# LinkedIn User Post History Scraper (`monumental_world/linkedin-user-post-history`) Actor Extract the complete post and activity history from any LinkedIn profile. Returns structured data on posts, reposts, comments, and reactions — with engagement metrics, reaction breakdowns, and media. Designed for research, content analysis, and audience intelligence. - **URL**: https://apify.com/monumental\_world/linkedin-user-post-history.md - **Developed by:** [Raised Pro](https://apify.com/monumental_world) (community) - **Categories:** Social media, Lead generation - **Stats:** 3 total users, 1 monthly users, 100.0% 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 User Post History Scraper Retrieve the complete publicly available activity history from any LinkedIn profile — posts, reposts, comments, and reactions — as structured, consistently formatted data. Designed for researchers, data analysts, and content intelligence teams that need reliable, bulk-extractable LinkedIn activity data. ### Activity Type Coverage This actor supports four distinct activity stream types: | `activity_type` | What it returns | |---|---| | `posts` | Original content published by the profile owner | | `reposts` | Content reshared by the profile owner (with or without commentary) | | `comments` | Posts the person publicly commented on (reveals their engagement behavior) | | `reactions` | Posts the person publicly reacted to (reveals consumption patterns) | The `comments` and `reactions` types are particularly valuable for audience intelligence — they reveal which content and creators a person actively engages with, not just what they produce. ### Dataset Schema | Field | Type | Description | |---|---|---| | `activity_type` | string | Post / Repost / Comment / Reaction | | `post_text` | string | Full post body | | `post_url` | string | Canonical post URL | | `created_at` | ISO 8601 | Publication or engagement timestamp | | `author_name` | string | Post author display name | | `author_headline` | string | Author LinkedIn headline | | `author_linkedin_url` | string | Author profile URL | | `author_type` | string | person / company | | `num_likes` | integer | Total like count | | `num_comments` | integer | Comment count | | `num_shares` | integer | Share count | | `reaction_counts` | object | Per-type breakdown | | `is_repost` | boolean | Reshared content flag | | `repost_commentary` | string | Resharer's added commentary | | `shared_post` | object | Original post data (for reposts) | | `media_attachments` | array | Attached media metadata | ### Input Schema ```json { "profile_urls": [ "https://www.linkedin.com/in/example-profile/" ], "activity_type": "posts", "max_results": 100 } ```` ### Research Applications **Content strategy research:** Extract full post histories from target industry voices. Analyze topic distribution, format mix, posting frequency, and engagement patterns at scale. **Audience behavior analysis:** Using `comments` and `reactions` modes, map which external content a set of profiles engages with — reveals consumption patterns and peer influence networks. **Influence measurement:** Compare engagement rates, reaction type distributions, and post frequency across a cohort of profiles in a target vertical. **Longitudinal tracking:** Run on a schedule via Apify API to build time-series activity datasets for ongoing research. ### Pagination The actor handles pagination automatically. LinkedIn limits access to approximately 200–300 activity items per type per profile. `max_results` can be set below this ceiling to retrieve a specific count. ### Pricing $15 per 1,000 activity items extracted. Billed on successful records only. No subscription. ### FAQ **Can I retrieve all four activity types for a single profile in one run?** Currently the actor retrieves one activity type per run. Run the actor four times with different `activity_type` settings to get complete coverage, or submit separate profile URLs for each type. **What does the `comments` activity type return exactly?** It returns the posts that the profile owner publicly commented on — not the person's own posts. Each record includes the original post content, the post author, engagement metrics on that post, and the comment context. **Is there a difference in data volume between activity types?** Yes — active creators typically have more `posts` history, while highly engaged users may have larger `reactions` and `comments` datasets. The ceiling varies by individual profile activity level. **How do I use this for audience intelligence?** Pull the `comments` and `reactions` activity streams for a target prospect. The resulting dataset reveals which creators and content they actively engage with — useful for warm intro mapping, relevance personalization, and understanding their information diet. # 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("monumental_world/linkedin-user-post-history").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("monumental_world/linkedin-user-post-history").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 monumental_world/linkedin-user-post-history --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=monumental_world/linkedin-user-post-history", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn User Post History Scraper", "description": "Extract the complete post and activity history from any LinkedIn profile. Returns structured data on posts, reposts, comments, and reactions — with engagement metrics, reaction breakdowns, and media. Designed for research, content analysis, and audience intelligence.\n", "version": "1.0", "x-build-id": "0km5R9ayCLfjxUG6f" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/monumental_world~linkedin-user-post-history/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-monumental_world-linkedin-user-post-history", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/monumental_world~linkedin-user-post-history/runs": { "post": { "operationId": "runs-sync-monumental_world-linkedin-user-post-history", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/monumental_world~linkedin-user-post-history/run-sync": { "post": { "operationId": "run-sync-monumental_world-linkedin-user-post-history", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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 } } } } } } } } } } ```