Abstract: With the widespread use of social media,the issue of the spread of online hate speech has become increasingly severe,especially under the cover of anonymity on the Internet,allowing hate speech to spread rapidly,posing a serious challenge to the detection of hate speech.In order to effectively address this issue,this paper proposes a cross-lingual hate speech detection me-thod based on Multi-language Embedding Graph Convolutional Network(MEGCN).This method fully integrates the advantages of sequence modeling and graph modeling,and uses multi-language pre-trained models for feature extraction,thus being able to handle complex relationships between different languages.At the same time,this paper proposes a joint training method based on interpolation prediction to improve the accuracy and robustness of the model.Experiments on four public datasets show that MEGCN achieves better performance than all existing comparative models in the task of cross-lingual hate speech detection.This method not only maintains a high sequence modeling accuracy,but also effectively captures the structural relationships between texts,thereby improving the performance of the model in multi-language environments,especially in terms of semantic correspondence between different languages.

Key words: Hate speech detection, Graph convolutional network, Multi-language pre-trained model, Natural language processing