Psychological research has long suggested that storytelling can shape beliefs and behaviors by fostering emotional engagement and narrative transportation. However, it remains unclear whether these effects extend to online argumentative discourse. In this paper, we examine the role of narrative in real-world argumentation using discussions from the ChangeMyView subreddit. Leveraging an automatic story detection model, we analyze how narrative use varies across persuasive comments, user types, discussion outcomes, and the kinds of change being sought. While narrative appears more frequently in some contexts, it is not consistently linked to successful persuasion. Notably, highly persuasive users tend to use narrative less, and storytelling does not demonstrate increased effectiveness for any specific type of persuasive goals. These findings suggest that narrative may play a limited and context-dependent role in online discussions, highlighting the need for computational models of argumentation to account for rhetorical diversity.

The task of computational textual narrative detection focuses on detecting the presence of narrative parts, or the degree of narrativity in texts. In this work, we focus on detecting the local degree of narrativity in texts, using short text passages. We performed a human annotation experiment on 325 English texts ranging across 20 genres to capture readers’ perception by means of three cognitive aspects: suspense, curiosity, and surprise. We then employed a linear regression model to predict narrativity scores for 17,372 texts. When comparing our average annotation scores to similar annotation experiments with different cognitive aspects, we found that Pearson’s r ranges from .63 to .75. When looking at the calculated narrative probabilities, Pearson’s r is .91. We found that it is possible to use suspense, curiosity and surprise to detect narrativity. However, there are still differences between methods. This does not imply that there are inherently correct methods, but rather suggests that the underlying definition of narrativity is a determining factor for the results of the computational models employed.