The “computational turn”: an “interdisciplinary turn”? A systematic review of text as data approaches in journalism studies

Abstract

Abstract Possibilities of applying automated content analysis in journalism studies include, for example, machine learning to identify topics in journalistic coverage or measuring news diffusion via automated approaches. But how have computational methods been applied thus far? And what are consequences of the “computational turn” in communication science, especially concerning interdisciplinarity? Based on a systematic literature review, this article summarizes the use of automated content analysis in journalism studies. Results illustrate an increasing use of the method by communication scientists, as yet another indicator of methodological interdisciplinarity in communication science. However, there is little evidence of an increase in theoretical interdisciplinarity: Studies relying on computational methods do not increasingly refer to theories from other disciplines. With respect to practical interdisciplinarity, for instance collaborations, our discipline is by no means becoming more interdisciplinary. Instead, we find a shift in favor of technical disciplines. At least up to now, the “computational turn” in communication science should thus not be equated with an “interdisciplinary turn.”