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A joint learning framework for fake news detection

Muhammad Abdullah Orcid Logo, Zan Hongying, Arifa Javed Orcid Logo, Orken Mamyrbayev Orcid Logo, Fabio Caraffini Orcid Logo, Hassan Eshkiki Orcid Logo

Displays, Volume: 90, Start page: 103154

Swansea University Authors: Fabio Caraffini Orcid Logo, Hassan Eshkiki Orcid Logo

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DOI (Published version): 10.1016/j.displa.2025.103154

Abstract

This paper presents a joint learning framework for fake news detection, introducing an Enhanced BERT model that integrates named entity recognition, relational feature classification, and Stance Detection through a unified multi-task approach. The model incorporates task-specific masking and hierarc...

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Published in: Displays
ISSN: 0141-9382
Published: Elsevier BV 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa70027
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last_indexed 2025-08-27T04:35:32Z
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spelling 2025-08-26T15:02:11.2705167 v2 70027 2025-07-24 A joint learning framework for fake news detection d0b8d4e63d512d4d67a02a23dd20dfdb 0000-0001-9199-7368 Fabio Caraffini Fabio Caraffini true false c9972b26a83de11ffe211070f26fe16b 0000-0001-7795-453X Hassan Eshkiki Hassan Eshkiki true false 2025-07-24 MACS This paper presents a joint learning framework for fake news detection, introducing an Enhanced BERT model that integrates named entity recognition, relational feature classification, and Stance Detection through a unified multi-task approach. The model incorporates task-specific masking and hierarchical attention mechanisms to capture both fine-grained and high-level contextual relationships across headlines and body text. Cross-task consistency losses are applied to ensure coherence and alignment with external factual knowledge. We analyse the average distance from components to the centroid of a news sample to differentiate genuine information from falsehoods in large-scale text data effectively. Experiments on two FakeNewsNet datasets show that our framework outperforms state-of-the-art models, with accuracy improvements of 2.17% and 1.03%. These results indicate the potential for applications needing detailed text processing, like automatic summarisation and misinformation detection. Journal Article Displays 90 103154 Elsevier BV 0141-9382 Joint learning; BERT; Semantics; NLP; Fake news; RFC; NER 1 12 2025 2025-12-01 10.1016/j.displa.2025.103154 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2025-08-26T15:02:11.2705167 2025-07-24T12:32:28.3611963 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Muhammad Abdullah 0009-0000-9434-7977 1 Zan Hongying 2 Arifa Javed 0009-0002-6112-6398 3 Orken Mamyrbayev 0000-0001-8318-3794 4 Fabio Caraffini 0000-0001-9199-7368 5 Hassan Eshkiki 0000-0001-7795-453X 6 70027__34991__8c51e7ab3ef44feaa61df6bed8f46ec3.pdf 70027.VoR.pdf 2025-08-26T14:59:03.9757496 Output 2845430 application/pdf Version of Record true © 2025 The Authors. This is an open access article under the CC BY license. true eng http://creativecommons.org/licenses/by/4.0/
title A joint learning framework for fake news detection
spellingShingle A joint learning framework for fake news detection
Fabio Caraffini
Hassan Eshkiki
title_short A joint learning framework for fake news detection
title_full A joint learning framework for fake news detection
title_fullStr A joint learning framework for fake news detection
title_full_unstemmed A joint learning framework for fake news detection
title_sort A joint learning framework for fake news detection
author_id_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb
c9972b26a83de11ffe211070f26fe16b
author_id_fullname_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb_***_Fabio Caraffini
c9972b26a83de11ffe211070f26fe16b_***_Hassan Eshkiki
author Fabio Caraffini
Hassan Eshkiki
author2 Muhammad Abdullah
Zan Hongying
Arifa Javed
Orken Mamyrbayev
Fabio Caraffini
Hassan Eshkiki
format Journal article
container_title Displays
container_volume 90
container_start_page 103154
publishDate 2025
institution Swansea University
issn 0141-9382
doi_str_mv 10.1016/j.displa.2025.103154
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description This paper presents a joint learning framework for fake news detection, introducing an Enhanced BERT model that integrates named entity recognition, relational feature classification, and Stance Detection through a unified multi-task approach. The model incorporates task-specific masking and hierarchical attention mechanisms to capture both fine-grained and high-level contextual relationships across headlines and body text. Cross-task consistency losses are applied to ensure coherence and alignment with external factual knowledge. We analyse the average distance from components to the centroid of a news sample to differentiate genuine information from falsehoods in large-scale text data effectively. Experiments on two FakeNewsNet datasets show that our framework outperforms state-of-the-art models, with accuracy improvements of 2.17% and 1.03%. These results indicate the potential for applications needing detailed text processing, like automatic summarisation and misinformation detection.
published_date 2025-12-01T05:25:32Z
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score 11.089967

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