How to Build an Influencer Vetting Tool with Social Scraping

How to Build an Influencer Vetting Tool with Social Scraping Brands waste billions on influencer fraud — fake followers, bot engagement, and inflated metrics. Building an automated vetting tool that scrapes public social data can detect these patterns before money changes hands. Here's how to build one with Python. The Influencer Fraud Problem Studies estimate that 15-25% of influencer followers are fake. Engagement pods artificially inflate likes and comments. Without data-driven vetting, brands rely on self-reported metrics that are easily manipulated. Architecture Our vetting tool analyzes three dimensions: Follower quality — bot detection via profile analysis Engagement authenticity — detecting pods and bought engagement Content consistency — verifying claimed niche and posting patterns Scraping Public Profile Data We focus on publicly available data only. ScraperAPI handles the complexity of scraping social platforms through proper proxy rotation: import requests from bs4 import B