# LinkedIn Post Search - Keywords & Public Posts (`thirdwatch/linkedin-post-search-scraper`) Actor Search public LinkedIn posts by keyword and extract post text, authors, reactions, comments, media, post IDs, and canonical URLs without login. - **URL**: https://apify.com/thirdwatch/linkedin-post-search-scraper.md - **Developed by:** [Thirdwatch](https://apify.com/thirdwatch) (community) - **Categories:** Social media, Marketing - **Stats:** 5 total users, 3 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 Search Scraper Find public LinkedIn posts by keyword, exact phrase, hashtag, person, or company—then enrich the discoveries with full public post text, author details, engagement counts, timestamp, and media when LinkedIn exposes them. Unlike tools that require a LinkedIn account, cookies, or Sales Navigator, this Actor discovers posts through the public Google index and reads LinkedIn’s public embed surface. It is designed for durable research and monitoring workflows, not authenticated surveillance. ### What you get | Field | Description | |---|---| | `query` | Search query that discovered the post | | `url`, `urn`, `post_id` | Canonical post identifiers | | `text`, `text_preview`, `title` | Full public text when available, with search preview fallback | | `author_name`, `author_headline`, `author_profile_url` | Public author details | | `reactions_count`, `comments_count` | Public engagement counters | | `posted_relative` | Relative post time exposed by LinkedIn | | `media` | Public post image URLs | | `enrichment_status` | `enriched`, `preview_only`, or `not_requested` | ### Example input ```json { "queries": ["generative AI hiring", "site reliability engineering"], "maxResultsPerQuery": 25, "enrichPosts": true, "country": "us", "language": "en" } ```` ### Use cases - Brand, executive, and competitor mention monitoring - Founder, creator, and thought-leader discovery - B2B buying-signal and hiring-signal research - Topic research for content and market intelligence - Building cited datasets of public professional discussion ### Coverage you can trust Search engines do not index every LinkedIn post. This Actor returns the public, Google-indexed subset and labels rows where LinkedIn enrichment is unavailable instead of inventing missing fields. Private, deleted, login-only, and non-indexed posts are outside scope. ### Pricing Pay only for posts returned: **$0.002 / $0.0017 / $0.0014 / $0.0012** across FREE, BRONZE, SILVER, and GOLD. Failed searches and duplicate URLs within the same query are not charged. More LinkedIn data tools at [thirdwatch.dev](https://thirdwatch.dev). # Actor input Schema ## `queries` (type: `array`): Keywords, exact phrases, hashtags, people, or companies to find in publicly indexed LinkedIn posts. ## `maxResultsPerQuery` (type: `integer`): Maximum unique public posts returned for each query. ## `enrichPosts` (type: `boolean`): Fetch LinkedIn's public embed page to add full text, author, reactions, comments, timestamp, and media when available. ## `country` (type: `string`): Two-letter Google search country code such as us, gb, de, or in. ## `language` (type: `string`): Two-letter Google interface language such as en, de, fr, or es. ## `proxyConfiguration` (type: `object`): Proxy used only for optional LinkedIn post enrichment. Residential is recommended. ## Actor input object example ```json { "queries": [ "generative AI hiring" ], "maxResultsPerQuery": 20, "enrichPosts": true, "country": "us", "language": "en", "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ``` # Actor output Schema ## `results` (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 = { "queries": [ "generative AI hiring" ] }; // Run the Actor and wait for it to finish const run = await client.actor("thirdwatch/linkedin-post-search-scraper").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 = { "queries": ["generative AI hiring"] } # Run the Actor and wait for it to finish run = client.actor("thirdwatch/linkedin-post-search-scraper").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 '{ "queries": [ "generative AI hiring" ] }' | apify call thirdwatch/linkedin-post-search-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=thirdwatch/linkedin-post-search-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Post Search - Keywords & Public Posts", "description": "Search public LinkedIn posts by keyword and extract post text, authors, reactions, comments, media, post IDs, and canonical URLs without login.", "version": "1.0", "x-build-id": "IbJ5b6NgsNVymc9ty" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/thirdwatch~linkedin-post-search-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-thirdwatch-linkedin-post-search-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/thirdwatch~linkedin-post-search-scraper/runs": { "post": { "operationId": "runs-sync-thirdwatch-linkedin-post-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/thirdwatch~linkedin-post-search-scraper/run-sync": { "post": { "operationId": "run-sync-thirdwatch-linkedin-post-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "queries" ], "properties": { "queries": { "title": "Search queries", "type": "array", "description": "Keywords, exact phrases, hashtags, people, or companies to find in publicly indexed LinkedIn posts.", "items": { "type": "string" } }, "maxResultsPerQuery": { "title": "Maximum posts per query", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum unique public posts returned for each query.", "default": 20 }, "enrichPosts": { "title": "Enrich full public post data", "type": "boolean", "description": "Fetch LinkedIn's public embed page to add full text, author, reactions, comments, timestamp, and media when available.", "default": true }, "country": { "title": "Search country", "type": "string", "description": "Two-letter Google search country code such as us, gb, de, or in.", "default": "us" }, "language": { "title": "Search language", "type": "string", "description": "Two-letter Google interface language such as en, de, fr, or es.", "default": "en" }, "proxyConfiguration": { "title": "LinkedIn proxy", "type": "object", "description": "Proxy used only for optional LinkedIn post enrichment. Residential is recommended.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } } }, "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 } } } } } } } } } } ```