  During the COVID-19 pandemic, "Chinese Virus" emerged as a controversial term
for coronavirus. To some, it may seem like a neutral term referring to the
physical origin of the virus. To many others, however, the term is in fact
attaching ethnicity to the virus. While both arguments appear reasonable,
quantitative analysis of the term's real-world usage is lacking to shed light
on the issues behind the controversy. In this paper, we attempt to fill this
gap. To model the substantive difference of tweets with controversial terms and
those with non-controversial terms, we apply topic modeling and LIWC-based
sentiment analysis. To test whether "Chinese Virus" and "COVID-19" are
interchangeable, we formulate it as a classification task, mask out these
terms, and classify them using the state-of-the-art transformer models. Our
experiments consistently show that the term "Chinese Virus" is associated with
different substantive topics and sentiment compared with "COVID-19" and that
the two terms are easily distinguishable by looking at their context.
