Algorithmic News Diversity and Democratic Theory: Adding Agonism to the Mix

The role news recommenders can play in stimulating news diversity is receiving increasing amounts of attention. Democratic theory plays an important role in this debate because it helps explain why news diversity is important and which kinds of news diversity should be pursued. In this article, I observe that the current literature on news recommenders and news diversity largely draws on a narrow set of theories of liberal and deliberative democracy. Another strand of democratic theory often referred to as ‘agonism’ is often ignored. This, I argue, is a mistake. Liberal and deliberative theories of democracy focus on the question of how political disagreements and conflicts can be resolved in a rational and legitimate manner. Agonism, to the contrary, stresses the ineradicability of conflict and the need to make conflict productive. This difference in thinking about the purpose of democratic politics can also lead to new ways of thinking about the value of news diversity and role algorithmic news recommenders should play in promoting it. The overall aim of the article is (re)introduce agonistic theory to the news recommender context and to argue that agonism deserves more serious attention.