  Hateful and offensive content detection has been extensively explored in a
single modality such as text. However, such toxic information could also be
communicated via multimodal content such as online memes. Therefore, detecting
multimodal hateful content has recently garnered much attention in academic and
industry research communities. This paper aims to contribute to this emerging
research topic by proposing DisMultiHate, which is a novel framework that
performed the classification of multimodal hateful content. Specifically,
DisMultiHate is designed to disentangle target entities in multimodal memes to
improve hateful content classification and explainability. We conduct extensive
experiments on two publicly available hateful and offensive memes datasets. Our
experiment results show that DisMultiHate is able to outperform
state-of-the-art unimodal and multimodal baselines in the hateful meme
classification task. Empirical case studies were also conducted to demonstrate
DisMultiHate's ability to disentangle target entities in memes and ultimately
showcase DisMultiHate's explainability of the multimodal hateful content
classification task.
