Arabic Fake News Detection Using Deep Learning - Nermin Abdelhakim Othman; Doaa S. Elzanfaly; Mostafa Mahmoud M. Elhawary (IEEE Access, 2024)
In recent years, the explosive growth of social media platforms has led to the rapid spread of vast amounts of false news and rumors on the internet. This disrupts various news sources such as online news outlets, radio and television stations, and newspapers, especially in Arab countries. Therefore, the fake news detection problem has been raised worldwide. Arabic research in this field is very little compared to English research. Previous researchers had used machine learning and deep learning techniques on a large scale, but recently they used pre-trained models in their studies. Our proposed model works by using the Arabic pre-trained Bidirectional Encoder Representations from Transformers (Arabic BERT) to extract features from the text, then uses a Convolutional Neural Network (1D-CNN or 2D-CNN) to reduce the size of the features and extract the important ones, then passes it to an artificial neural network to perform the classification process. In our experiment we introduce a novel hybrid system consists of two main parts. In the first part we try three Arabic pre-trained Bidirectional Encoder Representations from Transformers model (APBTM) which are AraBERT, GigaBERT or MARBERT, while in the second part, we use 1D-CNN or 2D-CNN. this leads to six architectures from this system. we make our experiment by train and evaluating every architecture using three datasets which are (Arabic News Stance (ANS), AraNews, and Covid19Fakes). A comparison is made between the proposed model and other modern models which used the same dataset. We made three sets of experiments depending on the used datasets. Each set includes a group of experiments, and then we present the results in tables. Our proposed model which is the hybrid model between AraBERT and 2D-CNN has achieved the best F1-scores of 0.6188,0.7837 and 0.8009 when using the ANS dataset, the Ara-News dataset, and the Covid19Fakes dataset respectively. Furthermore, the model reduces the training time by achieving better results with less number of epochs. The results indicate that the proposed model offers the best performance, with 71% accuracy in the Arabic News Stance (ANS) dataset outperforming the model made by Sorour and Abdelkader (2022) and the model made by Shishah (2022) that achieved accuracy of 67% and 66% respectively.