Toward a Better Understanding of News User Journeys: A Markov Chain Approach
Susan Vermeer, Damian Trilling
Abstract
In recent years, the volume of clickstream and user data collected by news organizations has reached enormous proportions. As a result, news organizations—as well as journalism scholars—face novel methodological challenges to describe and analyze this wealth of information. To move forward, we demonstrate a computational approach to understand the news journeys Web users take to find the news they want to read. We propose the use of Markov chains. These models provide an effective and compact way to discover meaningful patterns in clickstream data. In particular, they capture the sequentiality in news use patterns. We illustrate this approach with an analysis of more than 1 million Web pages, from 175 websites (news websites, search engines, social media), collected over 8 months in 2017/18. The analysis of such data is of high interest to journalism scholars, but can also help news organizations to design sales strategies, provide more personalized content, and find the most effective structure for their website.