Argument Mining for Organizational Research: A Computer-Aided Analysis of Organizational Talk

Argument mining—the automatic identification, classification, and linking of argumentative text—has been studied in natural language processing (NLP) for more than a decade. Despite its claimed potential for applications in legal, political, and social contexts, it remained largely unexplored in organizational research. This article introduces aspect-based argument mining (ABAM) as a methodical innovation for studying how organizations justify decisions, construct legitimacy, and relate to their environments through communicative acts. By scaling up the analysis of argumentative structures beyond the limits of small-scale, qualitative studies, ABAM enables the recognition and systematic analysis of argumentation patterns in large text corpora that were hardly detectable with previous (computational) approaches. The potential is demonstrated by a longitudinal case study of Twitter debates on nuclear energy in Germany, revealing how shifting societal values—particularly the reframing of nuclear energy from a safety to a climate issue—produced growing misalignments between organizational talk of a political party organization and its social media environment.