Article · Displays · July 23, 2025

A joint learning framework for fake news detection

Muhammad Abdullah, Hongying Zan, Arifa Javed, Orken J. Mamyrbayev, Fabio Caraffini, Hassan Eshkiki

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.

Misinformation and Its ImpactsTopic ModelingSpam and Phishing Detectionfake news detectionmisinformation detectionmulti-task learning

A joint learning framework for fake news detection

Abstract

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