Leveraging Domain-Specific Word Embedding and Hate Concepts in Hate Speech Detection

Malicious spreading of hate speech on social media hinders the construction of a harmonious online environment, so efficient automatic hate detection become crucial. However, due to the short text form in which most hate speech exists, the semantic features of these texts are relatively sparse, making it difficult for models to learn enough knowledge for classification. Additionally, an increasing number of hate speech tends to use abbreviated and misspelled hate words to evade detection, making it challenging for many hate speech detection methods to capture hidden hate intents. In this paper, we propose a hate speech detection model based on multi-source feature fusion. The model not only takes into account hate concepts and semantic structure information in the target sentence, but also recognizes abbreviated and misspelled hate words by introducing domain-specific word embedding.Ultimately, it dynamically fuses multisource feature through the attention mechanism to enrich the expressive ability of feature vector and realize the effective detection of complex hate speech. We conduct detailed comparison and ablation experiments, and the results prove the effectiveness of our proposed model.