A Comprehensive Study on NLP Data Augmentation for Hate Speech Detection: Legacy Methods, BERT, and LLMs

arXiv:2404.00303v1 Announce Type: new
Abstract: The surge of interest in data augmentation within the realm of NLP has been driven by the need to address challenges posed by hate speech domains, the dynamic nature of social media vocabulary, and the demands for large-scale neural networks requiring extensive training data. However, the prevalent use of lexical substitution in data augmentation has raised concerns, as it may inadvertently alter the intended meaning, thereby impacting the efficacy of supervised machine learning models. In …

abstract arxiv augmentation bert challenges cs.cl data detection domains dynamic hate speech hate speech detection llms media nature nlp realm scale social social media speech study type