# LinkedIn Thought Leader Posts Scraper (`patient_discovery/linkedin-thought-leader-posts`) Actor Scrape all posts from any LinkedIn creator or thought leader. Returns post content, engagement stats, reaction types, and media — perfect for content research, influencer vetting, and competitive content analysis. No login required. - **URL**: https://apify.com/patient\_discovery/linkedin-thought-leader-posts.md - **Developed by:** [Surge Street](https://apify.com/patient_discovery) (community) - **Categories:** Social media, Lead generation - **Stats:** 2 total users, 0 monthly users, 90.9% 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 Thought Leader Posts Scraper Scrape everything a LinkedIn creator has posted — full text, engagement numbers, and reaction breakdowns — without manually scrolling through their profile for hours. Perfect for researching influencers before a partnership, auditing a competitor's content strategy, or building a content inspiration database from the voices in your industry. ### What it pulls For every post in their LinkedIn history: - The full post text (no truncation) - When it was published - Total likes, comments, and shares - Reaction type breakdown — Like vs Celebrate vs Insightful vs Love vs Support vs Funny - Any images, documents, or video thumbnails attached - Whether they wrote it or reshared it (and if reshared, the original post) ### Why content teams use this **Influencer research before partnership deals** — stop guessing at engagement. Pull the actual numbers for their last 50 posts. Calculate average engagement rate per post. Know what you're buying. **Competitor content auditing** — what is the industry voice posting about? Which topics drive the most Insightful reactions? Which formats — text-only, carousels, videos — outperform on this audience? **Content inspiration at scale** — extract top-performing posts across 20 creators in your niche, analyze what makes them work, use as briefs for your own content team. **Influencer vetting for recruiting / PR** — before putting an executive or brand ambassador in front of an audience, audit their post history. ### How to use it 1. Find the LinkedIn profile of the creator or thought leader you want to research 2. Copy their profile URL 3. Paste it into the input field — add as many profiles as you need 4. Set how many posts to retrieve (default: 50, max: 200+) 5. Run. Results ready in 2–3 minutes. Export as CSV and sort by engagement to find the top performers. ### Sample output | Post Date | Likes | Comments | Shares | Top Reaction | Post Preview | |---|---|---|---|---|---| | Nov 22 | 4,200 | 380 | 210 | PRAISE (1,800) | "The best sales teams I've worked with..." | | Nov 18 | 1,900 | 142 | 88 | LIKE (1,200) | "A framework I use for..." | | Nov 14 | 870 | 76 | 31 | INTEREST (490) | "Hot take: most content calendars..." | ### Pricing $15 per 1,000 posts. No subscription. No commitment. ### FAQ **Can I track multiple thought leaders at once?** Yes — submit multiple profile URLs in one run. All are scraped in the same job. **Does it capture reposts (content they reshared)?** Yes. The `activity_type` toggle lets you retrieve original posts only, reposts only, or both. **What's the engagement rate calculation?** The actor returns raw engagement counts. You can calculate engagement rate in your spreadsheet: (likes + comments + shares) / followers × 100. # Actor input Schema ## `linkedin_url` (type: `string`): The LinkedIn Profile Url to scraping ## `maxPages` (type: `integer`): Maximum number of pages to fetch (pagination handled automatically) ## Actor input object example ```json { "linkedin_url": "https://in.linkedin.com/in/mayankchaba", "maxPages": 1 } ```` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("patient_discovery/linkedin-thought-leader-posts").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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-thought-leader-posts").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-thought-leader-posts --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=patient_discovery/linkedin-thought-leader-posts", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Thought Leader Posts Scraper", "description": "Scrape all posts from any LinkedIn creator or thought leader. Returns post content, engagement stats, reaction types, and media — perfect for content research, influencer vetting, and competitive content analysis. No login required.\n", "version": "1.0", "x-build-id": "Yn2pjNSEcPwawSQ8c" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/patient_discovery~linkedin-thought-leader-posts/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-patient_discovery-linkedin-thought-leader-posts", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/patient_discovery~linkedin-thought-leader-posts/runs": { "post": { "operationId": "runs-sync-patient_discovery-linkedin-thought-leader-posts", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/patient_discovery~linkedin-thought-leader-posts/run-sync": { "post": { "operationId": "run-sync-patient_discovery-linkedin-thought-leader-posts", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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 Profile Url", "type": "string", "description": "The LinkedIn Profile Url to scraping", "default": "https://in.linkedin.com/in/mayankchaba" }, "maxPages": { "title": "Maximum Pages", "minimum": 1, "maximum": 5, "type": "integer", "description": "Maximum number of pages to fetch (pagination handled automatically)", "default": 1 } } }, "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 } } } } } } } } } } ```