  The recent global outbreak of the coronavirus disease (COVID-19) has spread
to all corners of the globe. The international travel ban, panic buying, and
the need for self-quarantine are among the many other social challenges brought
about in this new era. Twitter platforms have been used in various public
health studies to identify public opinion about an event at the local and
global scale. To understand the public concerns and responses to the pandemic,
a system that can leverage machine learning techniques to filter out irrelevant
tweets and identify the important topics of discussion on social media
platforms like Twitter is needed. In this study, we constructed a system to
identify the relevant tweets related to the COVID-19 pandemic throughout
January 1st, 2020 to April 30th, 2020, and explored topic modeling to identify
the most discussed topics and themes during this period in our data set.
Additionally, we analyzed the temporal changes in the topics with respect to
the events that occurred during this pandemic. We found out that eight topics
were sufficient to identify the themes in our corpus. These topics depicted a
temporal trend. The dominant topics vary over time and align with the events
related to the COVID-19 pandemic.
