K
Kristin Van Damme
Researcher at Ghent University
Publications - 20
Citations - 384
Kristin Van Damme is an academic researcher from Ghent University. The author has contributed to research in topics: News media & Journalism. The author has an hindex of 7, co-authored 19 publications receiving 250 citations.
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Journal ArticleDOI
What's APPening to news? A mixed-method audience- centred study on mobile news consumption
TL;DR: In this paper, a guiding cluster analysis of a large-scale questionnaire (N = 1279) was performed, indicating three types of news consumers, and a mixed-method study was set up to thicken the originally derived clusters.
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360° Video Journalism: Experimental Study on the Effect of Immersion on News Experience and Distant Suffering
TL;DR: News producers are increasingly experimenting with news in virtual reality and 360° video, which is often presented as the ultimate form of immersive journalism as it provides viewers with a first-person experience as mentioned in this paper.
Journal ArticleDOI
Mapping the Mobile DNA of News. Understanding Incidental and Serendipitous Mobile News Consumption
TL;DR: In this article, the authors examine how users understand serendipity in mobile news consumption and whether this is a good or bad sign for the future of mobile news. But,
Journal ArticleDOI
Consumers’ Willingness to Share Personal Data: Implications for Newspapers’ Business Models
Tom Evens,Kristin Van Damme +1 more
TL;DR: The results of an industry-driven big data project are presented that allows news organizations to engage with their audience more deeply by suggesting personalized content recommendations, serving targeted advertising and/or improving the user experience.
Journal ArticleDOI
A user-centric evaluation of context-aware recommendations for a mobile news service
Toon De Pessemier,Cédric Courtois,Kris Vanhecke,Kristin Van Damme,Luc Martens,Lieven De Marez +5 more
TL;DR: Context-aware content-based recommendations may induce a higher user satisfaction after a longer period of service operation, enabling the recommender to overcome the cold-start problem and distinguish user preferences in various contextual situations.