Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

10-2021

Abstract

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 the 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.

Keywords

hate speech, hateful memes, multimodal, social media mining

Discipline

Artificial Intelligence and Robotics | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

First Page

5138

Last Page

5147

Identifier

10.1145/3474085.3475625

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3474085.3475625

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