Operationalizing Framing to Support Multiperspective Recommendations of Opinion Pieces
This work addresses the need for multiperspectivity in news recommender systems to support democratic discourse, though it is incremental by building on existing diversity concepts.
The paper tackled the problem of enhancing viewpoint diversity in personalized news recommendations by operationalizing framing from communication science to re-rank topic-relevant lists, resulting in improved diversity metrics in offline evaluation and user willingness to consume diverse recommendations in an online study with over 2000 users.
Diversity in personalized news recommender systems is often defined as dissimilarity, and based on topic diversity (e.g., corona versus farmers strike). Diversity in news media, however, is understood as multiperspectivity (e.g., different opinions on corona measures), and arguably a key responsibility of the press in a democratic society. While viewpoint diversity is often considered synonymous with source diversity in communication science domain, in this paper, we take a computational view. We operationalize the notion of framing, adopted from communication science. We apply this notion to a re-ranking of topic-relevant recommended lists, to form the basis of a novel viewpoint diversification method. Our offline evaluation indicates that the proposed method is capable of enhancing the viewpoint diversity of recommendation lists according to a diversity metric from literature. In an online study, on the Blendle platform, a Dutch news aggregator platform, with more than 2000 users, we found that users are willing to consume viewpoint diverse news recommendations. We also found that presentation characteristics significantly influence the reading behaviour of diverse recommendations. These results suggest that future research on presentation aspects of recommendations can be just as important as novel viewpoint diversification methods to truly achieve multiperspectivity in online news environments.