Effective Fake News Classifier and its Applications to COVID-19

With the spread of the COVID-19 over the globe, it has conducted a large amount of misinformation and fake news on social networking sites. In this situation, when true and accurate information is necessary for public safety and health, fake news related to COVID-19 has spread rapidly, even quicker than the truth. Rational confusion can be caused by this fake news and put people’s lives in danger during times like the COVID-19 pandemic. We used the COVID-19 Fake News dataset to conduct a study to compare the effect of various machine learning-related approaches. We looked at different traditional machine learning models and deep learning language models for detecting fake news and compared their results in multiple ways. We discovered that LSTM and similar neural network models are the most effective at detecting fake news, especially with large datasets. We are confident that our benchmark study will assist the research community and various news blogs/sites to choose the best fake news detection algorithm.