# Meta Brand Mention Monitor (`deepmine/meta-brand-mention-monitor`) Actor

Track Facebook ads mentioning your brand with Meta Ads Library monitoring. Detect new mentions, dropped mentions, mention type trends, and top advertisers referencing your brand over time. Built for brand protection, competitor monitoring, and trademark watch.

- **URL**: https://apify.com/deepmine/meta-brand-mention-monitor.md
- **Developed by:** [DeepMine](https://apify.com/deepmine) (community)
- **Categories:** Social media, Automation, SEO tools
- **Stats:** 3 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$25.00/month + usage

To use this Actor, you pay a monthly rental fee to the developer. The rent is subtracted from your prepaid usage every month after the free trial period. You also pay for the Apify platform usage, which gets cheaper the higher Apify subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#rental-actors

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Meta Brand Mention Monitor

### Facebook Ads Library Brand Monitoring, Competitor Ad Tracking, and Brand Mention Intelligence

Meta Brand Mention Monitor is a **Facebook Ads Library monitoring tool** built for teams that need clear, repeatable visibility into who is mentioning their brand in paid social ads.

If you need a practical **brand monitoring tool for Facebook ads**, this actor helps you track brand mentions, detect new competitor references, identify dropped mentions, and monitor long-running ads that continue using your brand terms.

This is a strong fit for:

- brand protection teams
- performance marketers
- media buying teams
- eCommerce operators
- affiliate compliance teams
- competitive intelligence analysts
- agencies managing multiple client brands

### Why Use a Facebook Ads Brand Mention Monitor?

Most teams only discover competitor brand mentions after spend has already been lost. A structured **Meta Ads Library brand monitoring workflow** gives you a faster signal loop.

With ongoing monitoring, you can:

- detect new brand mentions in active Meta ads
- identify advertisers repeatedly mentioning your brand
- monitor comparison ad pressure over time
- flag potential trademark-risk patterns for review
- measure mention trend changes instead of reacting late

For teams searching terms like **"facebook ad brand monitoring"**, **"meta ads competitor tracking"**, **"ads library brand mention tool"**, **"social ad intelligence"**, and **"brand protection monitoring"**, this actor is designed for that exact workflow.

### What This Actor Tracks

The actor focuses on **brand-term mention discovery in Meta Ads Library** and outputs two practical views:

1. `Mention Summary` for quick strategic decisions
2. `Mentions` for ad-level evidence and detail

It tracks:

- total mentions per tracked brand term
- new mention pressure since prior baseline
- dropped mention pressure since prior baseline
- top mention types and top mentioning advertisers
- ad age patterns and long-running mention campaigns

### Core SEO Use Cases

#### 1) Competitive Ad Monitoring

Use this actor as a **Meta ad competitor monitoring tool** to see which advertisers reference your brand name in active ads.

#### 2) Brand Protection and Trademark Watch

Use this actor as a **brand protection monitoring system** to detect potentially problematic references that may require legal or policy review.

#### 3) Affiliate and Partner Oversight

Use it as an **affiliate ad compliance monitor** to track whether partners are using approved brand language in paid social campaigns.

#### 4) Market Intelligence for Growth Teams

Use it as a **Facebook Ads Library intelligence tool** to understand how often your brand appears in comparison-style messaging.

#### 5) Agency Monitoring Workflows

Agencies can run scheduled checks for each client brand and use deltas to prioritize action.

### Delta Monitoring for Better Decision-Making

A key benefit of this actor is **change tracking over time**, not just one-off snapshots.

Important delta signals include:

- `isNew`: the mention ad was not in the prior baseline window
- `ageDelta`: ad age growth since the previous baseline
- `newMentionsSinceLastRun`: fresh mention ads discovered
- `droppedMentionsSinceLastRun`: previously seen mentions no longer present

This supports a better **brand mention trend analysis** workflow for Meta advertising.

### Consistency Safeguards

To reduce noisy false positives from rapid reruns, the actor includes consistency controls:

- confirmation logic for new and dropped mention states
- configurable minimum delta interval via `minDeltaWindowMinutes`

This improves reliability for teams running automated schedules and recurring reports.

### Mention Type Classification

Each matched ad is assigned a mention type to support prioritization:

- `comparison`
- `complaint`
- `endorsement`
- `neutral`

This allows quick triage between potentially aggressive competitor messaging and lower-priority neutral references.

### Output Tabs

### Mention Summary

One row per brand term, including:

- `query`
- `totalMentions`
- `newMentionsSinceLastRun`
- `droppedMentionsSinceLastRun`
- `battleTestedMentions30d`
- `gainingMentions14to30d`
- `newTestsUnder14d`
- `avgMentionAdAgeDays`
- `topMentionType`
- `topFormat`
- `topCTA`
- `topMentioningAdvertisers`
- `longestRunningMentionExcerpt`

### Mentions

Ad-level output with context, including:

- `query`
- `advertiser`
- `adArchiveId`
- `isNew`
- `ageDelta`
- `adAgeDays`
- `mentionType`
- `matchedFields`
- `matchedExcerpt`
- `body`
- `headline`
- `cta`
- `format`
- `pageName`
- `startDate`
- `linkUrl`
- `imageUrl`
- `videoUrl`

This structure makes it easy to move from trend detection to ad-level verification.

### Input Parameters

| Parameter | Type | Description |
|---|---|---|
| `brandTerms` | array of strings | Brand names, product names, or trademark terms to monitor in Meta Ads Library |
| `country` | string | Country code for ad search scope (default `US`) |
| `maxAdsPerAdvertiser` | integer | Upper cap on ad collection volume per term |
| `trackChanges` | boolean | Enables baseline comparison and delta metrics |
| `minDeltaWindowMinutes` | integer | Minimum time window for delta stability |
| `proxyConfiguration` | object | Proxy settings for resilient collection |

### Best Practices for Higher-Quality Brand Mention Monitoring

- Use exact official brand names first, then add common variants.
- Separate major spelling variants into distinct terms for clean reporting.
- Run on a schedule (for example daily or weekly) instead of random manual bursts.
- Review mention context in the `Mentions` tab before policy or legal escalation.
- Pair trend changes with advertiser patterns to prioritize response.

### Practical Workflows

#### Workflow A: Weekly Brand Protection Review

1. Run with core brand and product terms.
2. Sort by new mentions.
3. Review top mentioning advertisers.
4. Escalate suspicious cases for compliance review.

#### Workflow B: Competitor Intelligence Dashboard

1. Track multiple brand terms across your category.
2. Monitor changes in mention volume and mention type.
3. Identify recurring advertisers and persistent campaigns.
4. Feed findings into media strategy and messaging updates.

#### Workflow C: Agency Client Reporting

1. Set one run per client brand schedule.
2. Use `Mention Summary` for executive reporting.
3. Use `Mentions` for audit evidence and campaign notes.

### SEO-Relevant Problem Statements This Actor Solves

This actor is designed for teams searching for:

- Facebook Ads Library brand tracker
- Meta ad competitor monitoring tool
- brand mention monitor for paid social
- trademark monitoring in Facebook ads
- social ad intelligence for eCommerce brands
- Meta ads brand protection workflow
- competitor ad surveillance and mention tracking

If your objective is ongoing **brand intelligence from public Meta ad data**, this actor provides a clear operational format for that use case.

### Frequently Asked Questions

#### Does this scrape private data?

No. It uses publicly available Meta Ads Library records.

#### Is this only for large brands?

No. It works for small brands, startups, DTC stores, agencies, and enterprise teams.

#### Can I run this on a schedule?

Yes. Scheduled runs are recommended for stable deltas and trend tracking.

#### Is this useful for legal/compliance teams?

Yes. It helps surface and organize potential brand mention issues for review.

#### Can I use it for competitor analysis?

Yes. It is specifically useful for competitor mention discovery and trend tracking.

### Data Source and Compliance

- Source: Meta/Facebook Ads Library public transparency data
- No account login required for source visibility
- Output should be reviewed by your team before legal or policy action

### Related Actor Stack (Optional Expansion)

If you are building a broader Meta ad intelligence workflow, pair this with:

- category-level ad saturation tracking
- competitor ad trend monitoring
- advertiser-level creative monitoring

Together, these workflows support stronger paid social competitive intelligence and brand defense.

***

Meta Brand Mention Monitor is built for teams that need **reliable Facebook Ads Library brand mention tracking**, **Meta ads competitor visibility**, and **actionable brand protection insights** in a format that can be scheduled, reviewed, and operationalized.

# Actor input Schema

## `brandTerms` (type: `array`):

Brand names or trademark terms to monitor in ad copy. Add one per line. Each term is tracked independently — the tool shows new mentions, dropped mentions, and long-running mentions.

## `country` (type: `string`):

Country to search ads for. Uses ISO 2-letter country code. US covers the most advertisers.

## `maxAdsPerAdvertiser` (type: `integer`):

Maximum number of ads to collect per brand term. 200 is enough for most monitoring use cases.

## `trackChanges` (type: `boolean`):

The core monitoring feature. Each run saves a baseline. The next run compares against it and flags new mentions, dropped mentions, and age deltas.

Run on a schedule (daily or weekly) to continuously track brand mentions in Meta ads.

## `minDeltaWindowMinutes` (type: `integer`):

Suppresses new/dropped mention deltas if the previous run is too recent, reducing false churn from short-interval reruns.

## `proxyConfiguration` (type: `object`):

Proxy settings. Residential proxies are required — Meta's GraphQL pagination API rejects datacenter IPs. Only facebook.com API calls go through the proxy; CDN assets (JS bundles etc.) are fetched directly to minimise proxy bandwidth costs.

## Actor input object example

```json
{
  "brandTerms": [
    "Nike",
    "Adidas"
  ],
  "country": "US",
  "maxAdsPerAdvertiser": 200,
  "trackChanges": true,
  "minDeltaWindowMinutes": 30,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL"
    ]
  }
}
```

# Actor output Schema

## `results` (type: `string`):

No description

## `summary` (type: `string`):

No description

## `ads` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "brandTerms": [
        "Nike",
        "Adidas"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("deepmine/meta-brand-mention-monitor").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "brandTerms": [
        "Nike",
        "Adidas",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("deepmine/meta-brand-mention-monitor").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "brandTerms": [
    "Nike",
    "Adidas"
  ]
}' |
apify call deepmine/meta-brand-mention-monitor --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,deepmine/meta-brand-mention-monitor"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/oAj2EWEnewk6kG8SG/builds/0Umgl4GPs47dQb8rN/openapi.json