research-article

A Duo-generative Approach to Explainable Multimodal COVID-19 Misinformation Detection

Authors:

Dong WangAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 3623 - 3631

https://doi.org/10.1145/3485447.3512257

Published: 25 April 2022 Publication History

Abstract

This paper focuses on a critical problem of explainable multimodal COVID-19 misinformation detection where the goal is to accurately detect misleading information in multimodal COVID-19 news articles and provide the reason or evidence that can explain the detection results. Our work is motivated by the lack of judicious study of the association between different modalities (e.g., text and image) of the COVID-19 news content in current solutions. In this paper, we present a generative approach to detect multimodal COVID-19 misinformation by investigating the cross-modal association between the visual and textual content that is deeply embedded in the multimodal news content. Two critical challenges exist in developing our solution: 1) how to accurately assess the consistency between the visual and textual content of a multimodal COVID-19 news article? 2) How to effectively retrieve useful information from the unreliable user comments to explain the misinformation detection results? To address the above challenges, we develop a duo-generative explainable misinformation detection (DGExplain) framework that explicitly explores the cross-modal association between the news content in different modalities and effectively exploits user comments to detect and explain misinformation in multimodal COVID-19 news articles. We evaluate DGExplain on two real-world multimodal COVID-19 news datasets. Evaluation results demonstrate that DGExplain significantly outperforms state-of-the-art baselines in terms of the accuracy of multimodal COVID-19 misinformation detection and the explainability of detection explanations.

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https://doi.org/10.1109/TMM.2023.3330074
Zhong SPeng SLiu XZhu LXu XLi T(2024)Ecarnet: enhanced clue-ambiguity reasoning network for multimodal fake news detectionMultimedia Systems10.1007/s00530-023-01256-x30:1Online publication date: 1-Feb-2024
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Index Terms

A Duo-generative Approach to Explainable Multimodal COVID-19 Misinformation Detection
1. Computing methodologies
2. Information systems
  1. Information retrieval
  2. Information systems applications

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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April 25 - 29, 2022

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Cited By

Khalili HWimmer M(2024)Towards Improved XAI-Based Epidemiological Research into the Next Potential PandemicLife10.3390/life1407078314:7(783)Online publication date: 21-Jun-2024
https://doi.org/10.3390/life14070783
Wang JQian SHu JHong R(2024)Comment-Context Dual Collaborative Masked Transformer Network for Fake News DetectionIEEE Transactions on Multimedia10.1109/TMM.2023.333007426(5170-5180)Online publication date: 2024
https://doi.org/10.1109/TMM.2023.3330074
Zhong SPeng SLiu XZhu LXu XLi T(2024)Ecarnet: enhanced clue-ambiguity reasoning network for multimodal fake news detectionMultimedia Systems10.1007/s00530-023-01256-x30:1Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s00530-023-01256-x
Jangra AMukherjee SJatowt ASaha SHasanuzzaman M(2023)A Survey on Multi-modal SummarizationACM Computing Surveys10.1145/358470055:13s(1-36)Online publication date: 13-Jul-2023
https://dl.acm.org/doi/10.1145/3584700
Liao SOkpala ECheng LLi MVishwamitra NHu HLuo FCostello MSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Analysis of COVID-19 Offensive Tweets and Their TargetsProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599773(4473-4484)Online publication date: 6-Aug-2023
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Wu JHooi BSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)DECOR: Degree-Corrected Social Graph Refinement for Fake News DetectionProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599298(2582-2593)Online publication date: 6-Aug-2023
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Zong RZhang YShang LWang D(2023)ContrastFaux: Sparse Semi-supervised Fauxtography Detection on the Web using Multi-view Contrastive LearningProceedings of the ACM Web Conference 202310.1145/3543507.3583869(3994-4003)Online publication date: 30-Apr-2023
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Wilson AWilkes STeramoto YHale S(2023)Multimodal analysis of disinformation and misinformationRoyal Society Open Science10.1098/rsos.23096410:12Online publication date: 20-Dec-2023
https://doi.org/10.1098/rsos.230964
Wu LLong YGao CWang ZZhang Y(2023)MFIRInformation Fusion10.1016/j.inffus.2023.101944100:COnline publication date: 1-Dec-2023
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Comito CCaroprese LZumpano E(2023)Multimodal fake news detection on social media: a survey of deep learning techniquesSocial Network Analysis and Mining10.1007/s13278-023-01104-w13:1Online publication date: 1-Aug-2023
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