@inproceedings{sariyanto-etal-2025-towards,
title = "Towards Explainable Hate Speech Detection",
author = "Sariyanto, Happy Khairunnisa and
Ulucan, Diclehan and
Ulucan, Oguzhan and
Ebner, Marc",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.667/",
doi = "10.18653/v1/2025.findings-acl.667",
pages = "12883--12893",
ISBN = "979-8-89176-256-5",
abstract = "Recent advancements in deep learning have significantly enhanced the efficiency and accuracy of natural language processing (NLP) tasks. However, these models often require substantial computational resources, which remains a major drawback. Reducing the complexity of deep learning architectures, and exploring simpler yet effective approaches can lead to cost-efficient NLP solutions. This is also a step towards explainable AI, i.e., uncovering how a particular task is carried out. For this analysis, we chose the task of hate speech detection. We address hate speech detection by introducing a model that employs a weighted sum of valence, arousal, and dominance (VAD) scores for classification. To determine the optimal weights and classification strategies, we analyze hate speech and non-hate speech words based on both their individual and summed VAD-values. Our experimental results demonstrate that this straightforward approach can compete with state-of-the-art neural network methods, including GPT-based models, in detecting hate speech."
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%0 Conference Proceedings
%T Towards Explainable Hate Speech Detection
%A Sariyanto, Happy Khairunnisa
%A Ulucan, Diclehan
%A Ulucan, Oguzhan
%A Ebner, Marc
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F sariyanto-etal-2025-towards
%X Recent advancements in deep learning have significantly enhanced the efficiency and accuracy of natural language processing (NLP) tasks. However, these models often require substantial computational resources, which remains a major drawback. Reducing the complexity of deep learning architectures, and exploring simpler yet effective approaches can lead to cost-efficient NLP solutions. This is also a step towards explainable AI, i.e., uncovering how a particular task is carried out. For this analysis, we chose the task of hate speech detection. We address hate speech detection by introducing a model that employs a weighted sum of valence, arousal, and dominance (VAD) scores for classification. To determine the optimal weights and classification strategies, we analyze hate speech and non-hate speech words based on both their individual and summed VAD-values. Our experimental results demonstrate that this straightforward approach can compete with state-of-the-art neural network methods, including GPT-based models, in detecting hate speech.
%R 10.18653/v1/2025.findings-acl.667
%U https://aclanthology.org/2025.findings-acl.667/
%U https://doi.org/10.18653/v1/2025.findings-acl.667
%P 12883-12893
Markdown (Informal)
[Towards Explainable Hate Speech Detection](https://aclanthology.org/2025.findings-acl.667/) (Sariyanto et al., Findings 2025)
ACL
- Happy Khairunnisa Sariyanto, Diclehan Ulucan, Oguzhan Ulucan, and Marc Ebner. 2025. Towards Explainable Hate Speech Detection. In Findings of the Association for Computational Linguistics: ACL 2025, pages 12883–12893, Vienna, Austria. Association for Computational Linguistics.