  Since the classification of COVID-19 as a global pandemic, there have been
many attempts to treat and contain the virus. Although there is no specific
antiviral treatment recommended for COVID-19, there are several drugs that can
potentially help with symptoms. In this work, we mined a large twitter dataset
of 424 million tweets of COVID-19 chatter to identify discourse around drug
mentions. While seemingly a straightforward task, due to the informal nature of
language use in Twitter, we demonstrate the need of machine learning alongside
traditional automated methods to aid in this task. By applying these
complementary methods, we are able to recover almost 15% additional data,
making misspelling handling a needed task as a pre-processing step when dealing
with social media data.
