# LinkedIn Post Reactions Scraper (No Cookies) โœ… (No Cookies) (`harvestapi/linkedin-post-reactions`) Actor Extract reactions of LinkedIn posts or comments, such as likes, appreciations, etc. No cookies or account required. - **URL**: https://apify.com/harvestapi/linkedin-post-reactions.md - **Developed by:** [HarvestAPI](https://apify.com/harvestapi) (community) - **Categories:** Social media, Lead generation, Automation - **Stats:** 2,965 total users, 603 monthly users, 100.0% runs succeeded, 70 bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing Pay per event 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/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 Post Reactions Mass scraper Our powerful tool helps you extract reactions to LinkedIn posts or comments, such as likes, appreciations, and praises, without compromising security or violating platform policies. It is very helpful for engagement analysis and outreach purposes. #### Key Benefits - No cookies or account required: Access reactions data without sharing cookies or risking account restrictions - Low pricing: $2 per 1k reactions. - Fast response times deliver data in seconds ๐Ÿš€ - No caching, fresh data. ### How It Works - (required) List of post URLs or post comment URLs to scrape. Other params (optionally): - `maxItems` - Maximum number of reactions to scrape per each post. If you set to 0, it will scrape all available pages or up to 100 pages (each page 10 items) per post. #### Data You'll Receive - Reaction type (like, love, insightful, etc.) - Actor's name - Actor's LinkedIn encoded URL - Actor's position - Actor's profile picture URL Note: By default, this Actor returns encoded LinkedIn profile URLs as provided by the LinkedIn website. To obtain standard profile URLs and additional user data, enable Profile Scraper Mode in the input settings. Please note that visiting individual profiles incurs additional usage costs. Note: The total number of reactions is limited by the LinkedIn website reactions loader pop-up. Currently, the website stops loading more reactions after 1218 items. This Actor is scraping the LinkedIn website. It can only get the information that is available on the website. #### Sample output data Here is the example reaction output of this actor: ```json { "id": "urn:li:fsd_reaction:(urn:li:fsd_profile:ACoAAAMHAagBPz-UgeRDsX3klfoVZEkDw1ulS7E,urn:li:ugcPost:7329207002977443840,0)", "reactionType": "LIKE", "actor": { "id": "ACoAAAMHAagBPz-UgeRDsX3klfoVZEkDw1ulS7E", "name": "Gangotri Dey (Ph.D)", "linkedinUrl": "https://www.linkedin.com/in/ACoAAAMHAagBPz-UgeRDsX3klfoVZEkDw1ulS7E", "position": "Licensing Officer | Intellectual Property | Patents | Technology Transfer | Egalitarian | Numismatist", "pictureUrl": "https://media.licdn.com/dms/image/v2/D4E03AQGDj6wx_xJbRQ/profile-displayphoto-shrink_800_800/profile-displayphoto-shrink_800_800/0/1668631616852?e=1753315200&v=beta&t=12ishzrb2wqXHERve9a-wNaL4_V6H6a2rA76UYX5MWI", "picture": { "url": "https://media.licdn.com/dms/image/v2/D4E03AQGDj6wx_xJbRQ/profile-displayphoto-shrink_800_800/profile-displayphoto-shrink_800_800/0/1668631616852?e=1753315200&v=beta&t=12ishzrb2wqXHERve9a-wNaL4_V6H6a2rA76UYX5MWI", "width": 594, "height": 594, "expiresAt": 1753315200000 } }, "postId": "7329207003942125568" } ```` ### Linkedin Post Reactions API The actor stores results in a dataset. You can export data in various formats such as CSV, JSON, XLS, etc. You can scrape and access data on demand using API. #### Support and Feedback We continuously enhance our tools based on user feedback. If you encounter technical issues or have suggestions for improvement: - Create an issue on the actorโ€™s Issues tab in Apify Console - Chat with us on our [Discord server](https://discord.gg/TGA9k9u2gE) - Or contact us at contact@harvest-api.com **\* \* \*** **Disclaimer**: This Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by LinkedIn Corporation. LinkedInยฎ is a registered trademark of LinkedIn Corporation. All trademarks are property of their respective owners. # Actor input Schema ## `posts` (type: `array`): Queries to search LinkedIn profiles by name. ## `maxItems` (type: `integer`): Maximum number of reactions to scrape per each post ## `reactionTypeFilter` (type: `array`): Select specific reaction types to scrape. If none selected, all reaction types will be scraped. ## `profileScraperMode` (type: `string`): Choose the mode for scraping LinkedIn profiles. The Short mode provides basic information, while the Full mode includes full detailed profile data. ## Actor input object example ```json { "posts": [ "https://www.linkedin.com/posts/satyanadella_today-at-microsoft-build-we-showed-you-how-activity-7330296202275495938-EWFD?utm_source=share&utm_medium=member_desktop&rcm=ACoAACzazy4B3ajZsA0WxWr6m4S77iItYbYCLZM", "https://www.linkedin.com/posts/microsoft-events_microsoft-build-has-arrived-in-seattle-and-ugcPost-7329991434395160578-GnK7?utm_source=share&utm_medium=member_desktop&rcm=ACoAACzazy4B3ajZsA0WxWr6m4S77iItYbYCLZM", "https://www.linkedin.com/feed/update/urn:li:activity:7320867199693246465/", "https://www.linkedin.com/feed/update/urn:li:activity:7408723748973056001?commentUrn=urn%3Ali%3Acomment%3A%28activity%3A7408723748973056001%2C7408723949095772161%29&dashCommentUrn=urn%3Ali%3Afsd_comment%3A%287408723949095772161%2Curn%3Ali%3Aactivity%3A7408723748973056001%29" ], "maxItems": 10, "profileScraperMode": "short" } ``` # 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 = { "posts": [ "https://www.linkedin.com/posts/satyanadella_today-at-microsoft-build-we-showed-you-how-activity-7330296202275495938-EWFD?utm_source=share&utm_medium=member_desktop&rcm=ACoAACzazy4B3ajZsA0WxWr6m4S77iItYbYCLZM", "https://www.linkedin.com/posts/microsoft-events_microsoft-build-has-arrived-in-seattle-and-ugcPost-7329991434395160578-GnK7?utm_source=share&utm_medium=member_desktop&rcm=ACoAACzazy4B3ajZsA0WxWr6m4S77iItYbYCLZM", "https://www.linkedin.com/feed/update/urn:li:activity:7320867199693246465/", "https://www.linkedin.com/feed/update/urn:li:activity:7408723748973056001?commentUrn=urn%3Ali%3Acomment%3A%28activity%3A7408723748973056001%2C7408723949095772161%29&dashCommentUrn=urn%3Ali%3Afsd_comment%3A%287408723949095772161%2Curn%3Ali%3Aactivity%3A7408723748973056001%29" ], "maxItems": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("harvestapi/linkedin-post-reactions").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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 = { "posts": [ "https://www.linkedin.com/posts/satyanadella_today-at-microsoft-build-we-showed-you-how-activity-7330296202275495938-EWFD?utm_source=share&utm_medium=member_desktop&rcm=ACoAACzazy4B3ajZsA0WxWr6m4S77iItYbYCLZM", "https://www.linkedin.com/posts/microsoft-events_microsoft-build-has-arrived-in-seattle-and-ugcPost-7329991434395160578-GnK7?utm_source=share&utm_medium=member_desktop&rcm=ACoAACzazy4B3ajZsA0WxWr6m4S77iItYbYCLZM", "https://www.linkedin.com/feed/update/urn:li:activity:7320867199693246465/", "https://www.linkedin.com/feed/update/urn:li:activity:7408723748973056001?commentUrn=urn%3Ali%3Acomment%3A%28activity%3A7408723748973056001%2C7408723949095772161%29&dashCommentUrn=urn%3Ali%3Afsd_comment%3A%287408723949095772161%2Curn%3Ali%3Aactivity%3A7408723748973056001%29", ], "maxItems": 10, } # Run the Actor and wait for it to finish run = client.actor("harvestapi/linkedin-post-reactions").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "posts": [ "https://www.linkedin.com/posts/satyanadella_today-at-microsoft-build-we-showed-you-how-activity-7330296202275495938-EWFD?utm_source=share&utm_medium=member_desktop&rcm=ACoAACzazy4B3ajZsA0WxWr6m4S77iItYbYCLZM", "https://www.linkedin.com/posts/microsoft-events_microsoft-build-has-arrived-in-seattle-and-ugcPost-7329991434395160578-GnK7?utm_source=share&utm_medium=member_desktop&rcm=ACoAACzazy4B3ajZsA0WxWr6m4S77iItYbYCLZM", "https://www.linkedin.com/feed/update/urn:li:activity:7320867199693246465/", "https://www.linkedin.com/feed/update/urn:li:activity:7408723748973056001?commentUrn=urn%3Ali%3Acomment%3A%28activity%3A7408723748973056001%2C7408723949095772161%29&dashCommentUrn=urn%3Ali%3Afsd_comment%3A%287408723949095772161%2Curn%3Ali%3Aactivity%3A7408723748973056001%29" ], "maxItems": 10 }' | apify call harvestapi/linkedin-post-reactions --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=harvestapi/linkedin-post-reactions", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Post Reactions Scraper (No Cookies) โœ… (No Cookies)", "description": "Extract reactions of LinkedIn posts or comments, such as likes, appreciations, etc. No cookies or account required.", "version": "0.0", "x-build-id": "JOZvJXXt8aLWdlc82" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/harvestapi~linkedin-post-reactions/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-harvestapi-linkedin-post-reactions", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/harvestapi~linkedin-post-reactions/runs": { "post": { "operationId": "runs-sync-harvestapi-linkedin-post-reactions", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/harvestapi~linkedin-post-reactions/run-sync": { "post": { "operationId": "run-sync-harvestapi-linkedin-post-reactions", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "posts": { "title": "Posts", "type": "array", "description": "Queries to search LinkedIn profiles by name.", "items": { "type": "string" } }, "maxItems": { "title": "Maximum number of reactions to scrape per each post", "type": "integer", "description": "Maximum number of reactions to scrape per each post" }, "reactionTypeFilter": { "title": "Reaction Type Filter", "type": "array", "description": "Select specific reaction types to scrape. If none selected, all reaction types will be scraped.", "items": { "type": "string", "enum": [ "ALL", "LIKE", "PRAISE", "EMPATHY", "APPRECIATION", "INTEREST", "ENTERTAINMENT" ], "enumTitles": [ "All reaction types", "Like", "Praise", "Empathy", "Appreciation", "Interest", "Entertainment" ] } }, "profileScraperMode": { "title": "Profile Scraper Mode", "enum": [ "short", "main" ], "type": "string", "description": "Choose the mode for scraping LinkedIn profiles. The Short mode provides basic information, while the Full mode includes full detailed profile data.", "default": "short" } } }, "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 } } } } } } } } } } ```