ArticleBMC public health2021
A longitudinal and geospatial analysis of COVID-19 tweets during the early outbreak period in the United States.
Article in BMC public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- 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
- Tweeting environmental pollution: Analyzing twitter language to uncover its correlation with county-level obesity rates in the United States.Preventive medicine · 2024Article
- Linguistic Methodologies to Surveil the Leading Causes of Mortality: Scoping Review of Twitter for Public Health Data.Journal of medical Internet research · 2023Article
- Article
- Public Figure Vaccination Rhetoric and Vaccine Hesitancy: Retrospective Twitter Analysis.JMIR infodemiology · 2023Article
- Using geospatial social media data for infectious disease studies: a systematic review.International journal of digital earth · 2023Article
- Analyzing Tweeting Patterns and Public Engagement on Twitter During the Recognition Period of the COVID-19 Pandemic: A Study of Two U.S. States.IEEE access : practical innovations, open solutions · 2022Article
- Evidence and theory for lower rates of depression in larger US urban areas.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Temporal Variations and Spatial Disparities in Public Sentiment Toward COVID-19 and Preventive Practices in the United States: Infodemiology Study of Tweets.JMIR infodemiologyArticle
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionEarly reports of COVID-19 cases and deaths may not accurately convey community-level concern about the pandemic during early stages, particularly in the United States where testing capacity was initially limited. Social media interaction may elucidate public reaction and communication dynamics about COVID-19 in this critical period, during which communities may have formulated initial conceptions about the perceived severity of the pandemic.
methodsTweets were collected from the Twitter public API stream filtered for keywords related to COVID-19. Using a pre-existing training set, a support vector machine (SVM) classifier was used to obtain a larger set of geocoded tweets with characteristics of user self-reporting COVID-19 symptoms, concerns, and experiences. We then assessed the longitudinal relationship between identified tweets and the number of officially reported COVID-19 cases using linear and exponential regression at the U.S. county level. Changes in tweets that included geospatial clustering were also assessed for the top five most populous U.S. cities.
resultsFrom an initial dataset of 60 million tweets, we analyzed 459,937 tweets that contained COVID-19-related keywords that were also geolocated to U.S. counties. We observed an increasing number of tweets throughout the study period, although there was variation between city centers and residential areas. Tweets identified as COVID-19 symptoms or concerns appeared to be more predictive of active COVID-19 cases as temporal distance increased.
conclusionResults from this study suggest that social media communication dynamics during the early stages of a global pandemic may exhibit a number of geospatial-specific variations among different communities and that targeted pandemic communication is warranted. User engagement on COVID-19 topics may also be predictive of future confirmed case counts, though further studies to validate these findings are needed.
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