📰 TechCrunch Articles Search Scraper by Keyword

Extract comprehensive tech news article data from TechCrunch with our powerful TechCrunch Article Search Scraper. Search articles by keyword to unlock valuable insights into technology trends, startup news, venture capital, AI developments, and industry analysis. Perfect for tech news monitoring, content research, competitive intelligence, and trend analysis. Pay-per-result pricing ensures you only pay for the data you receive.

✨ What does this scraper do?

• Search TechCrunch articles by keyword across titles, descriptions, and content
• Extract comprehensive article data including titles, URLs, publication dates, authors, categories, and metadata
• Track publication information including publication date, datetime, and article IDs
• Extract author details including author names and profile URLs
• Capture category information including category names and URLs
• Support multiple keywords for comprehensive coverage across different tech topics
• Control result limits with configurable maximum results per keyword
• Return structured JSON data ready for analysis, integration, or further processing

🔍 How does it work?

1. Input:
   The actor receives an input with:

   • One or more search keywords (e.g., "artificial intelligence", "startup funding", "tech IPO", "cybersecurity", "blockchain", "venture capital")
   • Maximum number of results per keyword (from 1 to 100,000)

2. Process:
   For each keyword:

   • Connects to TechCrunch's search engine and performs keyword-based searches
   • Extracts article data from search results including titles, URLs, publication dates, authors, and categories
   • Handles pagination automatically to collect results across multiple pages
   • Returns structured data up to the specified maximum results limit per keyword
   • Each keyword is processed independently with its own result limit

3. Output:

   • A JSON dataset containing detailed article information, including:
     • Article identification (post ID, title, URL)
     • Publication metadata (publication date, publication datetime)
     • Author information (author name, author profile URL)
     • Category details (category name, category URL)
     • Search metadata (search term used, extraction date and timestamp)

Example input

{
  "keywords": [
    "artificial intelligence",
    "startup funding",
    "venture capital"
  ],
  "max_results": 100
}

Important: The max_results parameter applies to each keyword independently. If you set max_results to 100 and provide 3 keywords, you can receive up to 300 total results (100 per keyword).

Example output

[
  {
    "post_id": "3042478",
    "post_title": "Scale AI's former CTO launches AI agent that could solve big data's biggest problem",
    "post_url": "https://techcrunch.com/2025/09/05/scale-ais-former-cto-launches-ai-agent-that-could-solve-big-datas-biggest-problem/",
    "publication_date": "2025-09-05",
    "publication_datetime": "2025-09-05T08:00:00-07:00",
    "category_info": {
      "name": "Startups",
      "url": "https://techcrunch.com/category/startups/"
    },
    "author_info": {
      "name": "Julie Bort",
      "url": "https://techcrunch.com/author/julie-bort/"
    },
    "extraction_date": "2025-12-02",
    "extraction_datetime": "2025-12-02T00:37:44.252918+00:00",
    "search_term": "big data"
  }
]

📚 How to use this actor

1. In Apify Console:

   • Click "Run" and provide the input in JSON format as shown in the example above
   • The actor will automatically handle search execution, pagination, and data extraction
   • Results will appear in the Dataset tab as they are collected

2. From code (Python/Node.js):
   You can run the actor using the Apify SDK or by making an HTTP request to the Apify API.

   Python example:

   from apify_client import ApifyClient
   
   client = ApifyClient("YOUR_API_TOKEN")
   run = client.actor("your-actor-id").call(run_input={
       "keywords": ["artificial intelligence", "startup funding"],
       "max_results": 100
   })
   
   # Fetch results from dataset
   for item in client.dataset(run["defaultDatasetId"]).iterate_items():
       print(item)
   

   Node.js example:

   const { ApifyClient } = require('apify-client');
   
   const client = new ApifyClient({
       token: 'YOUR_API_TOKEN',
   });
   
   const run = await client.actor('your-actor-id').call({
       keywords: ['artificial intelligence', 'startup funding'],
       max_results: 100
   });
   
   // Fetch results from dataset
   const { items } = await client.dataset(run.defaultDatasetId).listItems();
   console.log(items);
   

🗒️ Technical notes

• Web scraping: Uses TechCrunch's search engine for efficient article discovery and data extraction
• Automatic pagination: Handles multiple pages of results automatically to collect comprehensive data
• Per-keyword result limiting: Each keyword is processed independently with its own result limit
• Structured output: Returns clean JSON data with consistent field names and data types
• Metadata tracking: Includes search term and extraction timestamps for data lineage
• Author and category extraction: Captures detailed author and category information for each article
• Publication date tracking: Extracts both date and datetime information for precise temporal analysis
• Deduplication: Ensures unique article entries in the output dataset
• Error handling: Robust error handling for network issues and missing data

💡 Use cases

• Tech News Monitoring: Track technology trends, startup news, and industry developments
• Content Research: Research articles for content creation, competitive analysis, or market research
• Competitive Intelligence: Monitor competitor mentions, product launches, and industry coverage
• Trend Analysis: Identify emerging technologies, trending topics, and industry shifts
• Author Tracking: Follow specific authors and their coverage areas
• Category Analysis: Analyze coverage within specific tech categories (AI, startups, cybersecurity, etc.)
• Media Monitoring: Track brand mentions, product launches, and company news
• Data Integration: Feed article data into analytics platforms, content management systems, or custom applications
• SEO Research: Analyze article titles, keywords, and publication patterns for SEO insights
• Academic Research: Collect data for technology and business research studies

💬 Support

If you need any help please contact us as soon as possible via email or any contact channel that Apify provides.

🔌 TechCrunch API Alternative

Looking for a TechCrunch API or API de TechCrunch? This actor serves as a powerful alternative to official TechCrunch API endpoints, providing programmatic access to TechCrunch article data through a simple, RESTful interface. Whether you need a TechCrunch API integration, TechCrunch data API, or TechCrunch scraping API, this tool offers comprehensive article search and data extraction capabilities.

Key Features:

• TechCrunch API access without official API credentials
• RESTful API interface via Apify platform
• Structured JSON responses compatible with API integrations
• Real-time article data extraction and search functionality
• Scalable API usage with pay-per-result pricing model
• Multiple search parameters including keyword-based search across article content
• Comprehensive article metadata including authors, categories, publication dates, and URLs

Use this actor as your TechCrunch API alternative to build applications, integrations, and data pipelines that require access to tech news article information. Perfect for developers building TechCrunch API integrations, data analysts needing TechCrunch article API access, journalists requiring TechCrunch news API functionality, and businesses needing TechCrunch data API capabilities for market intelligence and competitive analysis.

Credits

Developed and maintained by DataCach team 🌟.