  In this paper, we collect and study Twitter communications to understand the
societal impact of COVID-19 in the United States during the early days of the
pandemic. With infections soaring rapidly, users took to Twitter asking people
to self isolate and quarantine themselves. Users also demanded closure of
schools, bars, and restaurants as well as lockdown of cities and states. We
methodically collect tweets by identifying and tracking trending COVID-related
hashtags. We first manually group the hashtags into six main categories,
namely, 1) General COVID, 2) Quarantine, 3) Panic Buying, 4) School Closures,
5) Lockdowns, and 6) Frustration and Hope}, and study the temporal evolution of
tweets in these hashtags. We conduct a linguistic analysis of words common to
all hashtag groups and specific to each hashtag group and identify the chief
concerns of people as the pandemic gripped the nation (e.g., exploring bidets
as an alternative to toilet paper). We conduct sentiment analysis and our
investigation reveals that people reacted positively to school closures and
negatively to the lack of availability of essential goods due to panic buying.
We adopt a state-of-the-art semantic role labeling approach to identify the
action words and then leverage a LSTM-based dependency parsing model to analyze
the context of action words (e.g., verb deal is accompanied by nouns such as
anxiety, stress, and crisis). Finally, we develop a scalable seeded topic
modeling approach to automatically categorize and isolate tweets into hashtag
groups and experimentally validate that our topic model provides a grouping
similar to our manual grouping. Our study presents a systematic way to
construct an aggregated picture of peoples' response to the pandemic and lays
the groundwork for future fine-grained linguistic and behavioral analysis.
