Since March 2020, companies nationwide have started work from home (WFH) due to the rapid increase of COVID-19 confirmed cases in an attempt to help prevent the coronavirus from spreading and rescue the economy from the pandemic. URLanizations have conducted surveys to understand people's opinions towards WFH. However, due to the limited sample size in surveys and the dynamic topics over time , we instead 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 square 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 attitude . These findings provide evidence that sentiment about WFH varies across user characteristics. Furthermore,  content analysis sheds light on the nuanced differences of sentiment and reveal disparities relate to WFH.
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. URLanizations 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.
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COVID-19 confirmed
confirmed COVID-19
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due to the limited sample size in surveys
the findings are limited due to small sample size
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, we instead
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square
squares
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attitude
attitudes
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of sentiment and reveal
in sentiment and reveals