# Linkedin Posts Reactions Spider (`getdataforme/linkedin-posts-reactions-spider`) Actor The LinkedIn Posts Reactions Spider efficiently scrapes LinkedIn posts, capturing detailed data like reactions and comments.... - **URL**: https://apify.com/getdataforme/linkedin-posts-reactions-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 Posts Reactions Spider ### Introduction The Linkedin Posts Reactions Spider is a powerful tool designed to scrape LinkedIn posts and their associated reactions. It efficiently extracts detailed information such as post content, author details, reaction counts, and comments, providing valuable insights for various applications. ### Features - **Comprehensive Data Extraction**: Captures all relevant data from LinkedIn posts, including reactions and comments. - **Customizable Input Parameters**: Allows users to specify URLs and set item limits for tailored scraping sessions. - **High-Quality Output**: Delivers structured JSON output with detailed post information. - **Scalable Performance**: Supports large-scale data extraction with adjustable item limits. - **User-Friendly Interface**: Easy setup and execution through the Apify Store. ### Input Parameters | Parameter | Type | Required | Description | Example | |-------------|---------|----------|-----------------------------------------------------------------------------|----------------------------------------------------------------------------------------------| | Urls | array | Yes | The URLs for the spider to scrape. | `["https://www.linkedin.com/posts/sharonpeake_networking-genderequity-activity-7254017063415910401-pcqb"]` | | item_limit | integer | No | Maximum items to scrape per actor run. Set to 0 for no limit. | `10` | ### Example Usage #### Input JSON ```json { "Urls": [ "https://www.linkedin.com/posts/sharonpeake_the-saying-its-not-what-you-know-but-who-activity-7254017063415910401-eC--?utm_source=share&utm_medium=guest_desktop&utm_content=keyword-landing-page&utm_campaign=copy" ], "item_limit": 10 } ```` #### Output JSON ```json [ { "url": "https://www.linkedin.com/posts/sharonpeake_the-saying-its-not-what-you-know-but-who-activity-7254017063415910401-eC--?utm_source=share&utm_medium=guest_desktop&utm_content=keyword-landing-page&utm_campaign=copy", "post_id": "https://www.linkedin.com/posts/sharonpeake_networking-genderequity-threebarriers-activity-7254017063415910401-pcqb", "date_published": "2024-10-21T06:34:01.014Z", "headline": "The saying \u201cIt\u2019s not what you know, but who you know\u201d still holds true for career progression...", "content": "The saying \u201cIt\u2019s not what you know, but who you know\u201d still holds true for career progression...", "author": { "name": "Sharon Peake, CPsychol", "url": "https://uk.linkedin.com/in/sharonpeake", "image": "https://media.licdn.com/dms/image/v2/D4E03AQGRKSsIYUdmjw/profile-displayphoto-shrink_200_200/profile-displayphoto-shrink_200_200/0/1678826812279?e=2147483647&v=beta&t=klv4HfBifCVlcXkrBoO0vS7M0DTqCiC1HnqRm7voARc" }, "image": "https://media.licdn.com/dms/image/v2/D4D22AQGp3EM1NyskDg/feedshare-shrink_800/feedshare-shrink_800/0/1729267020723?e=2147483647&v=beta&t=9VQk-rlosYkiVTZqBRkMQpBiLUEFKNo-uzbeSKIrARc", "reactions": { "like_count": 15, "comment_count": 8 }, "comments": [ { "author": "John Doe", "date_published": "2024-10-21T08:00:00.000Z", "text": "Great insights!", "reactions": { "like_count": 2 } } ], "actor_id": "i8Vq02GYSTUBZQeEd", "run_id": "EWP9bm92giwivjP7n" } ] ``` ### Use Cases - **Market Research and Analysis**: Gain insights into industry trends by analyzing LinkedIn posts. - **Competitive Intelligence**: Monitor competitors' activities and strategies through their LinkedIn presence. - **Price Monitoring**: Track pricing discussions and changes in specific industries. - **Content Aggregation**: Collect valuable content for newsletters or reports. - **Academic Research**: Use data for studies on social media influence and networking. - **Business Automation**: Automate the collection of professional insights for strategic planning. ### Installation and Usage 1. Search for "Linkedin Posts Reactions 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 is a JSON array containing objects with fields such as `url`, `post_id`, `date_published`, `headline`, `content`, `author` details, `image`, `reactions`, and `comments`. Each comment includes the author's name, publication date, text, and reaction count. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: [Contact Us](https://getdataforme.com/contact/) We're here to help you get the most out of this Actor! # Actor input Schema ## `Urls` (type: `array`): The 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 { "Urls": [ "https://www.linkedin.com/posts/sharonpeake_the-saying-its-not-what-you-know-but-who-activity-7254017063415910401-eC--?utm_source=share&utm_medium=guest_desktop&utm_content=keyword-landing-page&utm_campaign=copy" ], "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-posts-reactions-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-posts-reactions-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-posts-reactions-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/linkedin-posts-reactions-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Linkedin Posts Reactions Spider", "description": "The LinkedIn Posts Reactions Spider efficiently scrapes LinkedIn posts, capturing detailed data like reactions and comments....", "version": "0.0", "x-build-id": "R5mRltW6ZS8E0ME3b" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~linkedin-posts-reactions-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-linkedin-posts-reactions-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~linkedin-posts-reactions-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-linkedin-posts-reactions-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~linkedin-posts-reactions-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-linkedin-posts-reactions-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "Urls": { "title": "Urls", "minItems": 1, "type": "array", "description": "The urls for the spider.", "default": [ "https://www.linkedin.com/posts/sharonpeake_the-saying-its-not-what-you-know-but-who-activity-7254017063415910401-eC--?utm_source=share&utm_medium=guest_desktop&utm_content=keyword-landing-page&utm_campaign=copy" ], "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 } } } } } } } } } } ```