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

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 social science 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.

@article{damo2025_2506.11919,
  title={ Effectiveness of Counter-Speech against Abusive Content: A Multidimensional Annotation and Classification Study },
  author={ Greta Damo and Elena Cabrio and Serena Villata },
  journal={arXiv preprint arXiv:2506.11919},
  year={ 2025 }
}