THIRTEEN WAYS OF LOOKING AT AN ALGORITHM: HOW JOURNALISM FRAMES THE DISRUPTIVE POTENTIAL OF GENERATIVE AI

Abstract

In this article, we aim to identify and trace some of the most prevalent frames and tropes surrounding GAI, examining how dominant media outlets–national news media and professional trade publications–framed and discussed generative AI, particularly in relation to education and media production, during the first year of GAI’s widespread rollout, from November 2022-October 2023. We employ framing analysis for its focus on how journalists’ understanding of emerging technologies shapes news coverage, which ultimately informs public opinion (D’Angelo, 2017; Tewksbury & Scheufele, 2009). Our emergent frames were developed using qualitative discourse analysis (Fairclough, 2010), recognizing that initial media coverage of an emerging social issue or technical regime serves as a proxy to understand broader public understanding, within key professions, of issues and problems surrounding the widespread adoption of new technologies. Once we developed our emergent taxonomy of common frames, we employed a novel, GAI-based technique for framing analysis. We used iterative prompt engineering on open source LLM DeepSeek to investigate not only the discursive frameworks but also the emotionally valent (positive vs. negative) subframes included in each news article, taking care to validate its outputs through intercoder reliability testing.