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arXiv · 2506.11919

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

Greta Damo·Elena Cabrio·Serena Villata
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Abstract

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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Greta Damo, Elena Cabrio, Serena Villata. 2025-06-13. Effectiveness of Counter-Speech against Abusive Content: A Multidimensional Annotation and Classification Study. https://arxiv.org/abs/2506.11919

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