ConvNet frameworks for multi-modal fake news detection

Raj, C; Meel, P

ConvNet frameworks for multi-modal fake news detection

Raj, C

Meel, P

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Applied Intelligence, 2021, 51, (11), pp. 8132-8148
Issue Date:: 2021-11-01

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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	2022-04-10T03:43:28Z
dc.date.available	2022-04-10T03:43:28Z
dc.date.issued	2021-11-01
dc.identifier.citation	Applied Intelligence, 2021, 51, (11), pp. 8132-8148
dc.identifier.issn	0924-669X
dc.identifier.issn	1573-7497
dc.identifier.uri	http://hdl.handle.net/10453/156016
dc.description.abstract	An upsurge of false information revolves around the internet. Social media and websites are flooded with unverified news posts. These posts are comprised of text, images, audio, and videos. There is a requirement for a system that detects fake content in multiple data modalities. We have seen a considerable amount of research on classification techniques for textual fake news detection, while frameworks dedicated to visual fake news detection are very few. We explored the state-of-the-art methods using deep networks such as CNNs and RNNs for multi-modal online information credibility analysis. They show rapid improvement in classification tasks without requiring pre-processing. To aid the ongoing research over fake news detection using CNN models, we build textual and visual modules to analyze their performances over multi-modal datasets. We exploit latent features present inside text and images using layers of convolutions. We see how well these convolutional neural networks perform classification when provided with only latent features and analyze what type of images are needed to be fed to perform efficient fake news detection. We propose a multi-modal Coupled ConvNet architecture that fuses both the data modules and efficiently classifies online news depending on its textual and visual content. We thence offer a comparative analysis of the results of all the models utilized over three datasets. The proposed architecture outperforms various state-of-the-art methods for fake news detection with considerably high accuracies.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Applied Intelligence
dc.relation.isbasedon	10.1007/s10489-021-02345-y
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	ConvNet frameworks for multi-modal fake news detection
dc.type	Journal Article
utslib.citation.volume	51
utslib.for	0801 Artificial Intelligence and Image Processing
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	2022-04-10T03:43:26Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	51
utslib.citation.issue	11

Abstract:

An upsurge of false information revolves around the internet. Social media and websites are flooded with unverified news posts. These posts are comprised of text, images, audio, and videos. There is a requirement for a system that detects fake content in multiple data modalities. We have seen a considerable amount of research on classification techniques for textual fake news detection, while frameworks dedicated to visual fake news detection are very few. We explored the state-of-the-art methods using deep networks such as CNNs and RNNs for multi-modal online information credibility analysis. They show rapid improvement in classification tasks without requiring pre-processing. To aid the ongoing research over fake news detection using CNN models, we build textual and visual modules to analyze their performances over multi-modal datasets. We exploit latent features present inside text and images using layers of convolutions. We see how well these convolutional neural networks perform classification when provided with only latent features and analyze what type of images are needed to be fed to perform efficient fake news detection. We propose a multi-modal Coupled ConvNet architecture that fuses both the data modules and efficiently classifies online news depending on its textual and visual content. We thence offer a comparative analysis of the results of all the models utilized over three datasets. The proposed architecture outperforms various state-of-the-art methods for fake news detection with considerably high accuracies.

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http://hdl.handle.net/10453/156016