  Social media has become an important communication channel during high impact
events, such as the COVID-19 pandemic. As misinformation in social media can
rapidly spread, creating social unrest, curtailing the spread of misinformation
during such events is a significant data challenge. While recent solutions that
are based on machine learning have shown promise for the detection of
misinformation, most widely used methods include approaches that rely on either
handcrafted features that cannot be optimal for all scenarios, or those that
are based on deep learning where the interpretation of the prediction results
is not directly accessible. In this work, we propose a data-driven solution
that is based on the ICA model, such that knowledge discovery and detection of
misinformation are achieved jointly. To demonstrate the effectiveness of our
method and compare its performance with deep learning methods, we developed a
labeled COVID-19 Twitter dataset based on socio-linguistic criteria.
