  As a platform, Twitter has been a significant public space for discussion
related to the COVID-19 pandemic. Public social media platforms such as Twitter
represent important sites of engagement regarding the pandemic and these data
can be used by research teams for social, health, and other research.
Understanding public opinion about COVID-19 and how information diffuses in
social media is important for governments and research institutions. Twitter is
a ubiquitous public platform and, as such, has tremendous utility for
understanding public perceptions, behavior, and attitudes related to COVID-19.
In this research, we present CML-COVID, a COVID-19 Twitter data set of
19,298,967 million tweets from 5,977,653 unique individuals and summarize some
of the attributes of these data. These tweets were collected between March 2020
and July 2020 using the query terms coronavirus, covid and mask related to
COVID-19. We use topic modeling, sentiment analysis, and descriptive statistics
to describe the tweets related to COVID-19 we collected and the geographical
location of tweets, where available. We provide information on how to access
our tweet dataset (archived using twarc).

Social and Information Networks;Computers and Society;Human-Computer Interaction