Influencer fraud is a multi-billion dollar issue, with studies showing up to 45% of Instagram accounts exhibiting signs of fake activity. This tutorial provides a step-by-step guide to building a powerful Node.js tool to detect fraudulent influencers. The system analyzes TikTok and Instagram creators using key metrics like engagement rates, follower/following ratios, and view count consistency.

It leverages the OpenAI API to perform sophisticated analysis of comment sections, identifying bot-generated patterns that manual review often misses. By combining quantitative data with AI-powered qualitative assessment, the tool generates a comprehensive authenticity score. This empowers brands and marketers to avoid paying for 'ghost audiences' and ensures marketing budgets are invested in creators with genuine reach.

The article details the entire stack, including SociaVault for data scraping, and provides complete code for fetching profiles, calculating engagement metrics, and running the final analysis.