Fake News Detection Using BERT Model with Joint Learning

摘要

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.