📊 Detect influencer fraud and fake followers with Instagram, X, TikTok and Claude

⚡ 44 views · 📊 Market Research & Insights

💡 Pro Tip — Since TikTok doesn’t have a public API, getting data usually means fragile HTTP scraping. ScraperNode is a community node that gives you clean JSON for TikTok profiles, videos, and comments — drop it into your workflow and skip the parsing.

Description

Analyzes influencer profiles and scores authenticity before brand partnership approval. Detects fake followers, bot accounts, and suspicious engagement patterns using AI-powered behavioral analysis.

🎯 How It Works

Simple 7-Node Workflow:

1. Input → Submit influencer username and platform (Instagram/Twitter/TikTok)
2. Fetch → Retrieve complete profile data and engagement metrics
3. Analyze → Examine follower patterns, ratios, growth velocity, engagement
4. AI Check → Deep behavioral analysis with Claude AI
5. Report → Generate comprehensive fraud assessment
6. Notify → Send detailed email report to partnership team
7. Log → Save to database for tracking

📊 Detection Capabilities

• Follower Authenticity: Analyzes follower-to-following ratio (red flag if < 0.5)
• Engagement Quality: Calculates engagement rate (industry avg: 1-5%)
• Growth Patterns: Detects suspicious rapid follower spikes
• Content Consistency: Evaluates posting frequency and regularity
• Profile Completeness: Checks verification, bio, activity
• AI Behavioral Analysis: Deep pattern recognition for sophisticated fraud

⚙️ Setup Instructions

1. Configure API Access

Social Platform APIs:

• Instagram: Get Graph API access token from Meta for Developers
• Twitter: OAuth 2.0 credentials from Twitter Developer Portal
• TikTok: Business API credentials (optional)

AI Analysis:

• Anthropic Claude API: Get key from console.anthropic.com
• Used for advanced behavioral fraud detection

2. Setup Notifications

• Configure SMTP in “Send Report” node
• Update recipient email (partnerships@company.com)
• Customize HTML template if needed

3. Database (Optional)

• Create PostgreSQL table (schema below)
• Add database credentials to final node
• Skip if you don’t need historical tracking

Database Schema

CREATE TABLE partnerships.influencer_fraud_reports (
  id SERIAL PRIMARY KEY,
  report_id VARCHAR(255) UNIQUE,
  username VARCHAR(255),
  platform VARCHAR(50),
  profile_url TEXT,
  followers BIGINT,
  following BIGINT,
  posts INTEGER,
  verified BOOLEAN,
  authenticity_score INTEGER,
  risk_level VARCHAR(50),
  final_decision TEXT,
  partnership_recommendation VARCHAR(100),
  ai_verdict VARCHAR(50),
  ai_confidence VARCHAR(20),
  red_flags JSONB,
  fake_follower_estimate VARCHAR(20),
  detailed_analysis JSONB,
  created_at TIMESTAMP
);

🚀 How to Use

Webhook Endpoint: POST /webhook/influencer-fraud-check

Request Body:

{
  "username": "influencer_handle",
  "platform": "instagram"  // or "twitter", "tiktok"
}

Example:

curl -X POST https://your-n8n.com/webhook/influencer-fraud-check \
  -H "Content-Type: application/json" \
  -d '{"username":"example_user","platform":"instagram"}'

📈 Scoring System

Overall Authenticity Score (0-100):

• 80-100: LOW RISK → Approved for partnership
• 60-79: MEDIUM RISK → Requires manual review
• 40-59: HIGH RISK → Caution advised
• 0-39: CRITICAL RISK → Rejected

Weighted Components:

• Follower Quality (25%)
• Engagement Quality (35%)
• Content Consistency (15%)
• Growth Pattern (15%)
• Profile Completeness (10%)

Final Score = 70% Automated + 30% AI Analysis

🚩 Red Flags Detected

• Following-to-follower ratio > 2:1
• Engagement rate < 0.5%
• Rapid growth (>50K followers/month)
• Large following with <10 posts
• No verification with >100K followers
• Bot-like comment patterns
• Suspicious audience demographics

💰 Cost Estimate

• Instagram/Twitter API: Free tier usually sufficient
• Claude AI: ~$0.10-0.20 per analysis
• Estimated: $5-10/month for 50 checks

💡 Best Practices

1. Always verify HIGH and MEDIUM risk profiles manually
2. Cross-reference with other influencer databases
3. Request media kit and past campaign results
4. Trial campaigns before large commitments
5. Monitor performance metrics post-partnership
6. Update detection thresholds based on your findings

🎯 What You Get

Detailed Report Includes:

• Overall authenticity score (0-100)
• Risk level classification
• Partnership recommendation (APPROVE/REVIEW/REJECT)
• Engagement quality analysis
• Fake follower percentage estimate
• AI behavioral insights
• Specific red flags and concerns
• Next steps and recommendations

🔗 Nodes Used

Send Email, HTTP Request, Postgres, Webhook

📥 Import

Download workflow.json and import into n8n: Workflow menu → Import from File

📖 Importing guide · 🔑 Credential setup