📰 Google News Scraper

Discover fresh article URLs from Google News RSS for topic monitoring, PR workflows, and content-intelligence pipelines. This actor is a discovery surface in the Content Intelligence Pack: use it to find news URLs, then hand those URLs to Article Content Extractor for full-body cleanup.

Store Quickstart

• Start with Quickstart (company news) for a reliable first run.
• Use Brand Monitoring to track multiple companies or themes.
• Use Google News → Article Cleanup when your next step is article extraction.

Where this actor fits

Surface	Best for
Google News Scraper	Discover current article URLs by query
Article Content Extractor	Clean the discovered article/news/blog pages
Website Content Extractor	Clean discovered non-article pages
RSS Feed Aggregator	Discover fresh URLs from known publishers and blogs

Key Features

• 🔎 Query-based discovery — Pull article URLs from Google News RSS without a paid API
• 🌍 Localized results — Tune by language and country
• 🔄 Deduplication — Remove duplicate URLs across multiple queries
• 📰 Publisher context — Keep headline, source, description, and publish date
• ⚡ Fast feeder step — Lightweight discovery before deeper extraction

Use Cases

Who	Why
PR teams	Find the latest media mentions to hand off for cleanup
Competitive intelligence	Build newsroom watchlists from search queries
Content ops	Discover trending stories before enrichment
AI / RAG teams	Create a steady article URL feed for downstream extraction

Input

Field	Type	Default	Description
queries	string[]	required	Search queries (max 50)
language	string	en	Google News language code
country	string	US	Google News country code
maxItems	integer	25	Max articles per query
deduplicate	boolean	true	Remove duplicate links across queries
timeoutMs	integer	15000	Request timeout
delivery	string	dataset	dataset or webhook
webhookUrl	string	—	Webhook target when delivery=webhook
dryRun	boolean	false	Run without saving

Input Example

{
  "queries": ["OpenAI", "Google AI"],
  "language": "en",
  "country": "US",
  "maxItems": 20,
  "deduplicate": true
}

Output

Field	Type	Description
title	string	Article headline
link	string	Direct article URL for downstream cleanup
source	string	Publisher name
pubDate	string	Original RSS publish date
pubDateISO	string	ISO timestamp version of pubDate
description	string	Short Google News snippet
query	string	Search query that surfaced the row

Output Example

{
  "title": "Codex for (almost) everything",
  "link": "https://openai.com/index/codex-for-almost-everything",
  "source": "OpenAI",
  "pubDate": "Thu, 16 Apr 2026 10:00:00 GMT",
  "pubDateISO": "2026-04-16T10:00:00.000Z",
  "description": "The updated Codex app for macOS and Windows adds computer use...",
  "query": "OpenAI"
}

First-run buyer experience

1. Run Quickstart (company news).
2. Confirm the dataset shows real article URLs, not generic homepages.
3. Pick the top URLs and send them to Article Content Extractor.
4. If a discovered URL is actually a docs/product/policy page, clean it with Website Content Extractor instead.

Tips & Limitations

• Use broad queries for the first run; refine later.
• RSS is a discovery layer only — it does not return full article bodies.
• Combine multiple narrower queries instead of one overloaded boolean query when relevance matters.

FAQ

Can I get full article text here?

No. This actor discovers URLs and returns metadata only. Use Article Content Extractor for full content.

Why use this instead of scraping the Google News UI?

The RSS surface is lighter, more stable, and better suited for recurring discovery runs.

Can I schedule recurring news monitoring?

Yes — run it on a schedule, then pass the discovered URLs into an article-cleanup step.

Related Actors

Content Intelligence Pack handoffs:

• 📰 Article Content Extractor — clean newsroom and blog article pages
• 📄 Website Content Extractor — clean discovered non-article pages
• 📡 RSS Feed Aggregator — discover fresh URLs from known publisher feeds

Cost

Pay Per Event:

• actor-start: $0.01
• dataset-item: $0.003 per output item

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