# LinkedIn Hashtag Post Finder (`datamagnet/linkedin-hashtag-post-finder`) Actor Find and return social posts matching a hashtag in a simple, easy-to-read format. - **URL**: https://apify.com/datamagnet/linkedin-hashtag-post-finder.md - **Developed by:** [Datamagnet](https://apify.com/datamagnet) (community) - **Categories:** Social media, Lead generation, News - **Stats:** 2 total users, 1 monthly users, 90.9% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 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 Hashtag Post Finder โ€” LinkedIn Search Post by Hashtag Scraper Find LinkedIn posts related to a hashtag and turn them into a clean, easy-to-read list you can use right away. Hashtag Post Finder is built for marketers, recruiters, researchers, and business teams who want to quickly collect LinkedIn post data without manual searching. Enter a hashtag, choose how many results you want, and get a combined set of matching posts with progress handled automatically. It is a simple LinkedIn post extractor for hashtag-based social listening, content research, and trend tracking. ### Key Features - Extract LinkedIn posts related to any hashtag you choose. - Collect only the number of results you need, from a few posts to a larger batch. - Automatically load more results until your requested limit is reached or no more posts are available. - Sort results in the order that best fits your workflow, such as the newest posts first. - Deliver a clean, readable list that is easy to review, share, and export. - Show friendly progress updates so you always know the actor is working. - Return a single combined result set, making analysis faster and simpler. ### Use Cases #### Social Listening Marketing teams use this actor to monitor what people are saying around a hashtag such as #ai, #hiring, or #leadership. The returned post details help teams spot recurring themes, audience sentiment, and timely topics worth responding to. This makes it easier to shape messaging based on real conversations instead of guesswork. #### Content Research Content creators and brand managers can review recent posts tied to a hashtag to see what formats, angles, and topics are getting attention. By looking at the post details in one place, they can identify ideas for future posts, newsletters, or campaigns. This helps teams create content that feels relevant and timely. #### Recruiting and Employer Branding Recruiters can search hashtags connected to hiring, careers, or specific industries to find posts that reveal what candidates and professionals are discussing. The post list can support outreach planning, employer brand monitoring, and talent market awareness. It is especially useful when you want to understand how your company or industry is being talked about on LinkedIn. #### Competitive Intelligence Business analysts can track hashtags related to competitors, product categories, or industry trends to see what is gaining traction. The returned posts help teams compare messaging, identify active voices in the market, and notice emerging opportunities. This supports smarter decisions about positioning and timing. #### Trend Tracking Founders, consultants, and strategy teams can use the actor to follow fast-moving topics over time. By collecting posts around a hashtag, they can see which ideas are growing and which conversations are fading. That makes it easier to react quickly to market changes and stay ahead of the curve. #### Lead and Partner Discovery Sales and partnership teams can search hashtags used by target audiences, communities, or industry groups to find relevant conversations. The post details can reveal active people, organizations, and discussion themes that may be worth following up on. This creates a practical starting point for outreach and relationship building. ### Input | Field | Type | Required | Description | Example | |---|---|---:|---|---| | sortBy | string | No | Choose how the results should be ordered. | mostRecent | | hashtag | string | No | Enter the hashtag you want to search for. You can include or leave out the # symbol. | ai | | maxResults | integer | No | Choose how many posts you want returned in total. The actor will keep loading more results until it reaches this number or there are no more results. | 10 | | max_results | integer | No | Legacy version of the result limit, kept for older setups that still use this field. | 10 | ### Output | Field | Type | Description | |---|---|---| | tag | string | The hashtag that was searched for, shown in a simple form so you can quickly confirm the topic. | | start | string | The starting point used for the returned set, helpful for understanding where the results began. | | sort by | string | The order used for the results, so you know whether newer or differently ranked posts were shown first. | | next page token | string | A marker that indicates whether more posts may be available if you want to continue later. | ### Sample Output ```json { "tag": "ai", "start": "0", "sort by": "most recent", "next page token": "" } ```` ### How It Works 1. You enter the hashtag you want to search for and choose how many posts you want. 2. The actor starts collecting matching LinkedIn posts and shows friendly progress while it runs. 3. If more posts are available, it keeps loading them automatically until it reaches your limit. 4. The results are cleaned up and combined into one easy-to-review set. 5. If no matching posts are found, you get a clear message instead of an empty or confusing result. ### Getting Started Getting started is simple and does not require coding knowledge. Click **Try for free**, enter your hashtag, choose your preferred sort order, and set the number of results you want. Then click **Run** and let the actor do the rest. When it finishes, you can view and download the results in **JSON, CSV, or Excel** format. ### Frequently Asked Questions #### Do I need technical skills? No. This actor is designed for business users who want results without dealing with complicated setup. Just enter a hashtag, choose how many posts you want, and run it. #### How fast does it run? Speed depends on how many posts you request and how many matching results are available. Smaller searches usually finish quickly, while larger searches may take longer because the actor keeps loading more results automatically. #### What format is the output? You can work with the results in formats that are easy to review and share, including JSON, CSV, and Excel. That makes it simple to use the data in reports, spreadsheets, or internal workflows. #### Is this legal to use? This actor is intended for lawful business use, such as research, monitoring, and analysis of publicly available content. You should always make sure your use follows applicable laws, platform rules, and your organizationโ€™s policies. #### Can I schedule it to run automatically? Yes, you can set it up to run on a schedule if you want regular updates. This is useful for ongoing monitoring of hashtags, trends, competitors, or campaign performance. # Actor input Schema ## `sortBy` (type: `string`): Choose how the results should be ordered. ## `hashtag` (type: `string`): Enter the hashtag you want to search for. You can include or leave out the # symbol. ## `max_results` (type: `integer`): Choose how many posts you want returned in total. The actor will keep loading more results until it reaches this number or there are no more results. ## Actor input object example ```json { "sortBy": "mostRecent", "hashtag": "ai", "max_results": 10 } ``` # 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 = { "sortBy": "date_posted", "hashtag": "ai", "max_results": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("datamagnet/linkedin-hashtag-post-finder").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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 = { "sortBy": "date_posted", "hashtag": "ai", "max_results": 10, } # Run the Actor and wait for it to finish run = client.actor("datamagnet/linkedin-hashtag-post-finder").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "sortBy": "date_posted", "hashtag": "ai", "max_results": 10 }' | apify call datamagnet/linkedin-hashtag-post-finder --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=datamagnet/linkedin-hashtag-post-finder", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Hashtag Post Finder", "description": "Find and return social posts matching a hashtag in a simple, easy-to-read format.", "version": "0.0", "x-build-id": "fnguBhVaXP5O7LWHQ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/datamagnet~linkedin-hashtag-post-finder/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-datamagnet-linkedin-hashtag-post-finder", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/datamagnet~linkedin-hashtag-post-finder/runs": { "post": { "operationId": "runs-sync-datamagnet-linkedin-hashtag-post-finder", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/datamagnet~linkedin-hashtag-post-finder/run-sync": { "post": { "operationId": "run-sync-datamagnet-linkedin-hashtag-post-finder", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "sortBy", "hashtag" ], "properties": { "sortBy": { "title": "Sort by", "type": "string", "description": "Choose how the results should be ordered." }, "hashtag": { "title": "Hashtag", "type": "string", "description": "Enter the hashtag you want to search for. You can include or leave out the # symbol." }, "max_results": { "title": "Max results", "type": "integer", "description": "Choose how many posts you want returned in total. The actor will keep loading more results until it reaches this number or there are no more results." } } }, "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 } } } } } } } } } } ```