Detecting fake news for COVID-19 using deep learning: a review

The December of 2019, marked the start of one of the biggest pandemics that the human race had seen for some centuries. COVID-19 after originating from China was in full force and was spreading quickly. This, however, was different from the previous pandemics as this is the age of technology and social circles on the internet. Thus, a sinister form of situation arose where fake news and misinformation flooded social media. The situation got to the point that WHO termed it as an "infodemic". Thus, NLP was again implored to find a solution and massive research was conducted for the detection of fake news on these platforms. The success of fake news detection improved and by today i.e. in 2023 the techniques have matured quite a bit. Keeping both of these aspects in mind, we have conducted a detailed review on fake news detection techniques for COVID-19. We have discussed the collection of data by providing a deep analysis of 7 COVID-19 Fake News datasets. Moreover, during the analysis of different methodologies, domination of deep learning and hybrid models was observed - specifically ensemble of transformer based models. Additionally, we explored the practical implications of COVID-19 Fake News detectors as components in generative AI models and as browser extensions to keep the common people safe. Finally, we discussed the limitations in existing research and how it can be improved in the future by exploring multi-modal, feature rich and cross-lingual approaches.