# LinkedIn Post Engagement Data Scraper (`monumental_world/linkedin-post-engagement-data`) Actor Extract structured engagement data from any LinkedIn post URL. Returns reaction counts by type, comment totals, share counts, author metadata, post content, and media attachments. Designed for researchers, analysts, and data pipelines. - **URL**: https://apify.com/monumental\_world/linkedin-post-engagement-data.md - **Developed by:** [Raised Pro](https://apify.com/monumental_world) (community) - **Categories:** Social media, Lead generation - **Stats:** 3 total users, 2 monthly users, 80.0% 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 Engagement Data Scraper A structured data extraction tool for LinkedIn post analytics. Submit any LinkedIn post URL and receive a comprehensive engagement record: reaction type distribution, comment and share totals, complete author metadata, and post content — all in clean, consistently structured JSON. ### Data Schema #### Engagement Metrics | Field | Type | Description | |---|---|---| | `num_likes` | integer | Total like count (all reaction types combined) | | `num_comments` | integer | Total comment count | | `num_shares` | integer | Total share/repost count | | `reaction_counts.LIKE` | integer | Standard Like reactions | | `reaction_counts.PRAISE` | integer | Celebrate reactions | | `reaction_counts.EMPATHY` | integer | Support reactions | | `reaction_counts.INTEREST` | integer | Insightful reactions | | `reaction_counts.APPRECIATION` | integer | Love reactions | | `reaction_counts.ENTERTAINMENT` | integer | Funny reactions | #### Post Content | Field | Type | Description | |---|---|---| | `post_text` | string | Full post body text | | `post_url` | string | Canonical URL | | `created_at` | ISO 8601 | Publication timestamp | | `urn` | string | LinkedIn internal URN | | `is_repost` | boolean | Whether this is a reshare | | `original_post` | object | If repost: original author + content | | `media_attachments` | array | Images, documents, video thumbnails | #### Author Data | Field | Type | Description | |---|---|---| | `author_name` | string | Display name | | `author_headline` | string | LinkedIn headline/description | | `author_linkedin_url` | string | Profile or company page URL | | `author_type` | string | "person" or "company" | | `author_image` | string | Profile photo URL | #### Reference URNs | Field | Description | |---|---| | `reactions_urn` | URN for fetching individual reactors | | `comments_urn` | URN for fetching individual comments | | `reposts_urn` | URN for fetching reposts | ### Input ```json { "post_urls": [ "https://www.linkedin.com/posts/example-activity-7201234567890-ABCD/", "https://www.linkedin.com/feed/update/urn:li:activity:7201234567890/" ] } ```` Accepts all LinkedIn post URL formats. Batch inputs processed in parallel. ### Research Applications - **Social media engagement analysis** — longitudinal study of engagement patterns across creator types, industries, or content formats - **Influence measurement** — quantifying reach and resonance of LinkedIn content at scale - **Platform behavior research** — studying reaction type distributions as signals of content quality or sentiment - **Dataset construction** — building labeled post datasets for NLP model training ### Pricing $15 per 1,000 records. Billed on successful results only. No subscription required. ### FAQ **Is this suitable for academic research?** Yes. The actor scrapes only publicly accessible post data. URNs are included in output to allow traceable citations. **What is the throughput rate?** Approximately 60–80 posts per minute under normal conditions. **Can I retrieve individual reactor identities?** This actor returns aggregate engagement counts only. The `reactions_urn` and `comments_urn` fields in the output can be used with separate actors to retrieve individual user-level data. # Actor input Schema ## `linkedin_url` (type: `string`): The LinkedIn Post Url to scraping ## Actor input object example ```json { "linkedin_url": "https://www.linkedin.com/posts/anthropicresearch_statement-on-comments-from-the-secretary-activity-7433337757906923520-p2nH" } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("monumental_world/linkedin-post-engagement-data").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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-post-engagement-data").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-post-engagement-data --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=monumental_world/linkedin-post-engagement-data", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Post Engagement Data Scraper", "description": "Extract structured engagement data from any LinkedIn post URL. Returns reaction counts by type, comment totals, share counts, author metadata, post content, and media attachments. Designed for researchers, analysts, and data pipelines.\n", "version": "1.0", "x-build-id": "fR3JY030Xd3s2J9zZ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/monumental_world~linkedin-post-engagement-data/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-monumental_world-linkedin-post-engagement-data", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/monumental_world~linkedin-post-engagement-data/runs": { "post": { "operationId": "runs-sync-monumental_world-linkedin-post-engagement-data", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/monumental_world~linkedin-post-engagement-data/run-sync": { "post": { "operationId": "run-sync-monumental_world-linkedin-post-engagement-data", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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 } } } } } } } } } } ```