Rhetorical strategies and their influence on audiences are often studied through social media posts and comments. However, this focus overlooks the “universal audience”, which is the majority of readers who remain silent and do not explicitly express how a message affects them. This study investigates how two classical modes of persuasion, ethos and pathos, resonate in the silent audience’s interpretations of meaning. Using a dataset of social media sentences paired with human-written interpretations, we label both sources for ethos and pathos and assess whether these rhetorical appeals are preserved. Our analyses show that interpretations diverge from the original sentences in 30% of cases, with rhetorically charged content eliciting greater variability than neutral content. We further find that ethos and pathos in original sentences can predict audience attitudes toward the author, underscoring the subtle ways rhetoric shapes perception beyond visible engagement.

This paper demonstrates LEPAn, Logos-Ethos-Pathos Analytics, which offers analysis of statistical patterns in the use of rhetorical arguments in public discussions on polarised issues. It is available at https://newethos.org/technologies/

This demo paper presents a platform designed to process changes of ethotic and sentiment load in argumentation when speakers rephrase what has been said. Our aim is to produce a linguistic analysis and a linguistic model from corpus analysis with the ultimate goal of the development of a natural language system which is capable of identifying the facets of rephrase.

Endorsing the character of allies and destroying credibility of opponents is a powerful tactic for persuading others, impacting how we see politicians and how we vote in elections, for example. Our previous work demonstrated that ethos supports and attacks use different language, we hypothesise that further distinctions should be made in order to better understand and implement ethotic strategies which people use in real-life communication. In this paper, we use the Aristotelian concept of elements of ethos: practical wisdom, moral virtue and goodwill, to determine specific grounds on which speakers can be endorsed and criticised. We propose a classification of types of ethos supports and attacks which is empirically derived from our corpus. The manual classification obtains a reliable Cohen's kappa κ=0.52 and weighted κ=0.7. Finally, we develop a pipeline to classify ethos supports and attacks into their types depending on whether endorsement or criticism is grounded in wisdom, virtue or goodwill. The automatic classification obtains a solid improvement of macro-averaged F1-score over the baseline of 10%, 25%, 9% for one vs all classification, and 16%, 18%, 10% for pairwise classification.

We demonstrate the first prototype of the ADD-up visual analytics system. The Augmented Deliberative Democracy (ADD-up) project aims to enhance public deliberations by providing argument analytics in real time. The system will ultimately take a stenographic feed of a public deliberation meeting, automatically extract the arguments therein and project visual analytics intended to improve the deliberative quality of the event.

Automatically recognising and extracting the reasoning expressed in natural language text is extremely demanding and only very recently has there been significant headway. While such argument mining focuses on logos (the content of what is said) evidence has demonstrated that using ethos (the character of the speaker) can sometimes be an even more powerful tool of influence. We study the UK parliamentary debates which furnish a rich source of ethos with linguistic material signalling the ethotic relationships between politicians. We then develop a novel deep modular recurrent neural network, DMRNN, approach and employ proven methods from argument mining and sentiment analysis to create an ethos mining pipeline. Annotation of ethotic statements is reliable and its extraction is robust (macro-F1 = 0.83), while annotation of polarity is perfect and its extraction is solid (macro-F1 = 0.84). By exploring correspondences between ethos in political discourse and major events in the political landscape through ethos analytics, we uncover tantalising evidence that identifying expressions of positive and negative ethotic sentiment is a powerful instrument for understanding the dynamics of governments.

The simulation of mathematical reasoning has been a driving force throughout the history of Artificial Intelligence research. However, despite significant successes in computer mathematics, computers are not widely used by mathematicians apart from their quotidian applications. An oft-cited reason for this is that current computational systems cannot do mathematics in the way that humans do. We draw on two areas in which Automated Theorem Proving (ATP) is currently unlike human mathematics: firstly in a focus on soundness, rather than understandability of proof, and secondly in social aspects. Employing techniques and tools from argumentation to build a framework for mixed-initiative collaboration, we develop three complementary arcs. In the first arc – our theoretical model – we interpret the informal logic of mathematical discovery proposed by Lakatos, a philosopher of mathematics, through the lens of dialogue game theory and in particular as a dialogue game ranging over structures of argumentation. In our second arc – our abstraction level – we develop structured arguments, from which we induce abstract argumentation systems and compute the argumentation semantics to provide labelings of the acceptability status of each argument. The output from this stage corresponds to a final, or currently accepted proof artefact, which can be viewed alongside its historical development. Finally, in the third arc – our computational model – we show how each of these formal steps is available in implementation. In an appendix, we demonstrate our approach with a formal, implemented example of real-world mathematical collaboration. We conclude the paper with reflections on our mixed-initiative collaborative approach.

Governments around the world are increasingly utilising online platforms and social media to engage with, and ascertain the opinions of, their citizens. Whilst policy makers could potentially benefit from such enormous feedback from society, they first face the challenge of making sense out of the large volumes of data produced. In this article, we show how the analysis of argumentative and dialogical structures allows for the principled identification of those issues that are central, controversial, or popular in an online corpus of debates. Although areas such as controversy mining work towards identifying issues that are a source of disagreement, by looking at the deeper argumentative structure, we show that a much richer understanding can be obtained. We provide results from using a pipeline of argument-mining techniques on the debate corpus, showing that the accuracy obtained is sufficient to automatically identify those issues that are key to the discussion, attracting proportionately more support than others, and those that are divisive, attracting proportionately more conflicting viewpoints.

Debating technologies, a newly emerging strand of research into computational technologies to support human debating, offer a powerful way of providing naturalistic, dialogue-based interaction with complex information spaces. The full potential of debating technologies for dialogical argument can, however, only be realized once key technical and engineering challenges are overcome, namely data structure, data availability, and interoperability between components. Our aim in this article is to show that the Argument Web, a vision for integrated, reusable, semantically rich resources connecting views, opinions, arguments, and debates online, offers a solution to these challenges. Through the use of a running example taken from the domain of citizen dialogue, we demonstrate for the first time that different Argument Web components focusing on sensemaking, engagement, and analytics can work in concert as a suite of debating technologies for rich, complex, dialogical argument.

Despite the fact it has been recognised since Aristotle that ethos and credibility play a critical role in many types of communication, these facts are rarely studied in linguistically oriented AI which has enjoyed such success in processing complex features as sentiment, opinion, and most recently arguments. This paper shows how a text analysis pipeline of structural and statistical approaches to natural language processing (NLP) can be deployed to tackle ethos by mining linguistic resources from the political domain. We summarise a coding scheme for annotating ethotic expressions; present the first openly available corpus to support further, comparative research in the area; and report results from a system for automatically recognising the presence and polarity of ethotic expressions. Finally, we hypothesise that in the political sphere, ethos analytics – including recognising who trusts whom and who is attacking whose reputation – might act as a powerful toolset for understanding and even anticipating the dynamics of governments. By exploring several examples of correspondence between ethos analytics in political discourse and major events and dynamics in the political landscape, we uncover tantalising evidence in support of this hypothesis.

We propose a dialogue game for mediation and its formalization in DGDL. This dialectical system is available as software through Arvina for automatic execution. This work expands the literature in dialectical systems, in particular those for more than two players, and shows the practical impact on mediation activity through the opportunity offered to mediators once implemented.

Rapid growth in the area of argument mining has resulted in an ever increasing volume of analysed argument data. Being able to store information about arguments people make in favour or against different opinions, decisions and actions is a highly valuable resource, yet extremely challenging for sense-making. How, for example, can an analyst quickly check whether in a corpus of citizen dialogue people tend to rather agree or disagree with new policies proposed by the department of transportation; how can she get an insight into the interactions typical of this specific dialogical context; how can the general public easily see which presidential candidate is currently winning the debate by being able to successfully defend his arguments? In this paper, we propose Argument Analytics – a suite of techniques which provide interpretation of, and insight into, large-scale argument data for both specialist and general audiences.

Argument mining has started to yield early results in automatic analysis of text to produce representations of reason-conclusion structures. This paper addresses for the first time the question of automatically extracting such structures from dialogical settings of argument. More specifically, we introduce theoretical foundations for dialogical argument mining as well as show the initial implementation in a software for dialogue processing, and the application in corpus analysis. We combine analysis of illocutionary structure with structured argumentation frameworks as our scaffolding, and apply a combination of statistical and grammatically based analytical techniques.

The aim of the paper is to show the potential of applying computational models of argument in the financial domain to the analysis of Earnings Conference Calls. We propose the formal description of ECC dialogue games: its locution rules and protocol. We also test its descriptive validity on an annotated corpus.

The aim of the paper is to propose a game-theoretic description of strategies available to players in dialogues. We show how existing dialogical systems can be formalized as Nash-style games, and how the game-theoretic concept of solutions (dominant strategies, Nash equilibrium) can be used to analyse these systems. Our first study, discussed in this article, describes the game DC introduced by Mackenzie.

We present a dialogue game representation of Lakatos's theory of justification and discovery in mathematics and describe our implementation of the game. As well as demonstrating a new domain for dialogue games, this could provide the basis for systems which can be used to aid mathematicians in their work.

The paper proposes a dialogue system LND which brings together and unifies two traditions in studying dialogue as a game: the dialogical logic introduced by Lorenzen; and persuasion dialogue games as specified by Prakken. The first approach allows th

The paper proposes a new perspective on modelling ad hominem (AH) techniques in a dialogue. The approach is built upon the following assumptions: (i) that ad hominem is not an inferential, but undercutting structure; (ii) that it can be a non-fallacious dialectical technique in some communicative contexts; and (iii) that critical questions in Walton's AH scheme can be used to determine strategies of defending against AH attack. We aim to achieve two goals in this paper: first, to represent the deep ontological structure of dialogues with the speaker's character attacks and defense; and second, to design a game AdHD in which a personal attack, and defense against it, are legitimate dialogue moves.

The aim of the paper is to extend the syntax and semantics of the AGnlogic with components allowing the representation and verification of strategies in persuasion dialogue games. The AGnlogic was introduced to express beliefs and persuasive actions of agents, and then it was used to perform model checking in persuasive inter-agent communication. In this paper, we enrich AGnby adapting strategy operators from Alternating-time Temporal Logic and adding new ones to allow reasoning about the success and persuasive power of dialogues.

The aim of the paper is to propose a robust model of interpersonal argumentation (IP). The IP-arguments directly address participants of communication, i.e. they refer to speech acts rather than to propositional contents. Argumentation theory recognizes several IP-arguments, e.g. argument from position to know or ad hominem arguments. The model proposed in the paper enables to describe references to different types of speech acts – not only assertives, but also commissives and directives. The IP-arguments are assumed to be warranted by the component of authorizing an agent to perform a given speech act. Consequently, the wider class of IP-communication can be expressed in the extended model, such as e.g. the structure of generic ad hominem can be explicitly represented as the undercutter.

This paper builds upon the proposed dialogical extensions to the AIF, termed AIF+, by making explicit the representation of the role of illocutionary force in the connection between argument structures and dialogical structures. Illocutionary force is realised in the form of Illocutionary Application (YA-) nodes that provide an explicit linkage between the locutions uttered during a dialogue and the underlying arguments expressed by the content of those locutions. This linkage is explored in the context of two contrasting dialogue games from the literature, demonstrating how the approach can support the development of computational models in which the speech-act function of communicative moves can be accounted for.

The aim of the paper is to propose how to enrich a formal model of persuasion with a specification for actions which are typical for persuasion process. First, since these actions are verbal, they influence a receiver but do not change the agent’s environment. In a formal framework, we represent them as actions that change not the particular state of a model, but the whole model. Second, effects of those actions depend on how much the receiver trusts the persuader. To formally model this phenomenon, we use a trust function. Finally, we want to represent uncertainty in terms of probability. Thus far, our model did not allow to express those properties of the persuasion process. Therefore, in this paper we extend Multimodal Logic of Actions and Graded Beliefs (AG n ) with Probabilistic Dynamic Epistemic Logic (PDEL) and elements of Reputation Management framework (RM). Incorporation of PDEL into the model of persuasion requires some modifications of PDEL. Such extended model is then used to enrich Perseus – our software tool that enables to examine persuasive multi-agent systems. New components of the tool allow us to execute parametric verification of the different properties related to updating probabilistic beliefs in persuasion.

The aim of the paper is to present the software tool Perseus and show how it can be used to examine multi-agent systems where the ability to persuade is specified. Especially we want to study the issues such as: what arguments individuals use to successfully convince others, what type of a persuader guarantees a victory etc. This work describes implementation of the tool and discusses what questions about persuasion process Perseus can answer and how it is done.