Appreciating News Algorithms: Examining Audiences’ Perceptions to Different News Selection Mechanisms

As news selection is increasingly controlled by algorithms, a growing number of scholars are exploring how recommenders can serve public services. Despite aspirations towards service little known about which type recommender people prefer, let alone that aims to promote societal values. This study give insights into audiences’ perceptions and their underlying mechanisms. To do so, we distinguish between three mechanisms, namely content-based similarity, collaborative similarity diversity. The first two strive for respectively content users, while the third one diversity in consumed. Results large-scale survey (n = 943) show prefer over Audience characteristics, such as information overload concerns missing challenging viewpoints, explain audiences evaluate different We discuss these results align with selectivity algorithms be used tackle concerns. therefore introduce concept ‘personalized diversity’ idea an individual filter abundance online information.