  Automated hate speech detection in social media is a challenging task that
has recently gained significant traction in the data mining and Natural
Language Processing community. However, most of the existing methods adopt a
supervised approach that depended heavily on the annotated hate speech
datasets, which are imbalanced and often lack training samples for hateful
content. This paper addresses the research gaps by proposing a novel multitask
learning-based model, AngryBERT, which jointly learns hate speech detection
with sentiment classification and target identification as secondary relevant
tasks. We conduct extensive experiments to augment three commonly-used hate
speech detection datasets. Our experiment results show that AngryBERT
outperforms state-of-the-art single-task-learning and multitask learning
baselines. We conduct ablation studies and case studies to empirically examine
the strengths and characteristics of our AngryBERT model and show that the
secondary tasks are able to improve hate speech detection.
