# PRO Post Reactions LinkedIn Scraper (`dendelabs/linkedin-reactions-scraper-pro`) Actor Extract all reactions from LinkedIn posts with auto-pagination, incremental scraping, headline filters, and analytics. No LinkedIn account needed. - **URL**: https://apify.com/dendelabs/linkedin-reactions-scraper-pro.md - **Developed by:** [Dende Labs API](https://apify.com/dendelabs) (community) - **Categories:** Social media, Automation, Lead generation - **Stats:** 3 total users, 1 monthly users, 100.0% runs succeeded, 2 bookmarks - **User rating**: No ratings yet ## Pricing from $4.00 / 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 ## ๐Ÿš€ LinkedIn Post Reactions Scraper Pro Extract detailed reaction data from any LinkedIn post โ€” including reactor names, headlines, profile URLs, and photos. Supports batch processing of up to 1,000 posts in a single run. **No LinkedIn account or cookies required.** > ๐Ÿ’ก **For other LinkedIn tools by Dende Labs**, check: https://apify.com/dendelabs --- ### โœจ Key Features | Feature | Description | |---------|-------------| | ๐Ÿ”„ **Auto-pagination** | One run extracts ALL reactions. No manual page management. | | ๐Ÿ“ฆ **Batch processing** | Scrape up to 1,000 posts in a single run. | | ๐Ÿ” **Headline keyword filters** | Only include reactors matching specific keywords (e.g., "CEO", "VP"). | | ๐Ÿข **Company detection** | Each reactor is flagged as person or company page. | | ๐Ÿ“Š **Incremental tracking** | Compare with previous runs โ€” each reactor is marked as `new` or `existing`. | | ๐Ÿšซ **Exclude companies** | Filter out company pages, keep only people. | | ๐Ÿ”— **All URL formats** | Paste any LinkedIn post link โ€” even with tracking parameters. | | ๐Ÿ” **No cookies needed** | No risk of account restrictions or bans. | --- ### ๐Ÿ“ฅ Input #### Basic Usage Just paste one or more LinkedIn post URLs: ```json { "postUrls": [ "https://www.linkedin.com/posts/satyanadella_no-one-becomes-a-clinician-to-do-paperwork-activity-7302346926123798528-jitu" ] } ```` #### All Input Parameters | Parameter | Type | Required | Default | Description | |-----------|------|----------|---------|-------------| | `postUrls` | string\[] | โœ… Yes | โ€” | LinkedIn post URLs or activity IDs (up to 1,000) | | `reactionType` | string | No | `ALL` | Filter: `ALL`, `LIKE`, `PRAISE`, `EMPATHY`, `APPRECIATION`, `INTEREST`, `ENTERTAINMENT` | | `maxReactions` | integer | No | `0` | Max reactions per post. `0` = unlimited | | `headlineKeywords` | string\[] | No | `[]` | Only include reactors whose headline contains these keywords | | `excludeCompanies` | boolean | No | `false` | Exclude company/org pages from results | | `previousDatasetId` | string | No | โ€” | Dataset ID from previous run for incremental tracking | | `metadataOnly` | boolean | No | `false` | Only fetch reaction counts, not individual reactions (see [Metadata Only Mode](#example-8-metadata-only--check-reaction-count-before-scraping)) | #### Supported URL Formats All of these work โ€” just paste whatever you have: ``` โœ… https://www.linkedin.com/posts/satyanadella_activity-7302346926123798528-jitu โœ… https://www.linkedin.com/feed/update/urn:li:activity:7302346926123798528/ โœ… https://www.linkedin.com/feed/update/urn:li:share:7302346926123798528/ โœ… https://www.linkedin.com/posts/user_slug-activity-123/?utm_source=share&utm_medium=... โœ… 7302346926123798528 ``` *** ### ๐Ÿ“ค Output #### Single Reaction Each reaction in the dataset looks like this: ```json { "reaction_type": "LIKE", "is_company": false, "reactor": { "id": "ACoAAB8v74YB5oqaAg-4F2VRFh9tEt0zXfRsjpE", "name": "Ashish Pandey", "headline": "Head of Ops @ Wiz Labs", "linkedinUrl": "https://www.linkedin.com/in/ACoAAB8v74YB...", "profile_pic": "https://media.licdn.com/dms/image/v2/..." }, "_metadata": { "post_url": "https://www.linkedin.com/posts/satyanadella_...", "activity_id": "urn:li:activity:7302346926123798528", "extracted_at": "2026-03-30T12:00:00.000Z" } } ``` #### Company Reactor Example When a company page reacts to a post, `is_company` is `true`: ```json { "reaction_type": "LIKE", "is_company": true, "reactor": { "id": "106345960", "name": "Dende Labs", "headline": "2 followers", "linkedinUrl": "https://www.linkedin.com/company/dendelabs/", "profile_pic": null }, "_metadata": { "post_url": "https://www.linkedin.com/posts/...", "activity_id": "urn:li:activity:7399183215997108224", "extracted_at": "2026-03-30T12:00:00.000Z" } } ``` #### Output Fields Reference | Field | Type | Description | |-------|------|-------------| | `reaction_type` | string | `LIKE`, `PRAISE`, `EMPATHY`, `APPRECIATION`, `INTEREST`, or `ENTERTAINMENT` | | `is_company` | boolean | `true` if the reactor is a company page, `false` if a person | | `is_new` | boolean | Only present when using incremental tracking. `true` = new reactor, `false` = seen before | | `reactor.id` | string | LinkedIn profile or company URN | | `reactor.name` | string | Full name of the person or company | | `reactor.headline` | string | LinkedIn headline / tagline | | `reactor.linkedinUrl` | string | Direct link to the reactor's LinkedIn profile | | `reactor.profile_pic` | string | URL to profile photo (800x800) or `null` | | `_metadata.post_url` | string | The original post URL you provided as input | | `_metadata.activity_id` | string | LinkedIn activity URN of the post | | `_metadata.extracted_at` | string | ISO timestamp of when the data was extracted | *** ### ๐Ÿ“Œ Examples #### Example 1: Get all reactions from a single post ```json { "postUrls": [ "https://www.linkedin.com/posts/satyanadella_no-one-becomes-a-clinician-to-do-paperwork-activity-7302346926123798528-jitu" ] } ``` #### Example 2: Batch โ€” scrape reactions from multiple posts ```json { "postUrls": [ "https://www.linkedin.com/posts/satyanadella_activity-7302346926123798528-jitu", "https://www.linkedin.com/posts/billgates_activity-7300000000000000000-abcd", "https://www.linkedin.com/feed/update/urn:li:activity:7298765432109876543/" ] } ``` #### Example 3: Only get reactions from decision-makers Use `headlineKeywords` to filter reactors by their LinkedIn headline: ```json { "postUrls": [ "https://www.linkedin.com/posts/satyanadella_activity-7302346926123798528-jitu" ], "headlineKeywords": ["CEO", "CTO", "VP", "Founder", "Director", "Head of"] } ``` Only reactors whose headline contains at least one of these keywords will be included. #### Example 4: Only people, no company pages ```json { "postUrls": [ "https://www.linkedin.com/posts/satyanadella_activity-7302346926123798528-jitu" ], "excludeCompanies": true } ``` #### Example 5: Filter by reaction type ```json { "postUrls": [ "https://www.linkedin.com/posts/satyanadella_activity-7302346926123798528-jitu" ], "reactionType": "PRAISE" } ``` Available types: `ALL`, `LIKE`, `PRAISE` (Celebrate), `EMPATHY` (Love), `APPRECIATION` (Insightful), `INTEREST` (Curious), `ENTERTAINMENT` (Funny) #### Example 6: Limit results per post ```json { "postUrls": [ "https://www.linkedin.com/posts/satyanadella_activity-7302346926123798528-jitu" ], "maxReactions": 500 } ``` #### Example 7: Incremental tracking โ€” detect new reactors First run โ€” scrape all reactions: ```json { "postUrls": ["https://www.linkedin.com/posts/satyanadella_activity-7302346926123798528-jitu"] } ``` Second run โ€” pass the previous dataset ID to tag each reactor as `new` or `existing`: ```json { "postUrls": ["https://www.linkedin.com/posts/satyanadella_activity-7302346926123798528-jitu"], "previousDatasetId": "abc123xyz" } ``` Each reactor in the output will have an `is_new` field: - `true` โ€” this reactor was NOT in the previous dataset (new engagement) - `false` โ€” this reactor was already in the previous dataset > ๐Ÿ’ก You can find the dataset ID in the Apify Console under the **Storage** tab of your previous run. #### Example 8: Metadata only โ€” check reaction count before scraping Use `metadataOnly` to quickly check how many reactions a post has **without extracting them all**. This makes just one API call per post and returns only the totals. ```json { "postUrls": [ "https://www.linkedin.com/posts/satyanadella_activity-7302346926123798528-jitu" ], "metadataOnly": true } ``` Output: ```json { "post_url": "https://www.linkedin.com/posts/satyanadella_...", "total_reactions": 847, "total_pages": 9, "extracted_at": "2026-03-31T12:00:00.000Z" } ``` **When to use this:** - **Avoid unnecessary scrapes** โ€” Check if a post has new reactions since your last run before paying for a full extraction. For example, if you scraped 847 reactions yesterday and the metadata still shows 847 today, you can skip the full scrape. - **Cost estimation** โ€” Know exactly how many reactions (and how much it will cost) before committing to a full run. - **Monitoring dashboards** โ€” Track reaction growth over time without extracting every reactor each time. > ๐Ÿ’ก Metadata-only mode costs just **$0.004** per post (one result in the dataset). A full scrape of 1,000 reactions would cost **$4.00**. Use metadata to decide if the full scrape is worth it. *** ### ๐Ÿค– Integration with AI Agents This actor is optimized for use with AI agents and LLMs via the Apify MCP server. #### Output Schema The actor produces a flat dataset where each item represents one reaction. Key fields for agent consumption: - `reactor.name` โ€” Who reacted - `reactor.headline` โ€” Their professional context - `reaction_type` โ€” How they reacted - `is_company` โ€” Whether it's a company or person #### Example Agent Prompt > "Scrape all reactions from this LinkedIn post and find decision-makers (CEOs, VPs, Directors) who engaged with it. Return their names and LinkedIn URLs." The agent can use `headlineKeywords: ["CEO", "VP", "Director"]` to filter directly at scrape time, reducing post-processing. *** ### ๐ŸŽฏ Use Cases - **๐Ÿ”Ž Sales prospecting** โ€” Find decision-makers engaging with competitor or industry content - **๐Ÿ“Š Market research** โ€” Analyze who engages with thought leaders in your space - **๐Ÿ† Competitor analysis** โ€” Track engagement patterns on competitor posts - **๐Ÿ“‹ Lead generation** โ€” Build targeted lists from post engagement data - **๐Ÿ“ˆ Content strategy** โ€” Understand what types of professionals engage with specific topics - **๐Ÿ”„ Engagement monitoring** โ€” Track reaction growth over time with incremental mode *** ### โ„น๏ธ Profile URL Format LinkedIn returns profile URLs in **profile ID format** for reactions, for example: ``` https://www.linkedin.com/in/ACoAAB8v74YB5oqaAg-4F2VRFh9tEt0zXfRsjpE ``` Instead of the human-friendly slug format: ``` https://www.linkedin.com/in/john-doe ``` This is a LinkedIn limitation โ€” the reactions popup only exposes profile IDs, not public slugs. Both formats are valid and redirect to the same profile. This is the standard behavior across all LinkedIn reaction scrapers. *** ### โš ๏ธ 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 ## `postUrls` (type: `array`): List of LinkedIn post URLs or activity IDs. Supports full URLs or just IDs. ## `reactionType` (type: `string`): Filter reactions by type. ALL returns every reaction type. ## `maxReactions` (type: `integer`): Maximum reactions to extract per post. 0 = unlimited (extracts all). ## `headlineKeywords` (type: `array`): Only include reactors whose headline contains at least one of these keywords (case-insensitive). Leave empty for no filter. ## `excludeCompanies` (type: `boolean`): If enabled, excludes company/organization pages from results (only people). ## `metadataOnly` (type: `boolean`): If enabled, only fetches the total reaction count for each post without extracting individual reactions. Useful for checking if a post has new reactions before running a full scrape. ## Actor input object example ```json { "postUrls": [ "https://www.linkedin.com/feed/update/urn:li:share:7404946870776868864/" ], "reactionType": "ALL", "maxReactions": 0, "headlineKeywords": [], "excludeCompanies": false, "metadataOnly": false } ``` # Actor output Schema ## `reactions` (type: `string`): No description # 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 = { "postUrls": [ "https://www.linkedin.com/feed/update/urn:li:share:7404946870776868864/" ] }; // Run the Actor and wait for it to finish const run = await client.actor("dendelabs/linkedin-reactions-scraper-pro").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 = { "postUrls": ["https://www.linkedin.com/feed/update/urn:li:share:7404946870776868864/"] } # Run the Actor and wait for it to finish run = client.actor("dendelabs/linkedin-reactions-scraper-pro").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 '{ "postUrls": [ "https://www.linkedin.com/feed/update/urn:li:share:7404946870776868864/" ] }' | apify call dendelabs/linkedin-reactions-scraper-pro --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=dendelabs/linkedin-reactions-scraper-pro", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "PRO Post Reactions LinkedIn Scraper", "description": "Extract all reactions from LinkedIn posts with auto-pagination, incremental scraping, headline filters, and analytics. No LinkedIn account needed.", "version": "1.0", "x-build-id": "ZNLiGFG7lIl8uOxQS" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/dendelabs~linkedin-reactions-scraper-pro/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-dendelabs-linkedin-reactions-scraper-pro", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/dendelabs~linkedin-reactions-scraper-pro/runs": { "post": { "operationId": "runs-sync-dendelabs-linkedin-reactions-scraper-pro", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/dendelabs~linkedin-reactions-scraper-pro/run-sync": { "post": { "operationId": "run-sync-dendelabs-linkedin-reactions-scraper-pro", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "postUrls" ], "properties": { "postUrls": { "title": "Post URLs or IDs", "minItems": 1, "maxItems": 1000, "type": "array", "description": "List of LinkedIn post URLs or activity IDs. Supports full URLs or just IDs.", "items": { "type": "string" } }, "reactionType": { "title": "Reaction Type Filter", "enum": [ "ALL", "LIKE", "PRAISE", "EMPATHY", "APPRECIATION", "INTEREST", "ENTERTAINMENT" ], "type": "string", "description": "Filter reactions by type. ALL returns every reaction type.", "default": "ALL" }, "maxReactions": { "title": "Max Reactions per Post", "minimum": 0, "type": "integer", "description": "Maximum reactions to extract per post. 0 = unlimited (extracts all).", "default": 0 }, "headlineKeywords": { "title": "Headline Keywords Filter", "type": "array", "description": "Only include reactors whose headline contains at least one of these keywords (case-insensitive). Leave empty for no filter.", "items": { "type": "string" }, "default": [] }, "excludeCompanies": { "title": "Exclude Company Pages", "type": "boolean", "description": "If enabled, excludes company/organization pages from results (only people).", "default": false }, "metadataOnly": { "title": "Metadata Only", "type": "boolean", "description": "If enabled, only fetches the total reaction count for each post without extracting individual reactions. Useful for checking if a post has new reactions before running a full scrape.", "default": false } } }, "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 } } } } } } } } } } ```