IEEE Access (Jan 2022)
JointBert for Detecting Arabic Fake News
Abstract
The rapid rise in the use of social media platforms has resulted in a recent surge of fake rumours, particularly among Arab countries. Such false information could potentially be detrimental to individuals and society. Detecting and blocking the spread of the fraudulent news in Arabic is critical. Many artificial intelligence algorithms, including contemporary transformer models, such as BERT, have been employed to detect the fake news in the past. Therefore, the fake news in Arabic can be detected using a revolutionary combined BERT architecture implemented in this paper. Extensive experiments were conducted to test the technique on real-world Arabic fake news datasets. In two of the fake news datasets, covid19fakes and Satirical, the suggested technique had a higher accuracy score than the current state-of-the-art Arabic fake news model. A comparable result can be achieved in other datasets; however, the proposed strategy fails to do so. All datasets except AraNews show an average F1 score improvement of 10% by implementing the proposed strategy. It was found that the proposed method was effective and superior to numerous other baselines of Arabic fake news detection models.
Keywords
