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).

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Recommender systems (RS) are on the rise in many domains. While they ofer great promises, they also raise concerns: lack of transparency, reduction of diversity, little to no user control. In this paper, we align with the normative turn in computer science which scrutinizes the ethical and societal implications of RS. We focus and elaborate on the concept of user control because that mitigates multiple problems at once. Taking the news industry as our domain, we conducted four focus groups, or moderated think-aloud sessions, with Dutch news readers (N=21) to systematically study how people evaluate diferent control mechanisms (at the input, process, and output phase) in a News Recommender Prototype (NRP). While these mechanisms are sometimes met with distrust about the actual control they ofer, we found that an intelligible user profle (including reading history and fexible preferences settings), coupled with possibilities to infuence the recommendation algorithms is highly valued, especially when these control mechanisms can be operated in relation to achieving personal goals. By bringing (future) users' perspectives to the fore, this paper contributes to a richer understanding of why and how to design for user control in recommender systems.

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Personalized content provided by recommender systems is an integral part of the current online news reading experience. However, news recommender systems are criticized for their'black-box' approach to data collection and processing, and for their lack of explainability and transparency. This paper focuses on explaining user profiles constructed from aggregated reading behavior data, used to provide content-based recommendations. The paper makes a first step toward consolidating epistemic values of news providers and news readers. We present an evaluation of an explanation interface reflecting these values, and find that providing users with different goals for self-actualization (i.e., Broaden Horizons vs. Discover the Unexplored) influences their reading intentions for news recommendations.

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The growing volume of digital data stimulates the adoption of recommender systems in different socioeconomic domains, including news industries. While news recommenders help consumers deal with information overload and increase their engagement, their use also raises an increasing number of societal concerns, such as “Matthew effects”, “filter bubbles”, and the overall lack of transparency. We argue that focusing on transparency for content-providers is an under-explored avenue. As such, we designed a simulation framework called SIREN 1 (SImulating Recommender Effects in online News environments), that allows content providers to (i) select and parameterize different recommenders and (ii) analyze and visualize their effects with respect to two diversity metrics. Taking the U.S. news media as a case study, we present an analysis on the recommender effects with respect to long-tail novelty and unexpectedness using SIREN. Our analysis offers a number of interesting findings, such as the similar potential of certain algorithmically simple (item-based k-Nearest Neighbour) and sophisticated strategies (based on Bayesian Personalized Ranking) to increase diversity over time. Overall, we argue that simulating the effects of recommender systems can help content providers to make more informed decisions when choosing algorithmic recommenders, and as such can help mitigate the aforementioned societal concerns. @en