  Misinformation during pandemic situations like COVID-19 is growing rapidly on
social media and other platforms. This expeditious growth of misinformation
creates adverse effects on the people living in the society. Researchers are
trying their best to mitigate this problem using different approaches based on
Machine Learning (ML), Deep Learning (DL), and Natural Language Processing
(NLP). This survey aims to study different approaches of misinformation
detection on COVID-19 in recent literature to help the researchers in this
domain. More specifically, we review the different methods used for COVID-19
misinformation detection in their research with an overview of data
pre-processing and feature extraction methods to get a better understanding of
their work. We also summarize the existing datasets which can be used for
further research. Finally, we discuss the limitations of the existing methods
and highlight some potential future research directions along this dimension to
combat the spreading of misinformation during a pandemic.
