Matrix-Based News Aggregation: Exploring Different News Perspectives

News aggregators capably handle the large amount of news that is published nowadays. However, these systems focus on the presentation of important, common information in news, but do not reveal different perspectives on the same topic. Thus, current news aggregators suffer from media bias, i.e. differences in the content or presentation of news. Finding such differences is crucial to reduce the effects of media bias. This paper presents matrix-based news analysis (MNA), a novel design for news exploration. MNA helps users gain a broad and diverse news understanding by presenting various news perspectives on the same topic. Furthermore, we present NewsBird, a news aggregator that implements MNA to find different perspectives on international news topics. The results of a case study demonstrate that NewsBird broadens the user's news understanding while it also provides similar news aggregation functionalities as established systems.