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Fake News Detection Using BERT Model with Joint Learning

  • Research Article-Computer Engineering and Computer Science
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Abstract

In the current Internet era, there exists rapid spread of fake news, which could lead to serious problems. Many artificial intelligence approaches have been deployed to address the problem; however, fake news detection remains a challenge. To detect a fake news, an understanding of certain actors, entities and the relation of between each word in a long text is essential. Many approaches fail to incorporate these attributes in a long text. We purpose a novel BERT approach with joint learning framework that combines relational features classification (RFC) and named entity recognition (NER). Experimenting on two real-world datasets, we observe the effectiveness of our proposed approach in three evaluation metrics: such as accuracy, F1, and area under the curve (AUC) scores. The uniqueness of our joint framework provides a meaningful weight to attributes, which leads to better performance compared to other baselines.

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Authors and Affiliations

  1. College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi Arabia

    Wesam Shishah

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  1. Wesam Shishah
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Correspondence to Wesam Shishah.

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Shishah, W. Fake News Detection Using BERT Model with Joint Learning. Arab J Sci Eng 46, 9115–9127 (2021). https://doi.org/10.1007/s13369-021-05780-8

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  • DOI: https://doi.org/10.1007/s13369-021-05780-8

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