@inproceedings{almohaimeed-etal-2025-towards,
title = "Towards Generalizable Generic Harmful Speech Datasets for Implicit Hate Speech Detection",
author = {Almohaimeed, Saad and
Almohaimeed, Saleh and
Turgut, Damla and
B{\"o}l{\"o}ni, Ladislau},
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-long.85/",
pages = "1582--1592",
ISBN = "979-8-89176-298-5",
abstract = "Implicit hate speech has increasingly been recognized as a significant issue for social media platforms. While much of the research has traditionally focused on harmful speech in general, the need for generalizable techniques to detect veiled and subtle forms of hate has become increasingly pressing. Based on lexicon analysis, we hypothesize that implicit hate speech is already present in publicly available harmful speech datasets but may not have been explicitly recognized or labeled by annotators. Additionally, crowdsourced datasets are prone to mislabeling due to the complexity of the task and often influenced by annotators' subjective interpretations. In this paper, we propose an approach to address the detection of implicit hate speech and enhance generalizability across diverse datasets by leveraging existing harmful speech datasets. Our method comprises three key components: influential sample identification, reannotation, and augmentation using Llama-3 70B and GPT-4o. Experimental results demonstrate the effectiveness of our approach in improving implicit hate detection, achieving a +12.9-point F1 score improvement compared to the baseline."
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%0 Conference Proceedings
%T Towards Generalizable Generic Harmful Speech Datasets for Implicit Hate Speech Detection
%A Almohaimeed, Saad
%A Almohaimeed, Saleh
%A Turgut, Damla
%A Bölöni, Ladislau
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-298-5
%F almohaimeed-etal-2025-towards
%X Implicit hate speech has increasingly been recognized as a significant issue for social media platforms. While much of the research has traditionally focused on harmful speech in general, the need for generalizable techniques to detect veiled and subtle forms of hate has become increasingly pressing. Based on lexicon analysis, we hypothesize that implicit hate speech is already present in publicly available harmful speech datasets but may not have been explicitly recognized or labeled by annotators. Additionally, crowdsourced datasets are prone to mislabeling due to the complexity of the task and often influenced by annotators’ subjective interpretations. In this paper, we propose an approach to address the detection of implicit hate speech and enhance generalizability across diverse datasets by leveraging existing harmful speech datasets. Our method comprises three key components: influential sample identification, reannotation, and augmentation using Llama-3 70B and GPT-4o. Experimental results demonstrate the effectiveness of our approach in improving implicit hate detection, achieving a +12.9-point F1 score improvement compared to the baseline.
%U https://aclanthology.org/2025.ijcnlp-long.85/
%P 1582-1592
Markdown (Informal)
[Towards Generalizable Generic Harmful Speech Datasets for Implicit Hate Speech Detection](https://aclanthology.org/2025.ijcnlp-long.85/) (Almohaimeed et al., IJCNLP-AACL 2025)
ACL
- Saad Almohaimeed, Saleh Almohaimeed, Damla Turgut, and Ladislau Bölöni. 2025. Towards Generalizable Generic Harmful Speech Datasets for Implicit Hate Speech Detection. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 1582–1592, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.