Communication Dans Un Congrès Année : 2025

Effectiveness of Counter-Speech against Abusive Content: A Multidimensional Annotation and Classification Study

Résumé

Counter-speech (CS) is a key strategy for mitigating online Hate Speech (HS), yet defining the criteria to assess its effectiveness remains an open challenge. We propose a novel computational framework for CS effectiveness classification, grounded in linguistics, communication and argumentation concepts. Our framework defines six core dimensions -Clarity, Evidence, Emotional Appeal, Rebuttal, Audience Adaptation, and Fairness -which we use to annotate 4,214 CS instances from two benchmark datasets, resulting in a novel linguistic resource released to the community. In addition, we propose two classification strategies, multi-task and dependency-based, achieving strong results (0.94 and 0.96 average F1 respectively on both expert-and user-written CS), outperforming standard baselines, and revealing strong interdependence among dimensions.

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hal-05353882 , version 1 (07-11-2025)

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  • HAL Id : hal-05353882 , version 1

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Greta Damo, Elena Cabrio, Serena Villata. Effectiveness of Counter-Speech against Abusive Content: A Multidimensional Annotation and Classification Study. WI-IAT 2025 - 24th IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, Nov 2025, London, United Kingdom. ⟨hal-05353882⟩
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