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Disentangling Hate in Online Memes

Authors:

Roy Ka-Wei Lee,

Wen-Haw ChongAuthors Info & Claims

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

Pages 5138 - 5147

https://doi.org/10.1145/3474085.3475625

Published: 17 October 2021 Publication History

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.

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Cited By

Alhazmi AMahmud RIdris NMohamed Abo MEke C(2024)A systematic literature review of hate speech identification on Arabic Twitter data: research challenges and future directionsPeerJ Computer Science10.7717/peerj-cs.196610(e1966)Online publication date: 2-Apr-2024
https://doi.org/10.7717/peerj-cs.1966
Aranda Serna F(2024)Los memes como simbolos del discurso de odioMemes as symbols of hate speechVISUAL REVIEW. International Visual Culture Review / Revista Internacional de Cultura Visual10.62161/revvisual.v16.522216:2(241-253)Online publication date: 10-Apr-2024
https://doi.org/10.62161/revvisual.v16.5222
Thapa SNaseem ULee RNasim MChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Foreword for the First International Workshop on Multimodal Content Analysis for Social Good (MM4SG 2024)Companion Proceedings of the ACM Web Conference 202410.1145/3589335.3641307(1803-1804)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3641307
Show More Cited By

Index Terms

Disentangling Hate in Online Memes
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
    2. Natural language processing

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Information & Contributors

Information

Published In

cover image ACM Conferences

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

October 2021

5796 pages

ISBN:9781450386517

DOI:10.1145/3474085

General Chairs:
Heng Tao Shen
University of Electronic Science&Technology of China, China
,
Yueting Zhuang
Zhejiang University, China
,
John R. Smith
IBM, USA
,
Program Chairs:
Yang Yang
University of Electronic Science and Technology of China, China
,
Pablo Cesar
CWI&TU Delft, The Netherlands
,
Florian Metze
FACEBOOK, Inc., USA
,
Balakrishnan Prabhakaran
University of Texas at Dallas, USA

Copyright © 2021 ACM.

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Published: 17 October 2021

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Cited By

Alhazmi AMahmud RIdris NMohamed Abo MEke C(2024)A systematic literature review of hate speech identification on Arabic Twitter data: research challenges and future directionsPeerJ Computer Science10.7717/peerj-cs.196610(e1966)Online publication date: 2-Apr-2024
https://doi.org/10.7717/peerj-cs.1966
Aranda Serna F(2024)Los memes como simbolos del discurso de odioMemes as symbols of hate speechVISUAL REVIEW. International Visual Culture Review / Revista Internacional de Cultura Visual10.62161/revvisual.v16.522216:2(241-253)Online publication date: 10-Apr-2024
https://doi.org/10.62161/revvisual.v16.5222
Thapa SNaseem ULee RNasim MChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Foreword for the First International Workshop on Multimodal Content Analysis for Social Good (MM4SG 2024)Companion Proceedings of the ACM Web Conference 202410.1145/3589335.3641307(1803-1804)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3641307
Hee MCao RChakraborty TLee RChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Understanding (Dark) Humour with Internet Meme AnalysisCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3641249(1276-1279)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3641249
Lin HLuo ZGao WMa JWang BYang RChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Towards Explainable Harmful Meme Detection through Multimodal Debate between Large Language ModelsProceedings of the ACM Web Conference 202410.1145/3589334.3645381(2359-2370)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645381
Gandhi AAhir PAdhvaryu KShah PLohiya RCambria EPoria SHussain A(2024)Hate speech detection: A comprehensive review of recent worksExpert Systems10.1111/exsy.1356241:8Online publication date: 25-Feb-2024
https://doi.org/10.1111/exsy.13562
Yuan JYu YMittal GHall MSajeev SChen M(2024)Rethinking Multimodal Content Moderation from an Asymmetric Angle with Mixed-modality2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00834(8517-8527)Online publication date: 3-Jan-2024
https://doi.org/10.1109/WACV57701.2024.00834
Huertas-Tato JKoutlis CPapadopoulos SCamacho DKompatsiaris I(2024)A CLIP-based Siamese Approach for Meme Classification2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650898(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650898
Lin MHuang QLu QGuo XGuo T(2024)Attribute-enhanced Selection of Proper Demonstrations for Harmful Meme Detection2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD61410.2024.10580867(2264-2269)Online publication date: 8-May-2024
https://doi.org/10.1109/CSCWD61410.2024.10580867
Festus Ayetiran EÖzgöbek Ö(2024)A Review of Deep Learning Techniques for Multimodal Fake News and Harmful Languages DetectionIEEE Access10.1109/ACCESS.2024.340625812(76133-76153)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3406258
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