One more day to go! \ud83e\ude77 #learn #lead #grow",
"textDirection": "FIRST_STRONG"
}
},
"actor_id": "qRnUvNQIk3ySF2epZ",
"run_id": "eClilVgGZ0hxA0E6H"
}
]
}
```
### Use Cases
- **Market Research and Analysis**: Gain insights into industry trends and competitor strategies.
- **Competitive Intelligence**: Monitor competitors' activities and public engagements on LinkedIn.
- **Price Monitoring**: Track pricing changes and promotions in specific industries.
- **Content Aggregation**: Collect and analyze content for research or marketing purposes.
- **Academic Research**: Use data for studies related to social media behavior and influence.
- **Business Automation**: Automate the collection of relevant business intelligence from LinkedIn.
### Installation and Usage
1. Search for "Linkedin User Posts Spider" in the Apify Store.
2. Click "Try for free" or "Run".
3. Configure input parameters as needed.
4. Click "Start" to begin extraction.
5. Monitor progress in the log.
6. Export results in your preferred format (JSON, CSV, Excel).
### Output Format
The output data is structured with key fields such as `commentary`, `actor_id`, and `run_id`. Each entry contains detailed information about LinkedIn posts, including text content, metadata, and user details.
### Support
For custom/simplified outputs or bug reports, please contact:
- Email: support@getdataforme.com
- Subject line: "custom support"
- Contact form:
We're here to help you get the most out of this Actor!
***
# Actor input Schema
## `LiAt` (type: `string`):
The li at for the spider.
## `JsessionId` (type: `string`):
The jsession id for the spider.
## `ProfileUrls` (type: `array`):
The profile urls for the spider.
## `item_limit` (type: `integer`):
Maximum items to scrape per actor run. Set to 0 for no limit.
## Actor input object example
```json
{
"LiAt": "AQEDAWlbK7QC55bOAAABno0-Qv0AAAGesUrG_U4AdYv2_FmMFtZJQwzzlfNd5MEUqsobcQJWUTso17FZIHIsX5IuDGBfnrU3AzAku5aFTKUM_FuvZeFYrmv6xUt1XCehq3Nop6TnTYxO_zJaLOSUZaUn",
"JsessionId": "ajax:8824102922978191655",
"ProfileUrls": [
"https://np.linkedin.com/in/jerusha-lamsal"
],
"item_limit": 10
}
```
# Actor output Schema
## `results` (type: `string`):
Scraped data items from dataset
# 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 '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {};
// Run the Actor and wait for it to finish
const run = await client.actor("getdataforme/linkedin-user-posts-spider").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 '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = {}
# Run the Actor and wait for it to finish
run = client.actor("getdataforme/linkedin-user-posts-spider").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{}' |
apify call getdataforme/linkedin-user-posts-spider --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=getdataforme/linkedin-user-posts-spider",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Linkedin User Posts Spider",
"description": "The Linkedin User Posts Spider efficiently extracts comprehensive data from LinkedIn user posts, including text and metadata. It offers high-quality, scalable data collection with a user-friendly interface, supporting multiple output formats like JSON, CSV, and Excel....",
"version": "0.0",
"x-build-id": "JwGIMfMcESJ9Q9fcp"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/getdataforme~linkedin-user-posts-spider/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-getdataforme-linkedin-user-posts-spider",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/getdataforme~linkedin-user-posts-spider/runs": {
"post": {
"operationId": "runs-sync-getdataforme-linkedin-user-posts-spider",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/getdataforme~linkedin-user-posts-spider/run-sync": {
"post": {
"operationId": "run-sync-getdataforme-linkedin-user-posts-spider",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"properties": {
"LiAt": {
"title": "Li At",
"type": "string",
"description": "The li at for the spider.",
"default": "AQEDAWlbK7QC55bOAAABno0-Qv0AAAGesUrG_U4AdYv2_FmMFtZJQwzzlfNd5MEUqsobcQJWUTso17FZIHIsX5IuDGBfnrU3AzAku5aFTKUM_FuvZeFYrmv6xUt1XCehq3Nop6TnTYxO_zJaLOSUZaUn"
},
"JsessionId": {
"title": "Jsession Id",
"type": "string",
"description": "The jsession id for the spider.",
"default": "ajax:8824102922978191655"
},
"ProfileUrls": {
"title": "Profile Urls",
"minItems": 1,
"type": "array",
"description": "The profile urls for the spider.",
"default": [
"https://np.linkedin.com/in/jerusha-lamsal"
],
"items": {
"type": "string"
}
},
"item_limit": {
"title": "Item limit",
"minimum": 0,
"type": "integer",
"description": "Maximum items to scrape per actor run. Set to 0 for no limit.",
"default": 10
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```