BBC-FND: An ensemble of deep learning framework for textual fake news detection

Abstract

A wide spread of false news over Online Social Network platforms (OSNs) causes numerous negative consequences. Several researchers proposed different models using machine learning (ML) and deep learning (DL) techniques to detect fake news. The performance analysis of these models exposed that they achieve low accuracy due to the major drawbacks like improper architectural design, unsuitability of the model on different datasets. To address these issues, a hybrid BERT-BiLSTM-CNN (BBC-FND) model is proposed. It comprises of three main layers: an embedding layer, a feature representation layer, and a classification layer. In the first layer, BERT is used to extract the contextual-dependent features among words from the news. A multichannel CNN and a stacked BiLSTM are employed to produce various key features in the feature extraction layer. The former extracts multiple features from the text and captures complex local patterns in word spatial relations, while the latter extracts global semantic features from the contextual feature vector. The resulting features are concatenated and fed as input to the FFN to predict the news as fake or real. The results exhibit that the BBC-FND model outperforms the SoTA techniques, achieving higher accuracy of 97.31%, 98.64%, 99.06% and 98.26% on four datasets respectively.