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

Abstract

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