Media Representations of Healthcare Robotics in Norway 2000-2020: A Topic Modeling Approach

Abstract

Robots are projected to affect healthcare services in significant, but unpredictable, ways. Many believe robots will add value to future healthcare, but their arrival has triggered controversy. Debates revolve around how robotics will impact healthcare provision, their effects on the future of labor and caregiver–patient relationships, and ethical dilemmas associated with autonomous machines. This study investigates media representations of healthcare robotics in Norway over a twenty-year period, using a mixed-methods design. Media representations affect public opinion in multiple ways. By assembling and presenting information through stories, they not only set the agenda by broadcasting values, experiences, and expectations about new technologies, but also frame and prime specific understandings of issues. First, we employ an inductive text-mining approach known as “topic modeling,” a computational method for eliciting abstract semantic structures from large text corpora. Using Non-Negative Matrix Factorization, we implement a topic model of manifest content from 752 articles, published in Norwegian print media between 1.1.2000 and 2.10.2020, sampled from a comprehensive database for news media (Atekst, Retriever). We complement this computational lens with a more fine-grained, qualitative analysis of content in exemplary texts sampled from each topic. Here, we identify prominent “frames,” discursive cues for interpreting how various stakeholders talk about healthcare robotics as a contested domain of policy and practice in a comprehensive welfare state. We also highlight some benefits of this approach for analyzing media discourse and stakeholder perspectives on controversial technologies.