Abstract: The rise of online hate speech and its significant societal harms have made automatic hate speech detection a critical task.Existing methods overlook the impact of hate objects on semantic extraction for hate speech detection,leading to inadequate contextual feature extraction and susceptibility to decision errors induced by specific expressions.Meanwhile,these methods fail to consider the interference of variant words on semantic extraction,resulting in a high miss rate in hate speech detection.Furthermore,the field of Chinese hate speech detection lacks the support of available datasets.To tackle these challenges,this paper proposes a hate speech detection method incorporating hate object features and variant word restoration mechanism.The method treats hate object recognition as an intermediate task,guiding the model to fully learn the contextual features of hate objects,thereby enhancing text comprehension in hate speech detection.Additionally,a variant word restoration module fine-tuned based on ChatGLM2-6B is proposed.It aims to effectively reduce the interference of variant words on hate speech detection by restoring variant words to their normal equivalents.Finally,a Chinese hate speech dataset is also presented to facilitate further research in this field.Experimental results verify that the proposed method achieves a 96.71% F1 score,outperforming baseline methods in all metrics.Specifically,the model exhibits a 4.21% improvement in detection accuracy for specific scenes and a 3.45% decrease in the miss rate caused by variant words.

Key words: Hate speech detection, Chinese dataset, Hate object recognition, Variant word restoration, Text enhancement, Natural language processing