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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. 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