Towards an Adaptive Turn in Journalism: Conceptualizing the Algorithmic Co-Constitution of the News Media–Audience Relationship

In this article, we introduce the term “adaptive turn” in the field of journalism to encapsulate the shift brought about by the introduction of algorithmic systems, such as predictive audience analytics, recommender systems, and generative AI technologies, across the news media industries. These algorithmic systems enable real-time adaptation to users’ individualized needs and actions based on their previous news consumption patterns. Previous literature and theorizing have dealt with either algorithmic systems focusing on the impact on journalistic values or practices or on the users’ algorithmic experiences, while our aim with the introduction of “the adaptive turn” is to conceptualize how these developments transform the relationship between the two separate fields. Building on an STS theoretical framework of technologies as mediating between humans and the world, we argue that the adaptive turn captures this shift to what we term the algorithmic co-constitution of the media/user relationship. By analysing current examples from existing literature, this paper pinpoints how this algorithmic co-constitution happens along four dimensions, namely a material, a normative, a commercial, and an epistemic dimension. Building on this, we suggest four research commitments going forward, which can qualify the identification of the social implications of the adaptive turn in journalism.