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Diversity in personalized news recommender systems is often defined asdissimilarity, and based on topic diversity (e.g., corona versus farmersstrike). Diversity in news media, however, is understood as multiperspectivity(e.g., different opinions on corona measures), and arguably a keyresponsibility of the press in a democratic society. While viewpoint diversityis often considered synonymous with source diversity in communication sciencedomain, in this paper, we take a computational view. We operationalize thenotion of framing, adopted from communication science. We apply this notion toa re-ranking of topic-relevant recommended lists, to form the basis of a novelviewpoint diversification method. Our offline evaluation indicates that theproposed method is capable of enhancing the viewpoint diversity ofrecommendation lists according to a diversity metric from literature. In anonline study, on the Blendle platform, a Dutch news aggregator platform, withmore than 2000 users, we found that users are willing to consume viewpointdiverse news recommendations. We also found that presentation characteristicssignificantly influence the reading behaviour of diverse recommendations. Theseresults suggest that future research on presentation aspects of recommendationscan be just as important as novel viewpoint diversification methods to trulyachieve multiperspectivity in online news environments.

Operationalizing Framing to Support Multiperspective Recommendations of Opinion Pieces