# Influencer Vetting — Engagement, Fake Followers & Brand Safety (`fuze/influencer-vetting`) Actor

Vet a list of creators before you pay them: real engagement rate, fake-follower risk, niche fit and brand-safety (adverse media) screening — one scored report per creator.

- **URL**: https://apify.com/fuze/influencer-vetting.md
- **Developed by:** [Fredrik Hansson](https://apify.com/fuze) (community)
- **Categories:** Social media, AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $44.00 / 1,000 creator vetteds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

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

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Influencer Vetting — Engagement, Fake Followers & Brand Safety

Before you pay a creator, know if they're worth it. Feed in a list of creators with their public metrics and get each one **scored**:

1. **Real engagement rate** — likes + comments vs followers, benchmarked per platform.
2. **Fake-follower risk** — LOW / MEDIUM / HIGH from engagement quality.
3. **Niche fit** — how well they match your brand/niche.
4. **Brand safety** — adverse-media and sanctions screening on their name, so you don't partner with someone risky.

One overall score + a clear recommendation per creator.

![Influencer Vetting — Engagement, Fake Followers & Brand Safety — input and sample output](https://datatools-hub.vercel.app/actor-shots/influencer-vetting.png)

### Quick start

1. **Fill in the input** — set the fields in the input tab (prefilled with a working example).
2. **Click Start** — the Actor runs and writes one record per item to the dataset.
3. **Get your data** — export to **JSON, CSV or Excel**, or pull it via the API.

### Example input

```json
{
  "creators": [
    { "name": "Jane Doe", "handle": "@janedoe", "platform": "instagram", "followers": 120000, "avgLikes": 3400, "avgComments": 120, "bio": "Fitness and wellness coach" }
  ],
  "niche": "sustainable activewear",
  "brandSafety": true
}
```

Pair it with any influencer/profile scraper for the raw `followers` / `avgLikes` / `avgComments`, or paste them in by hand.

### Output

One row per creator: `overallScore`, `engagementRate`, `fakeFollowerRisk`, `nicheFit`, `brandSafety`, `recommendation`.

### Note

Engagement and fake-follower scoring are heuristics based on the public metrics you provide — a strong signal, not a guarantee. Brand-safety screening is a fast aid, not a full background check.

Actively maintained.

# Actor input Schema

## `creators` (type: `array`):

One object per creator: { "name": "...", "handle": "@...", "platform": "instagram|tiktok|youtube", "followers": 120000, "avgLikes": 3400, "avgComments": 120, "bio": "..." }. followers + avg engagement power the fake-follower and engagement scoring.

## `niche` (type: `string`):

What your brand is about, e.g. 'sustainable activewear'. Used to score niche fit.

## `brandSafety` (type: `boolean`):

Screen each creator's name for adverse media and sanctions (so you don't partner with someone risky).

## `youtubeApiKey` (type: `string`):

If set, creators with platform 'youtube' and a handle are enriched automatically — real subscriber count and recent-video engagement are fetched, so you don't have to supply the metrics. Get a free key at console.cloud.google.com (YouTube Data API v3). For Instagram/TikTok, pair with any profile scraper or paste the metrics in.

## Actor input object example

```json
{
  "creators": [
    {
      "name": "Example Creator",
      "handle": "@example",
      "platform": "instagram",
      "followers": 120000,
      "avgLikes": 3400,
      "avgComments": 120,
      "bio": "Fitness and wellness coach"
    }
  ],
  "niche": "fitness and wellness",
  "brandSafety": true
}
```

# Actor output Schema

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

All rows in the default dataset.

# 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 = {
    "creators": [
        {
            "name": "Example Creator",
            "handle": "@example",
            "platform": "instagram",
            "followers": 120000,
            "avgLikes": 3400,
            "avgComments": 120,
            "bio": "Fitness and wellness coach"
        }
    ],
    "niche": "fitness and wellness"
};

// Run the Actor and wait for it to finish
const run = await client.actor("fuze/influencer-vetting").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 = {
    "creators": [{
            "name": "Example Creator",
            "handle": "@example",
            "platform": "instagram",
            "followers": 120000,
            "avgLikes": 3400,
            "avgComments": 120,
            "bio": "Fitness and wellness coach",
        }],
    "niche": "fitness and wellness",
}

# Run the Actor and wait for it to finish
run = client.actor("fuze/influencer-vetting").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 '{
  "creators": [
    {
      "name": "Example Creator",
      "handle": "@example",
      "platform": "instagram",
      "followers": 120000,
      "avgLikes": 3400,
      "avgComments": 120,
      "bio": "Fitness and wellness coach"
    }
  ],
  "niche": "fitness and wellness"
}' |
apify call fuze/influencer-vetting --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,fuze/influencer-vetting"
        }
    }
}
```

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/gGZSwVD7gzzX2J2Lk/builds/47towb2ajMtBUW0e7/openapi.json