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Towards Explainable Harmful Meme Detection through Multimodal Debate between Large Language Models

Authors:

Ruichao YangAuthors Info & Claims

WWW '24: Proceedings of the ACM Web Conference 2024

Pages 2359 - 2370

https://doi.org/10.1145/3589334.3645381

Published: 13 May 2024 Publication History

Abstract

The age of social media is flooded with Internet memes, necessitating a clear grasp and effective identification of harmful ones. This task presents a significant challenge due to the implicit meaning embedded in memes, which is not explicitly conveyed through the surface text and image. However, existing harmful meme detection methods do not present readable explanations that unveil such implicit meaning to support their detection decisions. In this paper, we propose an explainable approach to detect harmful memes, achieved through reasoning over conflicting rationales from both harmless and harmful positions. Specifically, inspired by the powerful capacity of Large Language Models (LLMs) on text generation and reasoning, we first elicit multimodal debate between LLMs to generate the explanations derived from the contradictory arguments. Then we propose to fine-tune a small language model as the debate judge for harmfulness inference, to facilitate multimodal fusion between the harmfulness rationales and the intrinsic multimodal information within memes. In this way, our model is empowered to perform dialectical reasoning over intricate and implicit harm-indicative patterns, utilizing multimodal explanations originating from both harmless and harmful arguments. Extensive experiments on three public meme datasets demonstrate that our harmful meme detection approach achieves much better performance than state-of-the-art methods and exhibits a superior capacity for explaining the meme harmfulness of the model predictions.

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Cited By

Chen JLiu LZhou F(2025)Do not wait: Preemptive rumor detection with cooperative LLMs and accessible social contextInformation Processing & Management10.1016/j.ipm.2024.10399562:3(103995)Online publication date: May-2025
https://doi.org/10.1016/j.ipm.2024.103995
Sultan TRony MIslam MAldosary SEl-Shafai W(2024)MemesViTa: A Novel Multimodal Fusion Technique for Troll Memes IdentificationIEEE Access10.1109/ACCESS.2024.350561412(177811-177828)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3505614

Index Terms

Towards Explainable Harmful Meme Detection through Multimodal Debate between Large Language Models
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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cover image ACM Conferences

WWW '24: Proceedings of the ACM Web Conference 2024

May 2024

4826 pages

ISBN:9798400701719

DOI:10.1145/3589334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Chong-Wah Ngo
Singapore Management University
,
Proceedings Chair:
Roy Ka-Wei Lee
Singapore University of Technology and Design
,
Program Chairs:
Ravi Kumar
Google
,
Hady W. Lauw
Singapore Management University

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Published: 13 May 2024

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Cited By

Chen JLiu LZhou F(2025)Do not wait: Preemptive rumor detection with cooperative LLMs and accessible social contextInformation Processing & Management10.1016/j.ipm.2024.10399562:3(103995)Online publication date: May-2025
https://doi.org/10.1016/j.ipm.2024.103995
Sultan TRony MIslam MAldosary SEl-Shafai W(2024)MemesViTa: A Novel Multimodal Fusion Technique for Troll Memes IdentificationIEEE Access10.1109/ACCESS.2024.350561412(177811-177828)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3505614

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