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 potential treatments . While seemingly a straightforward task, due to the informal nature of language use in Twitter, we demonstrate the need of machine learning  methods to aid in this task. By applying these methods we are able to recover almost 15\% additional data than with traditional methods, showing the need of more sophisticated approaches than just text matching .
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 .
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potential treatments
drug mentions
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alongside traditional automated
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methods
complementary methods,
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than with traditional methods, showing the need of more sophisticated approaches than just text matching
, making misspelling handling a needed task as a pre-processing step when dealing with social media data