A joint learning framework for fake news detection
Abdullah M. Hongying Z. Javed A. Mamyrbayev O. Caraffini F. Eshkiki H.
December 2025Elsevier B.V.
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2025#90
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.
BERT , Fake news , Joint learning , NER , NLP , RFC , Semantics
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School of Computer Science and Artificial Intelligence, Zhengzhou University, Henan, Zhengzhou, 450001, China
Institute of Information and Computational Technologies, Almaty, Kazakhstan
Department of Computer Science, Swansea University, Swansea, SA1 8EN, United Kingdom
School of Computer Science and Artificial Intelligence
Institute of Information and Computational Technologies
Department of Computer Science
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