Fake News Detection Using Deep Learning-Based Natural Language Processing Models

The rising popularity of social media websites has led to the increased usage of fake news, threatening the trust of the general public and the integrity of the society. As a result, the detection of fake news through deep learning approaches has now become an essential research topic. In this research study, the detection of fake news through deep learning approaches by utilizing natural language processing techniques is discussed. In the proposed approach, text processing and word embedding are used. The proposed approach uses deep learning techniques that give better syntactical as well as semantic insights of the news. The proposed approach uses different deep learning models such as Long Short-Term Memory (LSTM) Network and transformers. The experimental results show that the deep learning approach produces better accuracy, precision, recall, and F1-score values compared to existing machine learning approaches. The research study proves that deep learning approaches give accurate insights of the news and can be used as a scalable tool for fake news detection. Based on the experimental study of the research study, deep learning approaches can be implemented in the fake news detection system.