[Séminaire Humanités Numériques Tourangelles] #9 Argument Mining in News Media: Tailoring Models and Methods for Responsible Application

Organisation : Elena Pierazzo, (CESR, Université de Tours)

par Sarah Oberbichler – Digital Humanities Lab, Leibniz Institute of European History

Argument Mining automatically extracts arguments and their explicit or implicit components from unstructured text. While the method is well established, recent AI advances have significantly expanded possibilities for computational argument analysis (Gorur et al., 2024; Cabessa et al., 2025). These developments have made argument mining increasingly viable for news media, where arguments are often communicated implicitly rather than through explicit logical structures. Media arguments shape how events are perceived in public discourse. Through argumentative framing, media actors can construct specific interpretations of reality. For instance, when a disaster response is framed by praising a government’s swift action and effective coordination, this can construct an intended argumentative narrative of institutional competence and strength. Systematically identifying and analyzing these intended narratives is crucial for understanding how and with what intention media reality is created. In this talk, I will present findings from applying large language models to argument mining in historical news media. I will discuss challenges with closed-source models for this research context: their heavy alignment for general use introduces biases while their sensitivity to prompts and parameters creates further opportunities for «LLM hacking» (Baumann et al., 2025). 

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