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

• URL: http://arxiv.org/abs/2506.11919v2
• Date: Mon, 13 Oct 2025 13:41:38 GMT
• Title: Effectiveness of Counter-Speech against Abusive Content: A Multidimensional Annotation and Classification Study
• Authors: Greta Damo, Elena Cabrio, Serena Villata,
• Abstract summary: We propose a novel computational framework for CS effectiveness classification.<n>Our framework defines six core dimensions - Clarity, Evidence, Emotional Appeal, Rebuttal, Audience Adaptation, and Fairness.<n>In addition, we propose two classification strategies, multi-task and dependency-based, achieving strong results.
• Score: 10.488285141408253
• License: http://creativecommons.org/licenses/by/4.0/
• 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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