A Guide to Web Scraping Job Postings

Web Scraping Job Postings

The rise of the world wide web or internet sprouted the age of information, where various types of knowledge are literally at one’s fingertips. This information comes in many forms and formats. There is a way to collect all of this data efficiently and effectively.  One subset of data collection is web scraping, also called web data extraction.

In this article, we will explain to you the basics of web scraping and why web scraping job postings is one of the most popular use cases of web scraping.

What is web scraping?

Web scraping, as the name implies, is scraping data from the web. The “insides” of websites are generally posted as text-based HTML (or XHTML) code, and web scraping methods target the data embedded in the underlying code. Software may be used to scrape data from websites, but in more sophisticated set-ups bots or web crawlers are used. Gathered data is rendered in a format that can be easily processed, interpreted, and stored by the end-user. 

Web scraping is not as complex as it seems for experienced computer users. The first step involves fetching the website. During general web browsing, this is done by loading the website on one’s browser. The fetched website can now be analyzed by the software, bot, or code. Data will be parsed (subjected to syntax analysis), copied, and reformatted according to the needs of the end-user. 

Think of it as reading thousands of references and copying the data needed. Unlike painstaking manual methods, this will be done automatically or semi-automatically by software or coded bots. For example, a company might be interested in the contact details of every company that deals with a specific industry. In the absence of collated data, the company will use web scraping to collect emails to send them bulk emails. On rare occasions, websites may have anti-scraping mechanisms installed, so manual data extraction (e.g. manual copy-paste) might be the only way to get data.

Web scrapers generally take data to be used in analytics, market research, gauging public opinion, among others. One of the most popular uses of scraping is web scraping job postings. The succeeding sections will explain how and why companies scrape job data.

Why the need to scrape job data?

Competition between companies makes studying the market a critical aspect for many companies. According to American analytics company Gallup, 58% of those seeking employment use the internet to find job vacancies posted by companies. There are many reasons why web scraping job postings are done by companies. Usually, competitors study the trends, track open positions in other companies, compare benefits and compensations, and even find leads to people who might be interested in their company.

There are instances where web scraping of job postings is difficult due to the presence of security measures on the target website. Usually, websites of job portals employ this tactic as individual company websites are not usually the target of automated web scrapers (as It would be harder to scrape data from different websites with different HTML structures). These blocks include captcha checks during instances of high website access from a single IP address designed to block automated access done by bots.

How is web scraping usually done?

Online job posting sites (aggregators) are usually the target source of data for the web scraper. Regardless if you use commercial scraping software or code your scraper (e.g. using Python), the process first involves feeding the scraper the uniform resource locator (URL or the website link in simpler terms) that it will load. Once opened, the HTML code is viewed (any browser can do this part too). 

Some commercial software scrapers will ask the user to manually choose which portion of the website structure contains the data needed. (Note that job posting websites generally have the same format for all listings, allowing the scraper to know where to get the data on the other listings without the need for the user to guide it.)

The scraper extracts data as programmed or instructed by the user. Generally, for job scraping, the following data is scraped (if available):

  • Job title
  • Job description
  • Compensation
  • Benefits
  • Company information

Some scrapers can even load non-HTML code like JavaScript or Cascading Style Sheets (CCS). Finally, once the extraction of data has succeeded, the web scraper prepares the output where the data is placed in such a way that the user can use it however they want. For example, spreadsheets of Excel or comma-separated values (CSV) files are useful ways for the scraper to interpret the data as they see fit. Advanced scrapers can also generate outputs in over formats that can be integrated with codes and/or other applications.

In summary, web scraping of job postings is a competitive endeavor that one can do to get an edge on the ever-changing job market trends.

Read Also: Everything You Need to Know About Managing Saved Jobs on LinkedIn

A Guide to Web Scraping Job Postings

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