ArticlePloS one2020
Twitter reveals human mobility dynamics during the COVID-19 pandemic.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 2 of them syntheses that pooled it.
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Who cites it
71 citing papers in PubMed, 2 syntheses or guidelines pooled it, 242 citations in OpenAlex.
- A systematic review of using population-level human mobility data to understand SARS-CoV-2 transmission.Nature communications · 2024Pooled it
- Social Media Use for Health Purposes: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Outfitting the quest for spatial spread of infections: A review of mobility datasets for population health modelling.Infectious Disease Modelling · 2026Review
- Using Social Media Listening to Understand the Pressure Injury Experience: A Qualitative Descriptive Study.JMIR nursing · 2026Article
- Measuring human capital with social media data and machine learning.Scientific reports · 2026Article
- Sleepless in America: A social sensing study of pandemic-era sleeplessness using nighttime social media data.PloS one · 2026Article
- Understanding pre- and post-COVID urban neighborhood dynamics: a study analyzing georeferenced tweets for New York City.Computational urban science · 2026Article
- Characterizing Experiences With Hikikomori Syndrome on Twitter Among Japanese-Language Users: Qualitative Infodemiology Content Analysis.JMIR infodemiology · 2025Article
- Variant patterns and influence of inter-regional travel during the SARS-CoV-2 expansion in South Africa.PloS one · 2025Article
- Urban birds' tolerance towards humans was largely unaffected by COVID-19 shutdown-induced variation in human presence.Communications biology · 2024Article
- Twitter Analysis of Health Care Workers' Sentiment and Discourse Regarding Post-COVID-19 Condition in Children and Young People: Mixed Methods Study.Journal of medical Internet research · 2024Article
- Twitter social mobility data reveal demographic variations in social distancing practices during the COVID-19 pandemic.Scientific reports · 2024Article
- Public Health Policy, Political Ideology, and Public Emotion Related to COVID-19 in the U.S.International journal of environmental research and public health · 2023Article
- Impacts on Human Movement in Australian Cities Related to the COVID-19 Pandemic.Tropical medicine and infectious disease · 2023Article
- Reddit and Google Activity Related to Non-COVID Epidemic Diseases Surged at Start of COVID-19 Pandemic: Retrospective Study.JMIR formative research · 2023Article
- Gauging urban resilience in the United States during the COVID-19 pandemic via social network analysis.Cities (London, England) · 2023Article
- Three dimensions of COVID-19 risk perceptions and their socioeconomic correlates in the United States: A social media analysis.Risk analysis : an official publication of the Society for Risk Analysis · 2023Article
- Twitterati on COVID-19 pandemic-environment linkage: Insights from mining one year of tweets.Environmental development · 2023Article
- Segregation and the pandemic: The dynamics of daytime social diversity during COVID-19 in Greater Stockholm.Applied geography (Sevenoaks, England) · 2023Article
- Post COVID-19 pandemic recovery of intracity human mobility in Wuhan: Spatiotemporal characteristic and driving mechanism.Travel behaviour & society · 2023Article
11 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
The current COVID-19 pandemic raises concerns worldwide, leading to serious health, economic, and social challenges. The rapid spread of the virus at a global scale highlights the need for a more harmonized, less privacy-concerning, easily accessible approach to monitoring the human mobility that has proven to be associated with viral transmission. In this study, we analyzed over 580 million tweets worldwide to see how global collaborative efforts in reducing human mobility are reflected from the user-generated information at the global, country, and U.S. state scale. Considering the multifaceted nature of mobility, we propose two types of distance: the single-day distance and the cross-day distance. To quantify the responsiveness in certain geographic regions, we further propose a mobility-based responsive index (MRI) that captures the overall degree of mobility changes within a time window. The results suggest that mobility patterns obtained from Twitter data are amenable to quantitatively reflect the mobility dynamics. Globally, the proposed two distances had greatly deviated from their baselines after March 11, 2020, when WHO declared COVID-19 as a pandemic. The considerably less periodicity after the declaration suggests that the protection measures have obviously affected people's travel routines. The country scale comparisons reveal the discrepancies in responsiveness, evidenced by the contrasting mobility patterns in different epidemic phases. We find that the triggers of mobility changes correspond well with the national announcements of mitigation measures, proving that Twitter-based mobility implies the effectiveness of those measures. In the U.S., the influence of the COVID-19 pandemic on mobility is distinct. However, the impacts vary substantially among states.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.