Voices and media frames in the public debate on artificial intelligence: comparing results from manual and automated content analysis - Zai, Florin; Rohrbach, Tobias; Hänggli Fricker, Regula (Frontiers in Communication, 2025)
The rise of artificial intelligence (AI) has been accompanied by extensive reporting by news media, serving as a forum for public debate about its risks and potential for society. This study sheds light on this AI debate in news media by using the theoretical concepts of standing and framing and by combining manual and automated content analysis [reversed Joint Sentiment Topic model (rJST) and Named Entity Recognition (NER)]. Based on news articles published in Swiss, German, UK, and US quality and tabloid outlets between November 2020 and November 2022, we examine which actors have standing in the AI debate, which frames they use, and which positions they hold. We also compare manual and automated methods as a methodological contribution. We see that economic and scientific actors have a high standing in reporting and journalists themselves provide a considerable part of contextualization as speakers. As in previous studies, the progress and economic consequences frames dominate, with mostly pro positions. The ethics and morality frame, however, is underrepresented. More diverse voices could enrich the AI debate. Comparing the two methods, we see that the automated analysis (via rJST) detects topics relatively reliably. By contrast, there are differences between the results of the two methods regarding the framing of these topics which are mainly due to the lack of sensitivity of the automated analysis regarding nuanced contextual information such as individual positions. Further, the automated analysis overestimates political actors in the debate and underestimates journalistic actors, as named entities do not necessarily act as speakers.