Investigating Subjective Factors of Argument Strength: Storytelling, Emotions, and Hedging

TL;DR

This study explores how subjective factors like storytelling, emotions, and hedging influence argument strength, providing large-scale analysis and automated annotation methods to understand their contrasting effects on objective and subjective argument quality.

Contribution

It introduces new annotated datasets for subjective features and analyzes their impact on argument strength, revealing different patterns across datasets.

Findings

Storytelling and hedging have contrasting effects on argument quality.

Emotions influence argument strength depending on rhetorical use.

Automated annotation methods for subjective features are evaluated.

Abstract

In assessing argument strength, the notions of what makes a good argument are manifold. With the broader trend towards treating subjectivity as an asset and not a problem in NLP, new dimensions of argument quality are studied. Although studies on individual subjective features like personal stories exist, there is a lack of large-scale analyses of the relation between these features and argument strength. To address this gap, we conduct regression analysis to quantify the impact of subjective factors $-$ emotions, storytelling, and hedging $-$ on two standard datasets annotated for objective argument quality and subjective persuasion. As such, our contribution is twofold: at the level of contributed resources, as there are no datasets annotated with all studied dimensions, this work compares and evaluates automated annotation methods for each subjective feature. At the level of novel insights, our regression analysis uncovers different patterns of impact of subjective features on the two facets of argument strength encoded in the datasets. Our results show that storytelling and hedging have contrasting effects on objective and subjective argument quality, while the influence of emotions depends on their rhetoric utilization rather than the domain.