ARCNN framework for multimodal infodemic detection.

Raj, C; Meel, P

ARCNN framework for multimodal infodemic detection.

Raj, C

Meel, P

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Neural Netw, 2022, 146, pp. 36-68
Issue Date:: 2022-02

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Field	Value	Language
dc.contributor.author	Raj, C https://orcid.org/0000-0003-0083-6812
dc.contributor.author	Meel, P
dc.date.accessioned	2023-04-03T22:26:44Z
dc.date.available	2021-11-04
dc.date.available	2023-04-03T22:26:44Z
dc.date.issued	2022-02
dc.identifier.citation	Neural Netw, 2022, 146, pp. 36-68
dc.identifier.issn	0893-6080
dc.identifier.issn	1879-2782
dc.identifier.uri	http://hdl.handle.net/10453/169087
dc.description.abstract	Fake news and misinformation have adopted various propagation media over time, nowadays spreading predominantly through online social networks. During the ongoing COVID-19 pandemic, false information is affecting human life in many spheres The world needs automated detection technology and efforts are being made to meet this requirement with the use of artificial intelligence. Neural network detection mechanisms are robust and durable and hence are used extensively in fake news detection. Deep learning algorithms demonstrate efficiency when they are provided with a large amount of training data. Given the scarcity of relevant fake news datasets, we built the Coronavirus Infodemic Dataset (CovID), which contains fake news posts and articles related to coronavirus. This paper presents a novel framework, the Allied Recurrent and Convolutional Neural Network (ARCNN), to detect fake news based on two different modalities: text and image. Our approach uses recurrent neural networks (RNNs) and convolutional neural networks (CNNs) and combines both streams to generate a final prediction. We present extensive research on various popular RNN and CNN models and their performance on six coronavirus-specific fake news datasets. To exhaustively analyze performance, we present experimentation performed and results obtained by combining both modalities using early fusion and four types of late fusion techniques. The proposed framework is validated by comparisons with state-of-the-art fake news detection mechanisms, and our models outperform each of them.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Neural Netw
dc.relation.isbasedon	10.1016/j.neunet.2021.11.006
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	COVID-19
dc.subject.mesh	Disinformation
dc.subject.mesh	Humans
dc.subject.mesh	Infodemic
dc.subject.mesh	Pandemics
dc.subject.mesh	SARS-CoV-2
dc.subject.mesh	Social Media
dc.subject.mesh	Humans
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Pandemics
dc.subject.mesh	Social Media
dc.subject.mesh	COVID-19
dc.subject.mesh	SARS-CoV-2
dc.subject.mesh	Disinformation
dc.subject.mesh	Infodemic
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	COVID-19
dc.subject.mesh	Disinformation
dc.subject.mesh	Humans
dc.subject.mesh	Infodemic
dc.subject.mesh	Pandemics
dc.subject.mesh	SARS-CoV-2
dc.subject.mesh	Social Media
dc.title	ARCNN framework for multimodal infodemic detection.
dc.type	Journal Article
utslib.citation.volume	146
utslib.location.activity	United States
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-03T22:26:37Z
pubs.publication-status	Published
pubs.volume	146

Abstract:

Fake news and misinformation have adopted various propagation media over time, nowadays spreading predominantly through online social networks. During the ongoing COVID-19 pandemic, false information is affecting human life in many spheres The world needs automated detection technology and efforts are being made to meet this requirement with the use of artificial intelligence. Neural network detection mechanisms are robust and durable and hence are used extensively in fake news detection. Deep learning algorithms demonstrate efficiency when they are provided with a large amount of training data. Given the scarcity of relevant fake news datasets, we built the Coronavirus Infodemic Dataset (CovID), which contains fake news posts and articles related to coronavirus. This paper presents a novel framework, the Allied Recurrent and Convolutional Neural Network (ARCNN), to detect fake news based on two different modalities: text and image. Our approach uses recurrent neural networks (RNNs) and convolutional neural networks (CNNs) and combines both streams to generate a final prediction. We present extensive research on various popular RNN and CNN models and their performance on six coronavirus-specific fake news datasets. To exhaustively analyze performance, we present experimentation performed and results obtained by combining both modalities using early fusion and four types of late fusion techniques. The proposed framework is validated by comparisons with state-of-the-art fake news detection mechanisms, and our models outperform each of them.

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http://hdl.handle.net/10453/169087