Fake news stance detection using selective features and FakeNET

Aljrees, Turki; Cheng, Cheng; Ahmed, Mian Muhammad; Umer, Muhammad; Majeed, Rizwan; Alnowaiser, Khaled; Abuzinadah, Nihal; Ashraf, Imran

doi:10.1371/journal.pone.0287298

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Fake news stance detection using selective features and FakeNET

Turki Aljrees, Cheng Cheng

, Mian Muhammad Ahmed, Muhammad Umer

, Rizwan Majeed, Khaled Alnowaiser, Nihal Abuzinadah, Imran Ashraf

PLOS ONE, Volume: 18, Issue: 7, Start page: e0287298

Swansea University Author: Cheng Cheng

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DOI (Published version): 10.1371/journal.pone.0287298

Abstract

The proliferation of fake news has severe effects on society and individuals on multiple fronts. With fast-paced online content generation, has come the challenging problem of fake news content. Consequently, automated systems to make a timely judgment of fake news have become the need of the hour....

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Published in:	PLOS ONE
ISSN:	1932-6203
Published:	Public Library of Science (PLoS) 2023
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URI:	https://cronfa.swan.ac.uk/Record/cronfa65945
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spelling	v2 65945 2024-04-03 Fake news stance detection using selective features and FakeNET 11ddf61c123b99e59b00fa1479367582 0000-0003-0371-9646 Cheng Cheng Cheng Cheng true false 2024-04-03 MACS The proliferation of fake news has severe effects on society and individuals on multiple fronts. With fast-paced online content generation, has come the challenging problem of fake news content. Consequently, automated systems to make a timely judgment of fake news have become the need of the hour. The performance of such systems heavily relies on feature engineering and requires an appropriate feature set to increase performance and robustness. In this context, this study employs two methods for reducing the number of feature dimensions including Chi-square and principal component analysis (PCA). These methods are employed with a hybrid neural network architecture of convolutional neural network (CNN) and long short-term memory (LSTM) model called FakeNET. The use of PCA and Chi-square aims at utilizing appropriate feature vectors for better performance and lower computational complexity. A multi-class dataset is used comprising ‘agree’, ‘disagree’, ‘discuss’, and ‘unrelated’ classes obtained from the Fake News Challenges (FNC) website. Further contextual features for identifying bogus news are obtained through PCA and Chi-Square, which are given nonlinear characteristics. The purpose of this study is to locate the article’s perspective concerning the headline. The proposed approach yields gains of 0.04 in accuracy and 0.20 in the F1 score, respectively. As per the experimental results, PCA achieves a higher accuracy of 0.978 than both Chi-square and state-of-the-art approaches. Journal Article PLOS ONE 18 7 e0287298 Public Library of Science (PLoS) 1932-6203 31 7 2023 2023-07-31 10.1371/journal.pone.0287298 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Another institution paid the OA fee 2024-05-29T16:08:23.9423937 2024-04-03T17:23:01.8726296 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Turki Aljrees 1 Cheng Cheng 0000-0003-0371-9646 2 Mian Muhammad Ahmed 3 Muhammad Umer 0000-0002-6015-9326 4 Rizwan Majeed 5 Khaled Alnowaiser 6 Nihal Abuzinadah 7 Imran Ashraf 8 65945__30484__98aa775bf8174b978637c9d59a0a148e.pdf 65945.VoR.pdf 2024-05-29T16:07:00.4160180 Output 876274 application/pdf Version of Record true © 2023 Aljrees et al. This is an open access article distributed under the terms of the Creative Commons Attribution License. true eng http://creativecommons.org/licenses/by/4.0/
title	Fake news stance detection using selective features and FakeNET
spellingShingle	Fake news stance detection using selective features and FakeNET Cheng Cheng
title_short	Fake news stance detection using selective features and FakeNET
title_full	Fake news stance detection using selective features and FakeNET
title_fullStr	Fake news stance detection using selective features and FakeNET
title_full_unstemmed	Fake news stance detection using selective features and FakeNET
title_sort	Fake news stance detection using selective features and FakeNET
author_id_str_mv	11ddf61c123b99e59b00fa1479367582
author_id_fullname_str_mv	11ddf61c123b99e59b00fa1479367582_***_Cheng Cheng
author	Cheng Cheng
author2	Turki Aljrees Cheng Cheng Mian Muhammad Ahmed Muhammad Umer Rizwan Majeed Khaled Alnowaiser Nihal Abuzinadah Imran Ashraf
format	Journal article
container_title	PLOS ONE
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publishDate	2023
institution	Swansea University
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doi_str_mv	10.1371/journal.pone.0287298
publisher	Public Library of Science (PLoS)
college_str	Faculty of Science and Engineering
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description	The proliferation of fake news has severe effects on society and individuals on multiple fronts. With fast-paced online content generation, has come the challenging problem of fake news content. Consequently, automated systems to make a timely judgment of fake news have become the need of the hour. The performance of such systems heavily relies on feature engineering and requires an appropriate feature set to increase performance and robustness. In this context, this study employs two methods for reducing the number of feature dimensions including Chi-square and principal component analysis (PCA). These methods are employed with a hybrid neural network architecture of convolutional neural network (CNN) and long short-term memory (LSTM) model called FakeNET. The use of PCA and Chi-square aims at utilizing appropriate feature vectors for better performance and lower computational complexity. A multi-class dataset is used comprising ‘agree’, ‘disagree’, ‘discuss’, and ‘unrelated’ classes obtained from the Fake News Challenges (FNC) website. Further contextual features for identifying bogus news are obtained through PCA and Chi-Square, which are given nonlinear characteristics. The purpose of this study is to locate the article’s perspective concerning the headline. The proposed approach yields gains of 0.04 in accuracy and 0.20 in the F1 score, respectively. As per the experimental results, PCA achieves a higher accuracy of 0.978 than both Chi-square and state-of-the-art approaches.
published_date	2023-07-31T16:08:22Z
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Fake news stance detection using selective features and FakeNET

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