A Short Survey on Fake News Detection in Pandemic Situation Towards Future Directions

In December 2019, numerous news posts regarding the situation of COVID-19 in electronic media, traditional print, and social media emerged. These media sources are getting information from both non-trusted and trusted medical healthcare systems. The fake news regarding the pandemic situation is spread rapidly in multimedia. The wide spread of small deceptive information can cause unwanted exposure and anxiety for taking medical remedies and gives tricks for digital marketing. These unwanted medical remedies may lead to deadly factors. Therefore, a model to detect fake content and misinformation from the multimedia news pool during the pandemic is essential. The purpose of this systematic literature review is to review the significant studies about misinformation and fake news during COVID-19 on social media. In this survey, the currently used deep structured architectures to detect FND are utilized in the post and pre-pandemic situation. This review also explores the algorithmic categorization, dataset details, performance metrics, and tools utilized for implementation. Finally, this review gives the research gaps towards the future direction that has been aroused in the FND approaches, and it provides the appropriate pathway for researchers to implement several improvements in the future for getting better outcomes with a limited number of input data.