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

Rui Cao, Roy Ka-Wei Lee

Abstract
Academia and industry have developed machine learning and natural language processing models 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 improves 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 empirically examine the HateGAN generated hate speeches and show that the generated tweets are diverse, coherent, and relevant to hate speech detection.
Anthology ID:
2020.coling-main.557
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6327–6338
Language:
URL:
https://aclanthology.org/2020.coling-main.557
DOI:
10.18653/v1/2020.coling-main.557
Bibkey:
Cite (ACL):
Rui Cao and Roy Ka-Wei Lee. 2020. HateGAN: Adversarial Generative-Based Data Augmentation for Hate Speech Detection. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6327–6338, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
HateGAN: Adversarial Generative-Based Data Augmentation for Hate Speech Detection (Cao & Lee, COLING 2020)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/2020.coling-main.557.pdf
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