Journal article 283 views 181 downloads
A joint learning framework for fake news detection
,
Zan Hongying,
Arifa Javed ,
Orken Mamyrbayev ,
Fabio Caraffini ,
Hassan Eshkiki
Displays, Volume: 90, Start page: 103154
Swansea University Authors:
Fabio Caraffini , Hassan Eshkiki
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© 2025 The Authors. This is an open access article under the CC BY license.
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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...
| Published in: | Displays |
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| ISSN: | 0141-9382 |
| Published: |
Elsevier BV
2025
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa70027 |
| 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 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. |
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| Keywords: |
Joint learning; BERT; Semantics; NLP; Fake news; RFC; NER |
| College: |
Faculty of Science and Engineering |
| Funders: |
Swansea University |
| Start Page: |
103154 |