The Role Of News Recommendation Systems In Digital Democracies
Algorithm-based news recommendation systems are used in social media platforms and online news portals to recommend content to users based on their previous usage. In a collaboration between the University of Amsterdam and the University of Zürich, we will investigate the role these systems play in political news coverage in Switzerland and the Netherlands.
The project will provide a comprehensive overview of the implementation of digital strategies in the main Swiss and Dutch media organisations and will reveal the impact of algorithmic recommendation systems on trust in journalism. Of interest to media organisations and political actors: the project will also serve to formulate practical recommendations of public interest for improving the application and implementation of news recommendation systems.
The increasing automation and personalisation of communication processes in digitalised societies have triggered a public debate on the democratic implications of algorithmic news or recommendation systems such as social media, search engines, or news aggregation services. However, very little is known about the impact on the work of media professionals, the expectations and preferences of citizens, and the strategic choices of platforms.
Personalised and algorithmic news recommendation systems are increasingly influencing citizens’ media consumption. In the long term, they alter public opinion-forming processes and the structure of journalism. We will investigate how such recommendation systems affect the journalistic production of news, the public perception of topics, and the trust in the performance of journalism. We will also examine how these systems can be (re)programmed to prevent disinformation and loss of trust in the media. The main goal of this project is to understand how algorithmic recommendation systems influence the work of media professionals, and public perception and trust in journalism. Furthermore, we aim to develop evidence-based recommendations on how such recommendation systems could be constructed and (re)programmed in order to meet the various normative requirements of an enlightened, pluralistically informed public.