  Since March 2020, companies nationwide have started work from home (WFH) due
to the rapid increase of confirmed COVID-19 cases in an attempt to help prevent
the coronavirus from spreading and rescue the economy from the pandemic. Many
organizations have conducted surveys to understand people's opinions towards
WFH. However, the findings are limited due to small sample size and the dynamic
topics over time. This study aims to understand the U.S. public opinions on
working from home during the COVID-19 pandemic. We conduct a large-scale social
media study using Twitter data to portrait different groups who have
positive/negative opinions about WFH. We perform an ordinary least squares
regression to investigate the relationship between the sentiment about WFH and
user characteristics including gender, age, ethnicity, median household income,
and population density. To better understand public opinion, we use latent
Dirichlet allocation to extract topics and discover how tweet contents relate
to people's attitudes. These findings provide evidence that sentiment about WFH
varies across user characteristics. Furthermore, the content analysis sheds
light on the nuanced differences in sentiment and reveals disparities relate to
WFH.
