How to Build a Tech Layoff Tracker with News Scraping

Tech layoffs make headlines, but tracking them systematically reveals patterns that news articles miss. This guide shows you how to build a scraper that monitors layoff announcements and analyzes workforce trends. Why Track Layoffs Programmatically? Manual tracking is slow and incomplete. A scraper can monitor dozens of sources simultaneously, extract structured data from announcements, and build a dataset that reveals industry patterns. Setup pip install requests beautifulsoup4 pandas Scraping Layoff Data Sources Start with structured layoff tracking sites: import requests from bs4 import BeautifulSoup import pandas as pd import re def scrape_layoff_tracker (): params = { " api_key " : " YOUR_SCRAPERAPI_KEY " , " url " : " https://layoffs.fyi/ " , " render " : " true " } response = requests . get ( " https://api.scraperapi.com " , params = params ) soup = BeautifulSoup ( response . text , " html.parser " ) layoffs = [] table = soup . select_one ( " table " ) if table : headers = [ th