Data Augmentation for Improving Explainability of Hate Speech Detection

The paper presents a novel data augmentation-based approach to develop explainable, deep learning models for hate speech detection. Hate speech is widely prevalent on online social media but difficult to detect automatically due to challenges of natural language processing and complexity of hate speech. Further, the decisions of the existing solutions possess constrained explainability since limited annotated data are available for training and testing of models. Therefore, this work proposes the use of text-based data augmentation for improving the performance and explainability of deep learning models. Techniques based on easy data augmentation, bidirectional encoder representations from transformers and back translation have been utilized for data augmentation. Convolutional neural networks and long short-term memory models are trained with augmented data and evaluated on two publicly available datasets for hate speech detection. Methods of LIME and integrated gradients are used to retrieve explanations of the deep learning models. A diagnostic study is conducted on test samples to check for improvement in the models as a result of the data augmentation. The experimental results verify that the proposed approach improves the explainability as well as the accuracy of hate speech detection.