Rethinking Hate Speech Detection on Social Media: Can LLMs Replace Traditional Models?

Fuente: arXiv

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Bibliographic Details
Main Authors:	Singh, Daman Deep, Bhattacharjee, Ramanuj, Chakraborty, Abhijnan
Format:	Preprint
Published:	2025
Subjects:	Computation and Language Computers and Society
Online Access:	Acceder al recurso
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_version_	1866918059874189312
author	Singh, Daman Deep Bhattacharjee, Ramanuj Chakraborty, Abhijnan
author_facet	Singh, Daman Deep Bhattacharjee, Ramanuj Chakraborty, Abhijnan
contents	Hate speech detection across contemporary social media presents unique challenges due to linguistic diversity and the informal nature of online discourse. These challenges are further amplified in settings involving code-mixing, transliteration, and culturally nuanced expressions. While fine-tuned transformer models, such as BERT, have become standard for this task, we argue that recent large language models (LLMs) not only surpass them but also redefine the landscape of hate speech detection more broadly. To support this claim, we introduce IndoHateMix, a diverse, high-quality dataset capturing Hindi-English code-mixing and transliteration in the Indian context, providing a realistic benchmark to evaluate model robustness in complex multilingual scenarios where existing NLP methods often struggle. Our extensive experiments show that cutting-edge LLMs (such as LLaMA-3.1) consistently outperform task-specific BERT-based models, even when fine-tuned on significantly less data. With their superior generalization and adaptability, LLMs offer a transformative approach to mitigating online hate in diverse environments. This raises the question of whether future works should prioritize developing specialized models or focus on curating richer and more varied datasets to further enhance the effectiveness of LLMs.
format	Preprint
id	arxiv_https___arxiv_org_abs_2506_12744
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Rethinking Hate Speech Detection on Social Media: Can LLMs Replace Traditional Models? Singh, Daman Deep Bhattacharjee, Ramanuj Chakraborty, Abhijnan Computation and Language Computers and Society Hate speech detection across contemporary social media presents unique challenges due to linguistic diversity and the informal nature of online discourse. These challenges are further amplified in settings involving code-mixing, transliteration, and culturally nuanced expressions. While fine-tuned transformer models, such as BERT, have become standard for this task, we argue that recent large language models (LLMs) not only surpass them but also redefine the landscape of hate speech detection more broadly. To support this claim, we introduce IndoHateMix, a diverse, high-quality dataset capturing Hindi-English code-mixing and transliteration in the Indian context, providing a realistic benchmark to evaluate model robustness in complex multilingual scenarios where existing NLP methods often struggle. Our extensive experiments show that cutting-edge LLMs (such as LLaMA-3.1) consistently outperform task-specific BERT-based models, even when fine-tuned on significantly less data. With their superior generalization and adaptability, LLMs offer a transformative approach to mitigating online hate in diverse environments. This raises the question of whether future works should prioritize developing specialized models or focus on curating richer and more varied datasets to further enhance the effectiveness of LLMs.
title	Rethinking Hate Speech Detection on Social Media: Can LLMs Replace Traditional Models?
topic	Computation and Language Computers and Society
url	https://arxiv.org/abs/2506.12744