ARGUS: Seeing the Influence of Narrative Features on Persuasion in Argumentative Texts

TL;DR

This paper introduces ARGUS, a framework that analyzes how narrative features influence persuasion in online argumentative texts by combining annotated corpora, theoretical insights, and advanced classification models.

Contribution

The study presents a novel framework integrating narrative feature annotation, theoretical models, and large language models to study persuasion in argumentative discourse.

Findings

Narrative features significantly impact persuasion success.

Encoder-based classifiers and LLMs effectively identify narrative elements.

The framework enables large-scale analysis of narrative influence in online arguments.

Abstract

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.