ArticleInformation processing & management2022
Work from home during the COVID-19 pandemic: An observational study based on a large geo-tagged COVID-19 Twitter dataset (UsaGeoCov19).
Article in Information processing & management, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Understanding pre- and post-COVID urban neighborhood dynamics: a study analyzing georeferenced tweets for New York City.Computational urban science · 2026Article
- A bimodal longitudinal investigation on changes in sentiments over social media interactions owing to COVID-19 pandemic.Scientific reports · 2025Article
- Leveraging Large Language Models for Infectious Disease Surveillance-Using a Web Service for Monitoring COVID-19 Patterns From Self-Reporting Tweets: Content Analysis.Journal of medical Internet research · 2025Article
- Unveiling Vaccine Hesitancy on Twitter: Analyzing Trends and Reasons during the Emergence of COVID-19 Delta and Omicron Variants.Vaccines · 2023Article
- A novel TCNN-Bi-LSTM deep learning model for predicting sentiments of tweets about COVID-19 vaccines.Concurrency and computation : practice & experience · 2022Article
- The COVID-19 Infodemic on Twitter: A Space and Time Topic Analysis of the Brazilian Immunization Program and Public Trust.Tropical medicine and infectious disease · 2022Article
- Investigating the COVID-19 vaccine discussions on Twitter through a multilayer network-based approach.Information processing & management · 2022Article
- An iterative topic model filtering framework for short and noisy user-generated data: analyzing conspiracy theories on twitter.International journal of data science and analytics · 2022Article
- Exploring the role of digital media dependency on the relationship between personal involvement and flashbulb memory during the pandemic: Empirical evidence from Mainland China.Frontiers in psychology · 2022Article
- Article
- An augmented multilingual Twitter dataset for studying the COVID-19 infodemic.Social network analysis and mining · 2021Review
- A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis.PloS one · 2021Article
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2 authors.
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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.
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