Feature Selection Empowered BERT for Detection of Hate Speech with Vocabulary Augmentation
This work addresses the challenge of scalable and adaptive abusive content moderation for social media platforms, though it is incremental as it builds on existing BERT methods.
The paper tackled the problem of detecting hate speech on social media by fine-tuning BERT with a data-efficient strategy, achieving competitive performance while reducing training set size by 25% through TF-IDF-based sample selection and vocabulary augmentation with domain-specific slang.
Abusive speech on social media poses a persistent and evolving challenge, driven by the continuous emergence of novel slang and obfuscated terms designed to circumvent detection systems. In this work, we present a data efficient strategy for fine tuning BERT on hate speech classification by significantly reducing training set size without compromising performance. Our approach employs a TF IDF-based sample selection mechanism to retain only the most informative 75 percent of examples, thereby minimizing training overhead. To address the limitations of BERT's native vocabulary in capturing evolving hate speech terminology, we augment the tokenizer with domain-specific slang and lexical variants commonly found in abusive contexts. Experimental results on a widely used hate speech dataset demonstrate that our method achieves competitive performance while improving computational efficiency, highlighting its potential for scalable and adaptive abusive content moderation.