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Towards Complex Debate Understanding: Predicting Claim Impact Scores through the Modelling of Claim Interactions

Structured debates can be naturally modeled as argument graphs, with claims connected by support and attack relations, a representation formalised in Computational Argumentation Theory. In this paper, we propose a novel neural architecture that jointly models both the textual content of claims and their relational structure. Claims are encoded using contextualised embeddings and compressed through a feedforward compression layer. Then, a graph attention network explicitly captures attack/support interactions. Trained on real-world debates from the Kialo platform, our model predicts the distribution of user-assigned impact votes for each claim. It achieves a mean absolute error (MAE) of 0.068, significantly outperforming both text-only and structure-only baselines. Further experiments show strong out-of-domain generalisation across thematic clusters, as well as suggestive correlations between the model’s attention patterns and human voting behaviour. An analysis of linguistic and graph-based features suggests that the model relies on latent argumentative patterns as well as the text. Our findings also shed light on language differences between strong and weak claims, as determined by humans as well as by our best model.