ARGUS: Seeing the Influence of Narrative Features on Persuasion in Argumentative Texts
This work addresses the underexplored role of narratives in online argumentation, providing tools for researchers in computational linguistics and social sciences, though it is incremental as it builds on existing theoretical frameworks.
The authors tackled the problem of understanding how narrative features affect persuasion in online argumentative texts by developing ARGUS, a framework that includes a new annotated corpus and uses classifiers and LLMs to analyze narrative dimensions, resulting in insights into their influence on persuasion success.
Can narratives make arguments more persuasive? And to this end, which narrative features matter most? Although stories are often seen as powerful tools for persuasion, their specific role in online, unstructured argumentation remains underexplored. To address this gap, we present ARGUS, a framework for studying the impact of narration on persuasion in argumentative discourse. ARGUS introduces a new ChangeMyView corpus annotated for story presence and six key narrative features, integrating insights from two established theoretical frameworks that capture both textual narrative features and their effects on recipients. Leveraging both encoder-based classifiers and zero-shot large language models (LLMs), ARGUS identifies stories and narrative features and applies them at scale to examine how different narrative dimensions influence persuasion success in online argumentation.