  As the COVID-19 pandemic swept over the world, people discussed facts,
expressed opinions, and shared sentiments on social media. Since the reaction
to COVID-19 in different locations may be tied to local cases, government
regulations, healthcare resources and socioeconomic factors, we curated a large
geo-tagged Twitter dataset and performed exploratory analysis by location.
Specifically, we collected 650,563 unique geo-tagged tweets across the United
States (50 states and Washington, D.C.) covering the date range from January 25
to May 10, 2020. Tweet locations enabled us to conduct region-specific studies
such as tweeting volumes and sentiment, sometimes in response to local
regulations and reported COVID-19 cases. During this period, many people
started working from home. The gap between workdays and weekends in hourly
tweet volumes inspired us to propose algorithms to estimate work engagement
during the COVID-19 crisis. This paper also summarizes themes and topics of
tweets in our dataset using both social media exclusive tools (i.e., #hashtags,
@mentions) and the latent Dirichlet allocation model. We welcome requests for
data sharing and conversations for more insights.
  Dataset link: http://covid19research.site/geo-tagged_twitter_datasets/
