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Hate speech detection for social media posts is considered as a binary classification problem in existing approaches, largely neglecting distinct attributes of hate speeches from other sentimental types such as “aggressive” and “racist”. As these sentimental types constitute a significant major portion of data, the classification performance is compromised. Moreover, those classifiers often do not generalize well across different datasets due to a relatively small number of hate-class samples. In this paper, we adopt a one-class perspective for hate speech detection, where the detection classifier is trained with hate-class samples only. Our model employs a BERT-BiLSTM module for feature extraction and a one-class SVM for classification. A comprehensive evaluation with four benchmarking datasets demonstrates the better performance of our model than existing approaches, as well as the advantage of training our model with a combination of the four datasets.

Date classNe class svmOutlier detectionTransfer learning