Effectiveness of Counter-Speech against Abusive Content: A Multidimensional Annotation and Classification Study
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912644980539392 |
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| author | Damo, Greta Cabrio, Elena Villata, Serena |
| author_facet | Damo, Greta Cabrio, Elena Villata, Serena |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11919 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Effectiveness of Counter-Speech against Abusive Content: A Multidimensional Annotation and Classification Study Damo, Greta Cabrio, Elena Villata, Serena Computation and Language 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. |
| title | Effectiveness of Counter-Speech against Abusive Content: A Multidimensional Annotation and Classification Study |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2506.11919 |