# LinkedIn Post Insights Extractor (`patient_discovery/linkedin-post-insights-extractor`) Actor Get full engagement insights from any LinkedIn post URL — likes, comments, shares, reaction types, author profile, and post content. Perfect for influencer research, content audits, and competitive benchmarking. No LinkedIn account needed. - **URL**: https://apify.com/patient\_discovery/linkedin-post-insights-extractor.md - **Developed by:** [Surge Street](https://apify.com/patient_discovery) (community) - **Categories:** Social media, Lead generation - **Stats:** 2 total users, 1 monthly users, 75.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 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. 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 Insights Extractor Ever needed to know exactly how a LinkedIn post performed — not just "a lot of likes" but the actual numbers, broken down by reaction type, with the author's details attached? This actor gives you that data, cleanly, for any LinkedIn post URL you can paste in. No LinkedIn account, no scraper setup, no browser automation. Just a URL in, and a complete analytics record out. ### What you'll find in the output **Engagement numbers** The total like, comment, and share counts — plus a full reaction-type breakdown so you can see how many people hit Like vs Celebrate vs Insightful vs Love. **Post content** The full text of the post, when it was published, and any media attached (images, documents, videos). **Author profile** The poster's name, headline, and LinkedIn profile URL — whether they're an individual creator or a company page. **Technical identifiers** URNs for the reactions, comments, and reposts — useful if you're building deeper analysis on top of this. ### Perfect for **Influencer research** — before reaching out to a creator for a partnership, verify their actual engagement numbers. Average likes per post tells you more than follower count. **Content competitive analysis** — track how competitors' posts perform. Which topics get the most Insightful reactions? Which formats drive shares? **Campaign reporting** — if your team or a client posted something on LinkedIn, pull the stats into a report without manually screenshotting the UI. **Social listening** — run this actor on a schedule (daily or weekly) across a list of posts you're monitoring. ### Sample output | Field | Example value | |---|---| | Post text | "Thrilled to announce..." | | Author | Sarah Kim, VP of Product at Figma | | Posted | Nov 14, 2024 | | Likes | 2,341 | | Comments | 187 | | Shares | 94 | | Top reaction | PRAISE (Celebrate) — 980 | ### How to use it 1. Find the LinkedIn post you want to analyze. Copy the URL. 2. Paste it into the actor's input field (one or many URLs). 3. Hit Run. Results are ready in about 30 seconds. 4. Download as CSV or JSON, or connect via webhook. ### Pricing $15 per 1,000 posts analyzed. No monthly fee. You run it when you need it. ### FAQ **Can I submit multiple post URLs at once?** Yes — paste as many as you need. The actor processes them in parallel. **Does this include the actual commenter names?** No — this actor returns top-level analytics for the post. If you need the individual commenters or reactors, those are available via separate actors in this store. **What if a post has been deleted?** The actor will return an error for that URL and move on to the next one. You won't be charged for failed records. # Actor input Schema ## `linkedin_url` (type: `string`): The LinkedIn Post Url to scraping ## Actor input object example ```json { "linkedin_url": "https://www.linkedin.com/posts/anthropicresearch_statement-on-comments-from-the-secretary-activity-7433337757906923520-p2nH" } ```` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("patient_discovery/linkedin-post-insights-extractor").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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("patient_discovery/linkedin-post-insights-extractor").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 patient_discovery/linkedin-post-insights-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=patient_discovery/linkedin-post-insights-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Post Insights Extractor", "description": "Get full engagement insights from any LinkedIn post URL — likes, comments, shares, reaction types, author profile, and post content. Perfect for influencer research, content audits, and competitive benchmarking. No LinkedIn account needed.\n", "version": "1.0", "x-build-id": "tHrvBBQfa3BhQkP7f" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/patient_discovery~linkedin-post-insights-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-patient_discovery-linkedin-post-insights-extractor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/patient_discovery~linkedin-post-insights-extractor/runs": { "post": { "operationId": "runs-sync-patient_discovery-linkedin-post-insights-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/patient_discovery~linkedin-post-insights-extractor/run-sync": { "post": { "operationId": "run-sync-patient_discovery-linkedin-post-insights-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "linkedin_url" ], "properties": { "linkedin_url": { "title": "LinkedIn Post Url", "type": "string", "description": "The LinkedIn Post Url to scraping", "default": "https://www.linkedin.com/posts/anthropicresearch_statement-on-comments-from-the-secretary-activity-7433337757906923520-p2nH" } } }, "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 } } } } } } } } } } ```