This article introduces a method for detecting argumentative communities in semi-structured debates, which are supported by ad-populum argumentation schemes, as proposed by Walton. In these communities, opinions are characterized by their level of cohesion and the argumentative groups involved. In this context, we present a semi-structured argumentation formalism for analyzing social media debates and demonstrate how large language models can assist in instantiating the proposed formalism. We use conceptualizations, similarity and valued-based argumentation frameworks to assess argument cohesion and strength. Additionally, we model discursive interactions to analyze how stances influence one another and classify argumentative communities based on popular opinion argumentation schemes. We show that these communities can be modeled using such schemes, relying on public perception and consensus.
argumentation concerns the construction and evaluation of arguments according to their interactions. In Dung's abstract argumentation frameworks (AAFs), arguments interact negatively via an attack relation. Since then, a plethora of extensions have been proposed with the aim of expressing other common argument relationships. Two such proposals are bipolar argumentation frameworks (BAFs), in which both attack (negative) and support (positive) relations coexist; and frameworks with sets of attacking arguments (SETAFs), where attacks can be collective, originating from a set of arguments. In this paper, we show equivalences between a specific notion of support ( beta -semantics) and of joint attacks in argumentation, by providing direct translations from BAFs and SETAFs (and vice versa) in a one-to-one correspondence between (BAF) beta -complete and (SETAF) complete labellings, beta -grounded and grounded labellings, beta -preferred and preferred labellings, beta -stable and stable labellings, and beta -semi-stable and semi-stable labellings. Besides semantic equivalences, we show structural (or syntactic) equivalences between BAFs and SETAFs by finding subsets of them for which the proposed translations are each other's inverse up to isomorphism.
This paper considers various aspects of representing arguments and logical argumentation frameworks. We investigate different approaches to address consistency and minimality within such frameworks, arguing that these properties can, and in some cases should, be omitted from the definition of an argument. We analyze the relationship between how consistency is verified and the selection of attack rules, showing that this choice should align with the underlying logic. Based on these results, we propose compact representations of logical argumentation frameworks and examine methods for transforming one framework into another (e.g., a more concise version) without losing logical entailments.
Dung's theory of abstract argumentation provides a unified foundation to knowledge representation and reasoning. It models the acceptability of arguments through their attack and defense relations, a perspective referred to as the attack-defense paradigm shift. While formal argumentation has been conceptualized in terms of argumentation as inference, argumentation as dialogue, and argumentation as balancing, most developments in abstract argumentation have focused on the inference perspective. By contrast, the dialogue perspective remains less explored. In this article, we contribute to bridging this gap by introducing new notions of agent defense, extending abstract argumentation with explicit representations of agents and their roles in defending arguments. These notions account for both individual and collective defense, enabling richer models of multi-agent reasoning. We position our proposal within the literature by comparing it with three existing approaches that extend abstract argumentation with agency: social semantics, agent-reduction semantics, and agent-filtering semantics. Using a principle-based analysis, we evaluate the formal properties of these approaches and the behavioral differences between them. This paper broadens the focus of abstract argumentation from inference-oriented models toward dialogue-oriented and agent-centered perspectives. This aligns with ongoing developments described in the Handbook of Formal Argumentation and the International Conference on Computational Models of Argument (COMMA) literature, and contributes to the shift toward modeling complex, interactive reasoning in multi-agent systems.
Argumentation is the process of creating arguments for and against competing claims. Computational argumentation involves different ways of analyzing and reasoning upon arguments and their relations. More precisely, Argument Mining is the research field aiming at automatically identifying and classifying argument structures in text. The research field is mainly focussed on the extraction of explicit argument structures (i.e., claims and premises connected by support and attack relations). However, an even more challenging task consists in extracting implicit argument structures in text (e.g., enthymemes). These structures are particularly valuable to then address argument reasoning, e.g., on incomplete and uncertain information, to finally compute the set of acceptable arguments, i.e., argument justification and skepticism. In this paper, we present and compare current approaches and available datasets for the novel task of Implicit Argument Mining. Future work perspectives are discussed to pave the way to further studies in this direction.
