A Deep Learning Model for Classification of COVID-19 Related Fake News

ABSTRACT

The health crisis caused by COVID-19 throws the whole world into the biggest emergency of the century. Moreover, the pandemic has become awful because of the spread of inadequate and fake news or information among common people. Fake news, gossip and misleading information are on the rise due to the popularity of web-based information sources among people, such as social media, news feeds, online blogs and e-news articles. Monitoring and identifying such fake stories is a prerequisite to cease unwanted panic in this pandemic. But carrying out this task manually is challenging and labour intensive. Computer-assisted pattern recognition can now be used to replace human contact thanks to developments in machine learning, deep learning models and natural language processing. This is also essential for accurately distinguishing between true and false information automatically. A hybrid deep learning classification model has been proposed here to identify and classify the fake news and misleading information on the ‘COVID-19 Fake News Dataset’ (taken from Mendeley) which is a collection of news or web article related to COVID-19. The proposed classification model has achieved an accuracy of 75.34% and outperforms the existing LSTM and BiLSTM techniques. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.