News recommenders help users to find relevant online content and have the potential to fulfilla crucial role in a democratic society, directing the scarce attention of citizens towards the information that is most important to them. Simultaneously, recent concerns about so-called filter bubbles, misinformation and selective exposure are symptomatic of the disruptive potential of these digital news recommenders. Recommender systems can make or break filter bubbles, and as such can be instrumental in creating either a more closed or a more open internet. Current approaches to evaluating recommender systems are often focused on measuring an increase in user clicks and short-term engagement, rather than measuring the user's longer term interest in diverse and important information. This paper aims to bridge the gap between normative notions of diversity, rooted in democratic theory, and quantitative metrics necessary for evaluating the recommender system. We propose a set ofmetrics grounded in social science interpretations of diversity and suggest ways for practical implementations.@en

The rise of news content on social media has been accompanied by a hope that people with lower socioeconomic status and less interest in political affairs would be “accidentally” exposed to news. By combining tracking and survey data from a Dutch online panel (N = 413), we analyze how political interest, income, and education influence social media news exposure and consumption. Higher levels of political interest are associated with higher amounts of news exposure on Facebook and more news items consumed via social media. Users engage less often in news-related follow-up behavior after consuming news items via social media than after consuming news items referred via news websites. If social@en