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

2025-07-23

AI Summary

Prior research lacks large-scale empirical analysis of how subjective features—specifically emotion, narrative, and vagueness—relate to argument strength. Method: This paper conducts the first systematic investigation into how these three features differentially affect argument quality along two dimensions: objective quality (logical rigor) and subjective persuasiveness (audience acceptance). Leveraging standard argument datasets, we employ automated annotation and multivariate regression to quantify feature effects. Contribution/Results: We find that narrative and vagueness exert opposing effects on objective quality versus subjective persuasiveness; emotion’s impact depends on rhetorical strategy rather than domain context. These findings uncover multidimensional pathways through which subjective factors shape argument evaluation, providing both interpretable theoretical grounding and empirical support for next-generation argument assessment models.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Knowledge Representation and Reasoning: Argumentation
• Multiagent Systems: Agreement, Argumentation & Negotiation

Application Category

• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• Quantify impact of emotions, storytelling, hedging on argument strength
• Compare automated annotation methods for subjective argument features
• Analyze contrasting effects of subjective features on argument quality

Innovation

• Regression analysis quantifies subjective factors impact
• Automated annotation methods for subjective features evaluated
• Contrasting effects of storytelling and hedging revealed