Brand Mention Monitor — Brandwatch Alternative

What is Brand Mention Monitor — Brandwatch Alternative?

Brand Mention Monitor collects public mentions of a brand and its competitors from Google News, Hacker News, Reddit RSS and any public feeds you add, and returns a dataset of deduplicated mentions. Each row is one mention with its title, text excerpt, source and source URL, author when public, publish and retrieval times, the terms that matched, a brand-or-competitor label, a keyword-based sentiment label and score, and an urgency flag. Growth, communications and product-marketing teams and AI agents use it to build a reviewable queue of what people are saying about a brand this week.

Why teams use it

• Brand and competitors in one run. competitors are queried separately and each row is labeled brand or competitor in subject.
• Triage signals built in. sentiment, sentimentScore and urgency help a reviewer start with the mentions that matter most.
• News plus community. Google News, Hacker News and Reddit RSS cover press and developer conversation; feedUrls adds trade or community feeds.
• Clear provenance. sourceUrl, sourceQuery, feedUrl, publishedAt and retrievedAt show where and when each mention was found.
• Clean, bounded output. Duplicates across sources are merged and maxResults caps the whole run.

Best fit for this Actor

• Designed for a source-linked snapshot of public conversation around one brand, product or organization and a short list of competitors.
• Best fit for weekly brand-health reviews, launch monitoring and competitor share-of-voice spot checks in news and developer communities.
• When you want recurring news alerts that remember what was already delivered, continue with News & RSS Monitor for the same brand terms.
• To see how the brand appears in AI-generated answers, then use AI Brand Monitor as the next step.
• For deeper Reddit threads on the mentions you find, pass the brand to Reddit Search Scraper.

Example workflow: launch-week brand watch

A product-marketing manager is launching a new feature. On launch day she starts with brandName set to her product, one alias in brandAliases, two rivals in competitors, lookbackDays at 7 and maxResults at 50. The dataset returns news articles and Hacker News threads with sentiment and urgency. She sorts by urgency, replies to the high-urgency community threads first, compares the count of brand versus competitor rows to gauge share of voice, then schedules the same input daily for the launch week and exports each run to the team's review sheet.

Input

{
  "brandName": "Apify",
  "brandAliases": ["Apify Store"],
  "competitors": ["Bright Data"],
  "sources": ["googleNews", "hackerNews", "reddit"],
  "feedUrls": [],
  "language": "en-US",
  "country": "US",
  "lookbackDays": 7,
  "maxResults": 20,
  "excludeKeywords": ["sponsored"],
  "responseFormat": "concise"
}

Field	Type	What it controls
brandName	string	Required. The public brand, product, organization or topic to monitor.
brandAliases	array	Product names or former names that also count as the brand.
competitors	array	Competitor names queried separately and labeled competitor.
sources	array	Any of googleNews, hackerNews, reddit. Default all three.
feedUrls	array	Extra public RSS, Atom or JSON Feed URLs, filtered to your terms.
language, country	string	Google News locale and edition, such as en-GB and GB. Defaults en-US and US.
lookbackDays	integer	Collection window, 1 to 30 days. Default 7.
maxResults	integer	Mentions saved across the whole run, 1 to 100. Default 20.
excludeKeywords	array	Literal terms that remove a mention from titles, text or queries.
responseFormat	string	concise (default) or detailed for a longer text excerpt.

Output

One dataset row per deduplicated public mention.

Field	What it contains
recordType, id	Row type (mention) and stable mention ID
subject, brandName, matchedTerms	brand or competitor, the name it belongs to and the terms that matched
source, sourceUrl, sourceDomain	Source such as Hacker News or Google News, item URL and domain
title, text, author	Headline or thread title, text excerpt and public author when present
publishedAt, retrievedAt	Source publish time and collection time
sentiment, sentimentScore, urgency	Keyword-based sentiment label and score, and urgency flag
sourceQuery, feedUrl	The query and feed that produced the row
responseFormat, language	Excerpt setting and request locale

{
  "recordType": "mention",
  "id": "234a0881ca4962959db9be7f",
  "subject": "brand",
  "brandName": "Apify",
  "matchedTerms": ["Apify"],
  "source": "Hacker News",
  "title": "Show HN: MCP Security Auditor – Zero-execution static scanner for MCP servers",
  "text": "Share the benchmark findings on reference implementations versus community servers.",
  "sourceUrl": "https://apify.com/neon_innovation_lab/mcp-security-auditor",
  "sourceDomain": "apify.com",
  "author": "hn_builder_42",
  "publishedAt": "2026-09-04T11:19:48.000Z",
  "retrievedAt": "2026-09-06T21:13:28.636Z",
  "sentiment": "negative",
  "sentimentScore": -0.25,
  "urgency": "medium",
  "sourceQuery": "Apify",
  "feedUrl": null,
  "responseFormat": "concise",
  "language": "en-US"
}

Each run also writes OUTPUT (the terminal outcome) and RUN_SUMMARY (per-source counts and notes) to the default key-value store.

Pricing

This Actor uses Pay per event with Apify platform usage included in the event price. The events are:

• Actor Start, once per run.
• Public mention, once for each validated, deduplicated mention saved to the dataset.

Worked example: a brand plus one competitor across three sources with maxResults at 20 saves up to 20 mentions, so the run costs one Actor Start event plus up to 20 Public mention events. maxResults caps the mention charges for every run. Check the live Pricing tab on this page for current event prices.

API example

curl -X POST "https://api.apify.com/v2/acts/khadinakbar~brandwatch-alternative/run-sync-get-dataset-items?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"brandName": "Apify", "competitors": ["Bright Data"], "sources": ["googleNews", "hackerNews"], "lookbackDays": 7, "maxResults": 20}'

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")
run = client.actor("khadinakbar/brandwatch-alternative").call(
    run_input={"brandName": "Apify", "competitors": ["Bright Data"], "lookbackDays": 7, "maxResults": 20}
)
for mention in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(mention["subject"], mention["source"], mention["urgency"], mention["title"], mention["sourceUrl"])

Use it with AI agents (MCP)

Connect the Actor to Claude, ChatGPT, Cursor or another MCP client through Apify MCP, then ask:

Monitor public mentions of Apify and Bright Data from the last seven days, return up to 20 rows, and summarize the high-urgency mentions with their source URLs and collection times.

The agent reads mention rows with get-dataset-items, groups them by subject, sentiment and urgency, cites sourceUrl and retrievedAt as provenance, and reads OUTPUT for the run outcome. Sentiment is a keyword-based triage cue, and maxResults caps cost and scope.

Best results

Starting situation	Helpful action	Expected outcome
A brand name that is also a common word	Add precise aliases and excludeKeywords	Fewer off-topic mentions
Share-of-voice checks	List two or three competitors	Labeled rows to compare by subject
A trade publication that matters	Add its public feed to feedUrls	That publication in every snapshot
A non-US market	Set language and country, such as de and DE	The matching Google News edition
A busy review team	Keep maxResults close to what the team can read	A queue sized to real capacity

Builder's note

I built this for the step between "what are people saying about us?" and a list a person can actually review. I designed every row around provenance, with the source URL, the query that found it, the publish time and the collection time, so each finding can be checked in one click. Sentiment and urgency are deterministic keyword cues on purpose, because I found reviewers trust a transparent triage signal that sorts the queue while they make the final call.

FAQ

Is the sentiment AI-generated?

It is a transparent keyword-based score and label, designed to order a review queue.

Which sources does it read?

Google News RSS, Hacker News and Reddit RSS, plus any public feeds you add in feedUrls.

Can I monitor several competitors?

Yes. Add up to five names in competitors; each mention is labeled so you can compare them.

Responsible use

This Actor reads public sources you select. Mentions can include usernames and other personal data, so use the results with a lawful basis, respect site terms and privacy laws, and keep source URLs and collection times with any report. Brandwatch is a trademark of its owner, and this independent Actor is not affiliated with or endorsed by Brandwatch. For questions, open a ticket in the Issues tab.