HateGAN: Adversarial Generative-Based Data Augmentation for Hate Speech Detection

Proceedings of the 28th International Conference on Computational Linguistics, pages 6327–6338 Barcelona, Spain (Online), December 8-13, 2020 6327 HateGAN: Adversarial Generative-Based Data Augmentation for Hate Speech Detection Rui Cao Singapore Management University ruicao.2020@phdcs.smu.edu.sg Roy Ka-Wei Lee Singapore University of Technology and Design roy lee@sutd.edu.sg Abstract Academia and industry have developed machine learning and natural language processing mod- els to detect online hate speech automatically. However, most of these existing methods adopt a supervised approach that heavily depends on labeled datasets for training. This results in the methods’ poor detection performance of the hate speech class as the training datasets are highly imbalanced. In this paper, we propose HateGAN, a deep generative reinforcement learning model, which addresses the challenge of imbalance class by augmenting the dataset with hateful tweets. We conduct extensive experiments to augment two commonly-used hate speech detection datasets with the HateGAN generated tweets. Our experiment results show that HateGAN im- proves the detection performance of the hate speech class regardless of the classifiers and datasets used in the detection task. Specifically, we observe an average 5% improvement for the hate class F1 scores across all state-of-the-art hate speech classifiers. We also conduct case studies to em- pirically examine the HateGAN generated hate speeches and show that the generated tweets are diverse, coherent, and relevant to hate speech detection. 1 Introduction Motivation. The sharp increase in online hate speeches has raised concerns globally (Schmidt and Wiegand, 2017; Fortuna and Nunes, 2018). The spread of hate speech in social media has not only sowed discord among individuals or communities online but also resulted in violent hate crimes (Williams, 2019; Relia et al., 2019; Mathew et al., 2019). Therefore, it is a pressing issue to detect and curb hate speech in online social media. Major social media platforms such as Facebook and Twitter have made great efforts to combat the spread of hate speech in their platforms (Times, 2019; Bloomberg, 2019). Researchers have also proposed many traditional and deep learning hate speech classification methods to detect hate speeches in online social media automatically (Schmidt and Wiegand, 2017; Fortuna and Nunes, 2018). Most of these existing methods adopt a supervised approach that heavily depends on labeled datasets for training. This results in the methods’ poor detection performance of the hate speech class as the training datasets are highly imbalanced (Waseem and Hovy, 2016; Davidson et al., 2017). A potential solution to address the challenges of imbalance datasets is to perform data augmentation for the class with fewer training samples. For instance, perturbing replicas of data samples using noise injection or attribute modification techniques have been successful in other domains such as image and sound classification tasks (Shorten and Khoshgoftaar, 2019; Tran et al., 2017; Dong et al., 2016; Keren et al., 2016; Salamon and Bello, 2017). Nevertheless, these techniques are not transferable to text as they would break the text’s syntax and alter the semantics of the original sentences. There are also very few works that have explored improving hate speech detection …

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