# Theme Aspect Argumentation Model for Handling Fallacies

arXiv: https://arxiv.org/abs/2205.15141 · HTML: https://mlsanity.com/paper/2205.15141/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 52 references that are in the map.
- First posted on arXiv 30 May 2022.

## Builds on (most cited first, 7 of 7)

- [Fine-Grained Analysis of Propaganda in News Articles](https://mlsanity.com/paper/1910.02517/) (2019)
- [Argumentation Mining in User-Generated Web Discourse](https://mlsanity.com/paper/1601.02403/) (2016)
- [Before Name-calling: Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation](https://mlsanity.com/paper/1802.06613/) (2018)
- [A General Account of Argumentation with Preferences](https://mlsanity.com/paper/1804.06763/) (2018)
- [Abstract Argumentation / Persuasion / Dynamics](https://mlsanity.com/paper/1705.10044/) (2017)
- [Block Argumentation](https://mlsanity.com/paper/1901.06378/) (2019)
- [Numerical Abstract Persuasion Argumentation for Expressing Concurrent Multi-Agent Negotiations](https://mlsanity.com/paper/2001.08335/) (2020)

## Built on by (most cited first, 1 of 1)

- [Discussion Graph Semantics of First-Order Logic with Equality for Reasoning about Discussion and Argumentation](https://mlsanity.com/paper/2406.12163/) (2024)

## Abstract (arXiv)

From daily discussions to marketing ads to political statements, information manipulation is rife. It is increasingly more important that we have the right set of tools to defend ourselves from manipulative rhetoric, or fallacies. Suitable techniques to automatically identify fallacies are being investigated in natural language processing research. However, a fallacy in one context may not be a fallacy in another context, so there is also a need to explain how and why it has come to be judged a fallacy. For the explainable fallacy identification, we present a novel approach to characterising fallacies through formal constraints, as a viable alternative to more traditional fallacy classifications by informal criteria. To achieve this objective, we introduce a novel context-aware argumentation model, the theme aspect argumentation model, which can do both: the modelling of a given argumentation as it is expressed (rhetorical modelling); and a deeper semantic analysis of the rhetorical argumentation model. By identifying fallacies with formal constraints, it becomes possible to tell whether a fallacy lurks in the modelled rhetoric with a formal rigour. We present core formal constraints for the theme aspect argumentation model and then more formal constraints that improve its fallacy identification capability. We show and prove the consequences of these formal constraints. We then analyse the computational complexities of deciding the satisfiability of the constraints.