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

arXiv: https://arxiv.org/abs/2507.17409 · HTML: https://mlsanity.com/paper/2507.17409/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 62 references that are in the map.
- First posted on arXiv 23 Jul 2025.

## Builds on (most cited first, 15 of 15)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions](https://mlsanity.com/paper/1602.01103/) (2016)
- [Argumentation Mining in User-Generated Web Discourse](https://mlsanity.com/paper/1601.02403/) (2016)
- [A Large-scale Dataset for Argument Quality Ranking: Construction and Analysis](https://mlsanity.com/paper/1911.11408/) (2019)
- [Automatic Argument Quality Assessment -- New Datasets and Methods](https://mlsanity.com/paper/1909.01007/) (2019)
- [Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction](https://mlsanity.com/paper/2206.05238/) (2022)
- [Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion](https://mlsanity.com/paper/1708.09085/) (2017)
- [Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network](https://mlsanity.com/paper/1907.08971/) (2019)
- [Crowdsourcing and Validating Event-focused Emotion Corpora for German and English](https://mlsanity.com/paper/1905.13618/) (2019)
- [Finding Convincing Arguments Using Scalable Bayesian Preference Learning](https://mlsanity.com/paper/1806.02418/) (2018)
- [Fearful Falcons and Angry Llamas: Emotion Category Annotations of Arguments by Humans and LLMs](https://mlsanity.com/paper/2412.15993/) (2024)
- [Creating a Domain-diverse Corpus for Theory-based Argument Quality Assessment](https://mlsanity.com/paper/2011.01589/) (2020)
- [Towards a Holistic View on Argument Quality Prediction](https://mlsanity.com/paper/2205.09803/) (2022)
- [PetKaz at SemEval-2024 Task 3: Advancing Emotion Classification with an LLM for Emotion-Cause Pair Extraction in Conversations](https://mlsanity.com/paper/2404.05502/) (2024)

## Built on by (most cited first, 2 of 2)

- [Categorical Emotions or Appraisals - Which Emotion Model Explains Argument Convincingness Better?](https://mlsanity.com/paper/2511.07162/) (2025)
- [From Emotion to Expression: Theoretical Foundations and Resources for Fear Speech](https://mlsanity.com/paper/2601.17132/) (2026)

## Abstract (arXiv)

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.