Work from home during the COVID-19 pandemic: An observational study based on a large geo-tagged COVID-19 Twitter dataset (UsaGeoCov19). Issue 2 (March 2022)

• Record Type: Journal Article
• Authors: Feng, Yunhe; Zhou, Wenjun
• Is Part Of: Information processing & management. Volume 59:Issue 2(2022)
• Publication Date: 2022-03
• Subjects: Work from home -- Stay-at-home order -- Lockdown -- Reopen -- Spatiotemporal analysis -- Descriptive study; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2021.102820
• Languages: English
• ISSNs: 0306-4573

Abstract

Abstract: As COVID-19 swept over the world, people discussed facts, expressed opinions, and shared sentiments about the pandemic on social media. Since policies such as travel restriction and lockdown in reaction to COVID-19 were made at different levels of the society (e.g., schools and employers) and the government, we build a large geo-tagged Twitter dataset titled UsaGeoCov19 and perform an exploratory analysis by geographic location. Specifically, we collect 650, 563 unique geo-tagged tweets across the United States covering the date range from January 25 to May 10, 2020. Tweet locations enable 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 inspire 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. UsaGeoCov19 link: http://yunhefeng.me/geo-tagged_twitter_datasets/ . Highlights: A large-scale geo-tagged COVID-19 tweet dataset in the United States was released. An algorithm to estimate work engagement during the COVID-19 crisis is proposed. The negative emotion reached the highest level when 1000th deaths were reported. The positive emotion achieved the highest level when states began to reopen. Ten popular topics, such as Facts and Healthcare, are inferred and summarized.