Brand Mention News Monitor

Monitor news mentions of any brand, company, or keyword via Google News RSS. A pay-per-use alternative to expensive media-monitoring SaaS tools like Meltwater, Mention, and Brand24 — no monthly subscription required.

What it does

This actor continuously monitors Google News for mentions of your specified brands, companies, or keywords. For each mention, it returns the article title, source, publish date, direct link, a text snippet, and an optional sentiment classification (positive / neutral / negative).

Key features:

• Multi-term monitoring — track up to 20 brands or keywords in a single run
• Only-new mode — onlyNew: true remembers what previous runs already returned, so scheduled runs deliver only fresh mentions instead of the same articles every time
• Date filter — return only mentions published on or after a given date
• Sentiment filter — return only positive, neutral, or negative mentions (e.g. crisis watch)
• Language & region targeting — monitor news in any Google News locale (en-US, nl-NL, de-DE, fr-FR, …)
• Sentiment analysis — keyword-based classification in English, Dutch, German, and French
• Deduplication — automatic within-run dedup across search terms, plus optional cross-run dedup
• Structured output — clean JSON per mention, ISO-8601 timestamps, ready for dashboards, alerts, or AI-agent pipelines

Why use this instead of Meltwater/Mention/Brand24?

	Meltwater	Mention	Brand24	This actor
Price	$15K–60K/year	$49+/month	$99+/month	Pay-per-use (~$0.005/mention)
Commitment	Annual	Monthly	Monthly	None
Scheduled "new mentions only"	Enterprise	Limited	Limited	Yes, built in
API access	Enterprise only	Limited	Limited	Full dataset access
AI-agent ready	No	No	No	Yes (structured JSON)

Input

Field	Type	Required	Default	Description
searchTerms	string[]	Yes	—	Brand names, company names, or keywords to monitor (max 20)
language	string	No	"en-US"	Language/region code for Google News (e.g. en-US, nl-NL, de-DE, fr-FR)
maxResultsPerTerm	integer	No	50	Maximum mentions per search term (1–100)
includeSentiment	boolean	No	true	Add sentiment classification to each mention
onlyNew	boolean	No	false	Skip mentions already returned by a previous run (persisted in a key-value store)
publishedAfter	string	No	—	Only mentions published on or after this date (ISO 8601, e.g. 2026-09-21)
sentimentFilter	string	No	—	Only mentions with this sentiment: positive, neutral, or negative

Example input

{
  "searchTerms": ["Tesla", "Elon Musk"],
  "language": "en-US",
  "maxResultsPerTerm": 50,
  "includeSentiment": true,
  "onlyNew": true
}

Output

Each result is a structured JSON object:

{
  "searchTerm": "Tesla",
  "title": "Tesla Reports Record Quarterly Deliveries",
  "url": "https://news.google.com/rss/articles/CBMi...",
  "source": "Reuters",
  "sourceUrl": "https://www.reuters.com",
  "publishedAt": "2026-09-28T10:00:00.000Z",
  "snippet": "Tesla reported record quarterly deliveries on Monday...",
  "sentiment": "positive"
}

Field	Type	Description
searchTerm	string	The search term that matched this mention
title	string	Article headline
url	string	Direct link to the article (Google News redirect)
source	string	News source name (e.g. "Reuters", "GetLatka")
sourceUrl	string	URL of the news source
publishedAt	string	Publication date, ISO 8601 in UTC
snippet	string	Article snippet / summary text
sentiment	string | null	"positive", "neutral", "negative", or null when includeSentiment is false

Use Cases

• PR & communications teams — track brand coverage across news media in real time
• Competitive intelligence — monitor competitor mentions alongside your own brand
• Crisis monitoring — sentimentFilter: "negative" returns only adverse coverage
• Investor relations — track news sentiment around publicly traded companies
• AI-agent pipelines — feed structured mention data into LLM-based alerting or summarization workflows
• SEO & content strategy — identify which news outlets cover your brand most frequently

Scheduling tip

For a daily "what's new about my brand" report, use onlyNew: true together with an Apify schedule:

{ "searchTerms": ["YourBrand"], "onlyNew": true, "maxResultsPerTerm": 50 }

The actor stores hashes of up to 5000 previously returned articles in a key-value store, so each scheduled run only delivers mentions you have not seen before. Without onlyNew, every run returns the full current result window.

Pricing

Event	Price	Description
news-mention	$0.005	Per news mention delivered

Example cost: monitoring 3 brands with 50 results each = 150 mentions ≈ $0.75 per run.

Legal

This actor uses the public Google News RSS feed. It only stores article titles, snippets, and links — no full article text, no personal data. Google News RSS is intended for personal, non-commercial use in feed readers; commercial redistribution of the raw feed may be subject to Google's terms. The sentiment analysis is a lightweight keyword heuristic, not a professional NLP model.

FAQ

Q: How often should I run this actor? A: For active brand monitoring, run it daily or every few hours via Apify's scheduler. Combine with onlyNew: true so each run only returns fresh mentions. Google News returns roughly the 100 most recent items per query.

Q: Does it support non-English news? A: Yes — set the language parameter to any Google News locale (e.g. nl-NL for Dutch, de-DE for German, fr-FR for French). Sentiment analysis supports English, Dutch, German, and French; for other languages it falls back to the English word lists.

Q: What is the sentiment analysis based on? A: A keyword-based heuristic using curated positive/negative word lists. It is intentionally lightweight and transparent — for production-grade sentiment, pipe the output through a dedicated NLP service. The sentiment field is advisory, not a measurement.

Q: Are the article links direct? A: Links are Google News redirect URLs (news.google.com/rss/articles/...) that resolve to the original article when opened. Google does not expose the publisher URL directly in the feed.

Q: Why do I sometimes get fewer results than maxResultsPerTerm? A: The feed returns a limited and shifting window per query, and filters (publishedAfter, sentimentFilter, onlyNew) reduce the result further. A term with little recent coverage legitimately returns fewer items.

Q: How is this different from the Apify Store's existing brand-mention actors? A: Existing actors focus on social platforms (Reddit, Xiaohongshu) or generic web mentions with negligible traction. This actor specifically targets news media via Google News RSS — the core use case of Meltwater/Mention/Brand24 — with scheduled "new mentions only" mode and structured output.

When should an AI agent use this?

1. "Monitor news mentions of [brand] and return the latest 20 articles with sentiment."
2. "Track competitor news for [competitor1], [competitor2], and [competitor3] in Dutch media."
3. "Fetch the latest news about [company] and classify each mention as positive, neutral, or negative."
4. "Return only negative news mentions about [brand] from the past week."
5. "Compare news sentiment for [brand A] vs [brand B] over the latest articles."
6. "Give me all mentions of [product] since 2026-09-01 in German-language news."

Related Actors

• Google Trends Search Interest Monitor — track search-interest trends for the same brand or keyword. Combine it with this actor to see whether news coverage and public search interest move together.
• Universal Price Monitor — monitor competitor product prices and stock. Useful alongside brand-mention monitoring for competitive-intelligence workflows.

Keywords

brand monitoring, news monitor, media monitoring, sentiment analysis, Google News, PR tool, Meltwater alternative, Mention alternative, Brand24 alternative, keyword tracking, news alerts, brand mentions, media mentions, news tracking, competitive intelligence

Changelog

• 2026-09-28 — Initial release. Google News RSS integration, multi-term monitoring, language/region targeting, EN/NL/DE/FR sentiment heuristic, ISO-8601 timestamps, within-run dedup, optional cross-run dedup (onlyNew), publishedAfter date filter, sentimentFilter, retry with backoff.