Attention-based C-BiLSTM for fake news detection

Abstract

Social media platforms have radically transformed the creation and dissemination of news. Users can easily access this news in a fast and efficient manner. However, some users might post negative and fraudulent content in the form of comments or posts. Such content can constitute a threat to an individual or an organization. Therefore, the identification of fake news has become a major research field in natural language processing (NLP). The main challenge is to determine whether the news is real or fake. In this paper, we propose an attention-based convolutional bidirectional long short-term memory (AC-BiLSTM) approach for detecting fake news and classifying them into six categories. The evaluation of our proposed approach on a benchmarked dataset shows a significant improvement in accuracy rate in comparison with other existing classification models. In particular, this work contributes to the progress in the field of detecting fake news and confirms the feasibility of our proposed approach in classifying fake news on social media.