Abstract
ABSTRACTIn 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.
Highlights
In recent years, the very nature of news consumption has changed drastically as people increasingly consume news online (Flaxman, Goel, and Rao 2016)
We will focus on the following question: “How can Markov chains be applied to the field of journalism studies as a way to understand the sequentiality of news use?” Our objective is to familiarize journalism scholars with the basic principles of Markov chains and show how these principles can be implemented
The results indicated a gap in the online choices of journalists and news consumers: journalists were more likely to select hard news topics as the most newsworthy stories, whereas consumers’ choices were dominated by soft news topics
Summary
The very nature of news consumption has changed drastically as people increasingly consume news online (Flaxman, Goel, and Rao 2016). Researchers studying how citizens interact with journalistic content more and more find themselves in a position where they need to rely on online tracking data of user behavior (see e.g., Kleppe and Otte 2017; Dvir-Gvirsman, Tsfati, and Menchen-Trevino 2014; Menchen-Trevino and Karr 2012). This is especially important when researchers want to link audience data to content data in order to study media effects (Scharkow and Bachl 2017; De Vreese et al 2017)
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