Abstract
The volume and speed of data generation in biomedical literature, social media, and other resources during the COVID-19 pandemic is unprecedented. This mountain of data is growing daily across PubMed, Twitter, Google Scholar, and the World Health Organization's COVID-19 database [1], naming a few. The recently published COVID-19 Twitter dataset may offer insights into multiple topics from compliance with social distancing to assembling homemade masks and mental health tips [2]. Beyond social media, the massive COVID-19 Open Research Dataset (CORD-19) has been assembled from tech giants like Microsoft, the Allen Institute for Artificial Intelligence, and Georgetown University's Center for Security and Emerging Technology [3]. This dataset houses over 12,000 full text articles in “machine-readable form” that can be ingested programmatically into computer software programs and analyzed using machine learning applications like natural language processing (NLP).
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