# Linkedin Articles Scraper (`scraperoka/linkedin-articles-scraper`) Actor 📌 LinkedIn Articles Scraper extracts high-quality LinkedIn Article data—titles, authors, dates, engagement & content snippets. ⚡ Perfect for B2B research, lead gen, competitive insights & content strategy. Built for accuracy & speed. - **URL**: https://apify.com/scraperoka/linkedin-articles-scraper.md - **Developed by:** [Scraperoka](https://apify.com/scraperoka) (community) - **Categories:** Lead generation, Social media, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.01 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 Articles Scraper 🎯 Manually visiting LinkedIn profiles to collect article text and engagement stats wastes hours you don’t have. **LinkedIn Articles Scraper** pulls structured article data from a profile in one click—an **LinkedIn article scraper** built for marketers, recruiters, and growth teams. Use this **LinkedIn article scraper** to scrape LinkedIn articles and extract text from LinkedIn content at speed, so you can turn profile reading into thousands of records in minutes. --- ### What You Get: Sample Output Here's a sample record from a single run: ```json { "profile_url": "https://www.linkedin.com/in/williamhgates", "articles": [ { "title": "Building Better Systems for Responsible AI", "link": "https://www.linkedin.com/pulse/building-better-systems-responsible-ai", "date": "2024-03-18", "description": "Thoughts on governance, evaluation, and practical deployment.", "image_url": "https://example.com/article-cover.jpg", "num_reactions": 1240, "num_comments": 86 }, { "title": "Why Product Teams Need Faster Feedback Loops", "link": "https://www.linkedin.com/pulse/why-product-teams-need-faster-feedback-loops", "date": "2023-11-02", "description": "A practical framework for measuring impact and iteration speed.", "image_url": "https://example.com/article-cover-2.jpg", "num_reactions": 842, "num_comments": 41 } ] } ```` | Field | Type | What It Tells You | |---|---|---| | `profile_url` | string | The LinkedIn profile URL that was scraped for article data | | `articles` | array | Up to 10 extracted article entries from the profile | | `title` | string | The article headline you can use for topic clustering and reporting | | `link` | string | A clean article URL (with query parameters removed) for citations or follow-ups | | `date` | string | null | The published date when available from the article metadata | | `description` | string | null | A short article description snippet (when present) for quick summaries | | `image_url` | string | null | Cover image URL when available, useful for creative previews | | `num_reactions` | number | Engagement signal to prioritize which articles resonate | | `num_comments` | number | Another engagement signal to gauge audience discussion | | `status` | string | null | Not returned by the actor; if you don’t see a field you expected, it’s simply not part of the dataset schema | | `error_message` | string | null | Not returned by the actor; failures are logged, and failed pages return no dataset record | Export your dataset as JSON, CSV, or Excel — straight from the Apify dashboard. *** ### Why LinkedIn Articles Scraper? There are a lot of ways to pull data from LinkedIn content — here's what sets LinkedIn Articles Scraper apart. #### Extracts Articles and Profile Context **LinkedIn Articles Scraper** returns a compact object with `profile_url` plus an `articles` array, so you always keep source context. That makes it ideal for LinkedIn article scraper workflows where you need attribution for downstream analysis. #### Engagement Signals Included You get `num_reactions` and `num_comments` alongside each article. This turns a “content grab” into actionable LinkedIn content scraper data you can rank, filter, and score. #### Output Capped for Cleaner Datasets The actor returns only the first 10 articles found for each profile (`articles[:10]`). This keeps your datasets focused and makes it easy to iterate during research sprints. #### Real-World Resilience The run includes request timeouts and structured handling so you don’t end up with messy partial records. If a page fails to fetch, the run logs the error and returns no result for that URL. *** ### Configuring Your Run Drop this into your `input.json` to get started: ```json { "url": "https://www.linkedin.com/in/williamhgates", "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"] } } ``` | Parameter | Required | What It Does | |---|---|---| | `url` | ✅ | Enter the LinkedIn profile URL to scrape articles from | | `proxyConfiguration` | ⬜ | Optional proxy settings for the run | | ↳ `proxy support` | ⬜ | When enabled, the run routes requests through Apify Proxy for reliability | | ↳ `proxy support` | ⬜ | Proxy group selection (the actor defaults to residential if not provided) | *** ### Core Capabilities #### LinkedIn Article Extraction for Research The actor scrapes article cards and builds structured entries containing `title`, `link`, `date`, `description`, `image_url`, and engagement counts. This is a practical option for teams building a scraper for LinkedIn articles and posts. #### Metadata-Aware Output When available, it uses article structured metadata to populate `date` and `num_reactions`. That improves consistency when you’re extracting text from LinkedIn articles and building reports. #### Built-In Handling for Missing Values If an article card doesn’t include a field, the actor returns `null` for optional fields like `description` (and may leave other fields absent or defaulted as implemented). This keeps your LinkedIn news article scraper datasets usable even when profiles vary. #### Upload-Ready Dataset Structure Results are pushed to a dataset with a table view that includes `profile_url` and `articles`. Each run produces records you can export immediately for analytics, CRM imports, or content research. #### Designed for Automation Because the actor takes `url` as input and writes results to the dataset, it’s straightforward to integrate into repeatable workflows. This makes it a solid LinkedIn article scraping API-style building block even when you run via the Apify UI. *** ### Who Gets the Most Out of This Here's how different teams put LinkedIn Articles Scraper to work: **Content Strategists** — They use scraped article titles, descriptions, and engagement signals to identify which themes are gaining traction, then build a weekly insight brief with sources linked back to each `link`. The result is faster topic research and better content planning with real audience response data. **Recruiters and Talent Sourcers** — They scrape articles from target profiles to understand candidates’ interests, expertise, and thought leadership. Instead of manually reading individual posts, they assemble a LinkedIn profile article scraper output they can reference in outreach and screening notes. **Sales and Partnerships Teams** — They gather article engagement stats to prioritize prospects and tailor messaging based on what the person actually publishes. With LinkedIn content scraper output, they turn profile browsing into a prioritized list for follow-ups. **Market Researchers** — They compare engagement across multiple profiles to spot trends in a niche over time. The structured `date`, `num_reactions`, and `num_comments` fields make it easier to build dashboards from exported datasets. **Automation Specialists** — They plug the actor into an automated pipeline to refresh content research outputs on demand. Since the actor returns consistent JSON objects with `profile_url` and `articles`, it’s easier to map into downstream storage or reporting. *** ### Step-by-Step: How to Use It No coding needed. Here's how to run LinkedIn Articles Scraper from start to finish: 1. **Open the actor on Apify** — visit [console.apify.com](https://console.apify.com) and open the actor page for LinkedIn Articles Scraper. 2. **Enter your inputs** — set `url` to the LinkedIn profile URL you want to scrape for articles. 3. **Configure proxy settings** — optionally enable `proxyConfiguration.proxy support` for more reliable runs. 4. **Hit Run and watch the live log** — monitor progress in the Apify console while the actor scrapes. 5. **View results in the dataset tab** — open the dataset to see `profile_url` and the `articles` array. 6. **Export as JSON, CSV, or Excel** — download the dataset in the format your workflow needs. The whole process takes under 5 minutes to set up. *** ### Integrations & Export Options Once your data is collected, LinkedIn Articles Scraper plugs directly into your existing workflow. You can export results from the Apify dataset tab in common formats like **JSON, CSV, and Excel**, which is perfect for analysts and marketers who want to jump straight into spreadsheets or BI tools. For automation, you can connect the actor run to downstream systems using Apify’s built-in capabilities and integrations, including **API access** (via the Apify API at https://apify.com/docs/api). For more event-driven workflows, you can use **webhooks** and common automation platforms like **Zapier** or **Make** to push results automatically after a run completes. You can also schedule recurring runs using Apify’s scheduled execution options. *** ### Pricing & Free Trial LinkedIn Articles Scraper runs on the Apify platform, which offers a **free tier** — no credit card required to get started. You’ll generally pay **pay-as-you-go** based on Actor compute usage (CU), with options for teams that run heavy workloads through Apify plans. For the most accurate pricing details, check the Apify pricing page in the marketplace listing and adjust based on your run volume. Start for free at [apify.com](https://apify.com) and scale when you’re ready. *** ### Reliability & Performance | What We Handle | How | |---|---| | Rate limit resilience | Includes built-in handling to keep scraping stable during a run | | Proxy support | Optional proxy configuration to improve reliability for repeated requests | | Fetch failures | If a page can’t be fetched successfully, the run logs the HTTP error and returns no result for that URL | | Timeouts | Network requests use a defined timeout to prevent hanging | | Data completeness | Each successful scrape produces a structured record with `profile_url` and an `articles` array | **Limitations:** The actor works with publicly accessible LinkedIn profile pages and extracts what’s available through the page structure and structured metadata. If a profile doesn’t present article cards as expected, the `articles` array may be empty. For enterprise-scale runs, contact us to discuss custom configurations. *** ### Frequently Asked Questions #### Is there a free plan or trial for LinkedIn Articles Scraper? Yes. Apify provides a **free tier** so you can run test jobs without a credit card, depending on current platform availability. #### Do I need to log in to LinkedIn to use this? No. The actor is designed to work from publicly accessible profile pages, so you don’t need to provide LinkedIn credentials. #### How accurate is the data extracted by LinkedIn Articles Scraper? It depends on what’s publicly available on each profile. The actor extracts article fields like `title`, `link`, `date`, `description`, `image_url`, and engagement counts when they appear on the page. #### How many results can I get per run? For each provided `url`, the actor returns up to **10** items in the `articles` array (`articles[:10]`). #### How often is the data updated / how fresh is it? Data freshness depends on when you run the actor. Each run scrapes the current page content at the time of execution and writes the results immediately to the dataset. #### Is this legal? Does it comply with GDPR / CCPA? Generally, the actor is limited to **publicly available data**, but compliance still depends on how you store, process, and use the results. You’re responsible for ensuring your workflow complies with applicable GDPR, CCPA, and platform terms. #### Can I export results to Google Sheets or Excel? Yes. You can export the dataset from the Apify dashboard and then import it into tools like Google Sheets or Excel using your preferred method. #### Can I run this on a schedule automatically? Yes. You can set up scheduled runs using Apify’s scheduling capabilities so the actor executes automatically on your chosen cadence. #### Can I access this via API? Yes. Apify supports API-based access to runs and outputs, letting you integrate LinkedIn article scraping API workflows into your systems. #### What happens if the actor hits an error? If a fetch fails or scraping throws an error, the actor logs the issue and returns no dataset record for that URL. Successful scrapes still push their structured results to the dataset. *** ### Need Help or Have a Request? Got a question about LinkedIn Articles Scraper or want a new feature added? Reach out at . We’re happy to help with setup questions, and we actively maintain this LinkedIn article scraper based on user feedback. If you want enhancements like batch CSV upload of profile URLs or webhooks on completion, tell us what your workflow needs. *** ### Disclaimer & Responsible Use *LinkedIn Articles Scraper is the fastest, most reliable way to structure LinkedIn article data for analysis—start your free run today.* This actor collects **publicly available data** from LinkedIn profiles and does not access private accounts, login-gated content, or password-protected pages. You are responsible for complying with GDPR, CCPA, and any applicable platform Terms of Service when using and storing the results. For data removal requests, contact . Use responsibly, ethically, and only for lawful purposes. # Actor input Schema ## `url` (type: `string`): Enter the LinkedIn profile URL to scrape articles from. ## Actor input object example ```json { "url": "https://www.linkedin.com/in/williamhgates" } ``` # 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 = { "url": "https://www.linkedin.com/in/williamhgates" }; // Run the Actor and wait for it to finish const run = await client.actor("scraperoka/linkedin-articles-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 = { "url": "https://www.linkedin.com/in/williamhgates" } # Run the Actor and wait for it to finish run = client.actor("scraperoka/linkedin-articles-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 '{ "url": "https://www.linkedin.com/in/williamhgates" }' | apify call scraperoka/linkedin-articles-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scraperoka/linkedin-articles-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Linkedin Articles Scraper", "description": "📌 LinkedIn Articles Scraper extracts high-quality LinkedIn Article data—titles, authors, dates, engagement & content snippets. ⚡ Perfect for B2B research, lead gen, competitive insights & content strategy. Built for accuracy & speed.", "version": "0.1", "x-build-id": "5D1LXeuH34lP294OG" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scraperoka~linkedin-articles-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scraperoka-linkedin-articles-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scraperoka~linkedin-articles-scraper/runs": { "post": { "operationId": "runs-sync-scraperoka-linkedin-articles-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scraperoka~linkedin-articles-scraper/run-sync": { "post": { "operationId": "run-sync-scraperoka-linkedin-articles-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "url" ], "properties": { "url": { "title": "Url to check", "type": "string", "description": "Enter the LinkedIn profile URL to scrape articles from." } } }, "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 } } } } } } } } } } ```