The prompt spread of misleading information through recent information and communication technologies (ICT) admonishes social convention and credence. Developing trustworthy algorithms that can automatically identify fake content becomes increasingly difficult. We investigate a hybrid artificial intelligence (AI) strategy that integrates machine learning (ML) and deep learning (DL) to enhance fake news detection. The model’s deep learning entity evaluates confined text arrangements and inclusive text values using a Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) with an attention layer. Conventional machine learning classifiers, mostly Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR), are trained synchronously employing Term Frequency–Inverse Document Frequency (TF-IDF). A simple ensemble averaging strategy is used on both machine learning and deep learning predictions. The model demonstrates strong generalization across various text types when evaluated on the LIAR dataset and a Kaggle-style fake news dataset. The combined system performs noticeably better than each of the separate models in terms of accuracy, precision, recall, F1, and AUC.
Ibrahim et al. (Fri,) studied this question.