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Explaining Online News Engagement Based on Browsing Behavior: Creatures of Habit?
[journal article]
Abstract Understanding how citizens keep themselves informed about current affairs is crucial for a functioning democracy. Extant research suggests that in an increasingly fragmented digital news environment, search engines and social media platforms promote more incidental, but potentially more shallow mode... view more
Understanding how citizens keep themselves informed about current affairs is crucial for a functioning democracy. Extant research suggests that in an increasingly fragmented digital news environment, search engines and social media platforms promote more incidental, but potentially more shallow modes of engagement with news compared to the act of routinely accessing a news organization's website. In this study, we examine classic predictors of news consumption to explain the preference for three modes of news engagement in online tracking data: routine news use, news use triggered by social media, and news use as part of a general search for information. In pursuit of this aim, we make use of a unique data set that combines tracking data with survey data. Our findings show differences in predictors between preference for regular (direct) engagement, general search-driven, and social media-driven modes of news engagement. In describing behavioral differences in news consumption patterns, we demonstrate a clear need for further analysis of behavioral tracking data in relation to self-reported measures in order to further qualify differences in modes of news engagement.... view less
Keywords
social media; news; information-seeking behavior; online media; utilization; search engine; data capture
Classification
Interactive, electronic Media
Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Free Keywords
information search; news use; survey data; tracking data
Document language
English
Publication Year
2020
Page/Pages
p. 616-632
Journal
Social Science Computer Review, 38 (2020) 5
Issue topic
Integrating Survey Data and Digital Trace Data
DOI
https://doi.org/10.1177/0894439319828012
ISSN
1552-8286
Status
Published Version; peer reviewed