Abstract
The article contributes both conceptually and methodologically to the study of online news consumption by introducing new approaches to measuring user information behaviour and proposing a typology of users based on their click behaviour. Using as a case study two online outlets of large national newspapers, it employs computational approaches to detect patterns in time- and content-based user interactions with news content based on clickstream data. The analysis of interactions detects several distinct timelines of news consumption and scrutinises how users switch between news topics during reading sessions. Using clustering analysis, the article then identifies several types of news readers (e.g. samplers, gourmets) and examines their news diets. The results point out the limited variation in topical composition of the news diets between different types of readers and the tendency of these diets to align with the news supply patterns (i.e. the average distribution of topics covered by the outlet).
Highlights
The increasing adoption of digital technologies by legacy media has significant implications for news dissemination and consumption
This article contributes an observation of online reading habits of legacy media users and introduces new ways of studying them based on clickstream data
We focus on the legacy media and discuss the implications of digitalisation for information behaviour of their users
Summary
The increasing adoption of digital technologies by legacy media has significant implications for news dissemination and consumption. The formation of a high-choice information environment (Van Aelst et al, 2017) challenges users with the unprecedented amount of news content, whereas the rise of mobile devices enables them to consume news at a different pace and in different contexts compared with predigital times (Westlund and Färdigh, 2015) These factors fundamentally transform the media–audience relationship, yet their impact on user information behaviour online and the long-term societal consequences remain unclear. Empirical studies of online news consumption remain relatively limited in number and usually rely on self-reported or small-scale experimental data (see, for review, Mitchelstein and Boczkowski, 2010). More large-scale approaches are required to assess how legacy media users consume news online and in which ways digital environments change their reading habits, in particular considering the ongoing debate on the societal effects of the ‘algorithmic’ (Anderson, 2013) turn in news distribution. Without this knowledge, it is hardly possible to evaluate the impact of more targeted ways of news distribution on how users inform themselves and assess if algorithms enclose users in ‘echo chambers’ (Sunstein, 2017) and facilitate ‘masked censorship’ (Makhortykh and Bastian, 2020) or diversify their information diets (Eskens et al, 2017) and enable more control over their information diets (Harambam et al, 2018)
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