TechCrunch Articles Scraper — Search by Keyword

Extract comprehensive tech news article data from TechCrunch with the 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 TechCrunch Articles 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 TechCrunch Keyword Search Works

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"
  }
]

You can download the dataset in various formats such as JSON, HTML, CSV, or Excel.

Output Data Fields

Field	Type	Description
post_id	String	Unique TechCrunch post identifier
post_title	String	Article headline
post_url	String	Full URL to the article
publication_date	String	Publication date (YYYY-MM-DD)
publication_datetime	String	Publication datetime (ISO 8601)
author_info	Object	Author name and profile URL
category_info	Object	Category name and URL
search_term	String	Keyword used to find this article
extraction_date	String	Date the data was collected
extraction_datetime	String	Full timestamp of data collection

How to Use TechCrunch Article Scraper

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);
   

Use Cases for TechCrunch Data Scraping

• 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

TechCrunch API Alternative

This scraper works as a practical alternative to a TechCrunch API for developers and analysts who need programmatic access to TechCrunch article data. It provides structured JSON output, supports keyword-based search, and integrates with the Apify platform for scheduling, monitoring, and webhook notifications — without requiring any official API credentials.

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
• Error handling: Robust error handling for network issues and missing data

Support

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

Credits

Developed and maintained by the DataCach team.