Beschreibung
News recommender systems (NRS) determine news exposure for digital publics. However, the effects of NRSs on users remain understudied. This study examines how diversity of political news within NRSs can influence political news use, knowledge, and attitudes. We implemented an NRS that gathers news from a variety of news outlets, identifies political party representations in the articles, and recommends them based on a diversity model in a smartphone app. Results are based on a field experiment conducted during the state elections in Lower Saxony, Germany. While NRSs can increase knowledge about minority parties, they do not change voting behavior. These findings underscore the complex nature of NRSs in the political context and the need for interdisciplinary collaboration in their design.
Keywords
Computational methods
Journalism
News recommender systems
Voting behavior
