Google News brand monitoring from search queries

Use this page when you do not have publisher feed URLs yet and want recent headlines for companies or themes. Google News Scraper is the existing Store Actor for that query-based discovery step. It is not a new Actor, not known-publisher feed XML, and not an article-body extractor. Pass search terms in queries.

Live Store price: $0.003 per successfully parsed public RSS article row ($3.00 / 1,000 Google News RSS articles). Cheapest first paid cap: 1 query × maxItems 1 = $0.003 if one article row is parsed. There is no Actor start event on the current Store pricing tab. Platform usage is listed as Included.

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Open taroyamada/google-news-scraper on the Apify Store · Google News Scraper landing

What a brand-mention row contains

The published README field list is title (headline), link (direct article URL), source (publisher name), pubDate, pubDateISO, description (short Google News snippet), and query (the search term that surfaced the row). There is no published full-article text field. deduplicate defaults to true and removes duplicate article URLs across queries.

The published README use cases include PR media mentions, competitive-intelligence newsroom watchlists, content-ops story discovery, and a steady article-URL feed for downstream extraction. The Actor does not score sentiment. Localized topic monitoring uses language and country; see localized Google News headlines.

Tiny first-paid input

The cheapest first paid cap is one query with maxItems 1. The published Store Quickstart is company news. Schema prefill is OpenAI and Google AI. The published Store example run uses queries artificial intelligence with maxItems 10. Live names are queries, maxItems, and deduplicate. Do not send README aliases such as maxItemsPerQuery or dedupeAcrossQueries.

{
  "queries": ["OpenAI"],
  "language": "en",
  "country": "US",
  "maxItems": 1,
  "deduplicate": true,
  "delivery": "dataset",
  "dryRun": false
}

If one public RSS article row is parsed, documented PPE is $0.003. There is no Actor start event. Start with broad terms for the first-run proof; prefer several narrower queries over one overloaded boolean query when relevance matters. Confirm the dataset shows real article URLs, not generic homepages. Store templates named in the README are Quickstart (company news), Brand Monitoring to track multiple companies or themes, and Google News → Article Cleanup when the next step is article extraction.

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Can Google News Scraper track brand and company mentions from search queries?

Yes, as a discovery step. Required input is queries (maximum 50 search terms). The Actor reads Google News RSS, not the Google News HTML interface, and returns title, link, source, pubDate, pubDateISO, description, and query. It does not return full article bodies, score sentiment, or crawl news sites. The published Store Brand Monitoring template is for tracking multiple companies or themes. Recurring monitoring is scheduling this Actor, then sending news, blog, and press links to Article Content Extractor.

What is the cheapest first paid input for Google News brand monitoring?

The cheapest first paid cap is one queries item with maxItems 1. If one public RSS article row is parsed, live PPE is $0.003. There is no Actor start event. The published Store Quickstart is company news. Live schema names are queries (required), language (default en), country (default US), maxItems (default 25, maximum 100 per query), deduplicate (default true), delivery dataset, and dryRun false. See Google News Scraper pricing.

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Why not send Google News rows straight into RAG or a sentiment model?

Each row is metadata plus a direct article URL. There is no published full-article text field. Discover URLs first, confirm they are real article pages rather than generic homepages, then send news, blog, and press links to Article Content Extractor. Docs, product, or policy pages belong on Website Content Extractor. For ingesting article bodies into RAG, see article extractor for news and blog RAG.

Open Google News Scraper Input on Apify

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