HACK: A Hierarchical Model for Fake News Detection

Li, Y; Ji, K; Ma, K; Chen, Z; Wu, J; Li, Y; Xu, G

HACK: A Hierarchical Model for Fake News Detection

Li, Y Ji, K Ma, K Chen, Z Wu, J Li, Y Xu, G

Permalink

Publisher:: Springer International Publishing
Publication Type:: Chapter
Citation:: Web Information Systems Engineering – WISE 2021, 2021, 13080 LNCS, pp. 565-572
Issue Date:: 2021-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

The embargo period expires on 1 Jan 2024

Adobe PDF

Download Accepted versionAdobe PDF (552.84 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Li, Y
dc.contributor.author	Ji, K
dc.contributor.author	Ma, K
dc.contributor.author	Chen, Z
dc.contributor.author	Wu, J
dc.contributor.author	Li, Y
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663
dc.date.accessioned	2022-01-19T00:34:18Z
dc.date.available	2022-01-19T00:34:18Z
dc.date.issued	2021-01-01
dc.identifier.citation	Web Information Systems Engineering – WISE 2021, 2021, 13080 LNCS, pp. 565-572
dc.identifier.isbn	9783030908874
dc.identifier.uri	http://hdl.handle.net/10453/153324
dc.description.abstract	Online social media sites have become the most powerful platform to share news nowadays. However, all kinds of unauthenticated news that are released online without strict limits may lead to the spread of fake news, which has become a synonym for social and political threats. The existing solutions to the fake news issue are mostly trying to construct a social graph network by integrating the news content and social context of the news, which may be restricted when lacking social context information. In this paper, we propose a model for text only, regardless of contextual information, and named it HACK (HierArchical deteCtion for faKe news), which can construct high-level combined features of spatial capsule vectors from low-level character features and phrase features by fusing a pre-trained language model and convolution network. The experimental results on real-life data show that the classification accuracy is significantly improved by our method comparing with the state-of-the-art methods.
dc.language	en
dc.publisher	Springer International Publishing
dc.relation.ispartof	Web Information Systems Engineering – WISE 2021
dc.relation.isbasedon	10.1007/978-3-030-90888-1_43
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	HACK: A Hierarchical Model for Fake News Detection
dc.type	Chapter
utslib.citation.volume	13080 LNCS
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/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2024-01-01T00:00:00+1000Z
dc.date.updated	2022-01-19T00:34:16Z
pubs.publication-status	Published
pubs.volume	13080 LNCS

Abstract:

Online social media sites have become the most powerful platform to share news nowadays. However, all kinds of unauthenticated news that are released online without strict limits may lead to the spread of fake news, which has become a synonym for social and political threats. The existing solutions to the fake news issue are mostly trying to construct a social graph network by integrating the news content and social context of the news, which may be restricted when lacking social context information. In this paper, we propose a model for text only, regardless of contextual information, and named it HACK (HierArchical deteCtion for faKe news), which can construct high-level combined features of spatial capsule vectors from low-level character features and phrase features by fusing a pre-trained language model and convolution network. The experimental results on real-life data show that the classification accuracy is significantly improved by our method comparing with the state-of-the-art methods.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/153324