The coronavirus pandemic sparked a renewed interest in news consumption patterns. When major crises occur, people experience an increasing need for information and sense-making; given the extraordinary impact of this health crisis on people’s social and work life, relevant work support a ‘rally around the news’ effect, news fatigue and news avoidance, doomscrolling and a trend toward mainstream and trusted news outlets. This study explored how the coronavirus pandemic shaped news consumption patterns in Cyprus. The results show that news use hit record levels at the onset of the crisis, followed by corona news fatigue in the following months. Increased news consumption levels and greater engagement with the news were recorded again in the last couple of months of 2020 when the second wave of the pandemic hit Cyprus. Direct traffic to widely used and trusted sources doubled while a crisis boosting effect on mobile access to the detriment of computers was recorded.
News recommending systems (NRSs) are algorithmic tools that filter incoming streams of information according to the users’ preferences or point them to additional items of interest. In today’s high-choice media environment, attention shifts easily between platforms and news sites and is greatly affected by algorithmic technologies; news personalization is increasingly used by news media to woo and retain users’ attention and loyalty. The present study examines the implementation of a news recommender algorithm in a leading news media organization on the basis of observation of the recommender system’s outputs. Drawing on an experimental design employing the ‘algorithmic audit’ method, and more specifically the ‘collaborative audit’ which entails utilizing users as testers of algorithmic systems, we analyze the composition of the personalized MyNews area in terms of accuracy and user engagement. Premised on the idea of algorithms being black boxes, the study has a two-fold aim: first, to identify the implicated design parameters enlightening the underlying functionality of the algorithm, and second, to evaluate in practice the NRS through the deployed experimentation. Results suggest that although the recommender algorithm manages to discriminate between different users on the basis of their past behavior, overall, it underperforms. We find that this is related to flawed design decisions rather than technical deficiencies. The study offers insights to guide the improvement of NRSs’ design that both considers the production capabilities of the news organization and supports business goals, user demands and journalism’s civic values.
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