Persuasive Argumentation and Emotions: An Empirical Evaluation with Users

Benlamine, Mohamed S.; Villata, Serena; Ghali, Ramla; Frasson, Claude; Gandon, Fabien; Cabrio, Elena

doi:10.1007/978-3-319-58071-5_50

Cited by 7 publications

(5 citation statements)

References 16 publications

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“…Most work in Argument Mining (AM) focuses on the logos mode of persuasion, i.e., whether arguments are logically plausible. Nevertheless, recent studies support that the mode of pathos represented by emotions is essential in the persuasion process (Benlamine et al, 2015(Benlamine et al, , 2017D'Errico et al, 2018;Li and Xiao, 2020;Hilton, 2008). Those studies have in common that they relied on relatively small sample sizes.…”

Section: Emotion Detectionmentioning

confidence: 99%

“…Thus, enabling transfer between sources and datasets appears especially appealing, but existing works (Gretz et al, 2020;Toledo et al, 2019;Swanson et al, 2015;Habernal and Gurevych, 2016b) cease to provide detailed studies thereupon. Moreover, social science research suggests that the strength of an argument depends less on its logical coherence and depends much more appealing to the recipient's emotions (Benlamine et al, 2015(Benlamine et al, , 2017D'Errico et al, 2018;Li and Xiao, 2020;Hilton, 2008) -a fact which is insufficiently considered so far.…”

Section: Introductionmentioning

confidence: 99%

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Towards a Holistic View on Argument Quality Prediction

Fromm¹,

Berrendorf²,

Reiml³

et al. 2022

Preprint

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Argumentation is one of society's foundational pillars, and, sparked by advances in NLP and the vast availability of text data, automated mining of arguments receives increasing attention. A decisive property of arguments is their strength or quality. While there are works on the automated estimation of argument strength, their scope is narrow: they focus on isolated datasets and neglect the interactions with related argument mining tasks, such as argument identification, evidence detection, or emotional appeal. In this work, we close this gap by approaching argument quality estimation from multiple different angles: Grounded on rich results from thorough empirical evaluations, we assess the generalization capabilities of argument quality estimation across diverse domains, the interplay with related argument mining tasks, and the impact of emotions on perceived argument strength. We find that generalization depends on a sufficient representation of different domains in the training part. In zero-shot transfer and multi-task experiments, we reveal that argument quality is among the more challenging tasks but can improve others. Finally, we show that emotions play a minor role in argument quality than is often assumed. We publish our code at https://anonymous.4open.science/ r/kdd-holistic-view-aq-C0D8.

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Section: Emotion Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards a Holistic View on Argument Quality Prediction

Fromm¹,

Berrendorf²,

Reiml³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…It has been suggested that a substantial link exists between the process of argumentation and the emotions experienced by the participants in that process. According to the study of social science (Benlamine et al, 2015;D'Errico et al, 2018;Li and Xiao, 2020;Hilton, 2008), the strength of an argument relies much more on the argument's emotional appeal rather than the argument's logical coherence, which means it is hard to capture the quality of arguments only with their own context. The first impression made by comparing different arguments plays an important role in assessing the argument's quality.…”

Section: Introductionmentioning

confidence: 99%

Contextual Interaction for Argument Post Quality Assessment

Wang,

Chen,

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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Recently, there has been an increased emphasis on assessing the quality of natural language arguments. Existing approaches primarily focus on evaluating the quality of individual argument posts. However, they often fall short when it comes to effectively distinguishing arguments that possess a narrow quality margin. To address this limitation, this paper delves into two alternative methods for modeling the relative quality of different arguments: 1) Supervised contrastive learning that captures the intricate interactions between arguments. By incorporating this approach, we aim to enhance the assessment of argument quality by effectively distinguishing between arguments with subtle differences. 2) Large language models (LLMs) with in-context examples that harness the power of LLMs and enrich them with incontext demonstration. Through extensive evaluation and analysis on the publicly available IBM-Rank-30k dataset, we demonstrate the superiority of our contrastive interaction approach over state-of-the-art baselines. On the other hand, while LLMs with in-context examples demonstrate a commendable ability to identify high-quality argument posts, they exhibit relatively limited effectiveness in quantifying argument quality and distinguishing between arguments with a narrow quality gap. Code is available at https://github.com/ucasYW/Contextual-Interaction-for-AQA.

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“…On the other hand, we have identified a recent line of research that aims at investigating if rational reasoning can trigger human emotions, and understand their implications. For that purpose, in [51], the authors present a preliminary experiment where the participants were debating while wearing specific hardware for detecting their emotions. The observed results indicate clear trends that can be discerned from an emotional analysis while having argumentative dialogues.…”

Section: Emotions In Argumentationmentioning

confidence: 99%

“…The observed results indicate clear trends that can be discerned from an emotional analysis while having argumentative dialogues. These experiments were followed by further research with a similar study where persuasive aspects of argumentation were also taken into account [52,385]. The three Aristotelian argumentative strategies (i.e., Ethos, Logos, and Pathos) are taken into account in the experiments.…”

Section: Emotions In Argumentationmentioning

confidence: 99%

Computational Argumentation for the Automatic Analysis of Argumentative Discourse and Human Persuasion

Dolz¹

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Finalment, m'agradaria acabar expressant els meus agraïments a les dues persones fonamentals en la definició i consolidació de la meua carrera acadèmica, les meues tutores. Des que vaig comenc ¸ar a treballar al VRAIN, el suport rebut per part d'Ana i de Stella ha sigut incommensurable. Sota la seua tutela excepcional ha sigut possible desenvolupar totes les idees i propostes presents en aquesta tesi doctoral.Moltes gràcies a totes i tots.Ramon

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Persuasive Argumentation and Emotions: An Empirical Evaluation with Users

Cited by 7 publications

References 16 publications

Towards a Holistic View on Argument Quality Prediction

Towards a Holistic View on Argument Quality Prediction

Contextual Interaction for Argument Post Quality Assessment

Computational Argumentation for the Automatic Analysis of Argumentative Discourse and Human Persuasion

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