Twitter reveals human mobility dynamics during the COVID-19 pandemic
Open accessAbstract
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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Li, Z.-L., Huang, X., Ye, X.-Y., Jiang, Y.-Q., Martín, Y., Ning, H., Hodgson, M., & Li, X.-M. (2021) · Scientific Reports · doi:10.1038/s41598-021-94300-7 · Cited by 49 · see citing works
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“[50] analyzed Twitter data for changes in mobility using metrics of longest distance per day and across days, inspired by Warren and Skillman [5] mobile phone data study and developing the metrics further to a normalized mobility index and showing the significant spatial effects.”
MethodRao, J., Gao, S., & Zhu, S. (2023) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2023.2262550 · Cited by 51 · see citing works
“…planning (Caceres et al., 2012; Liu and Cheng, 2020), understanding the COVID-19 pandemic impacts on human mobility changes (Chow et al., 2021; Huang et al., 2020; Long and Ren, 2022; Noi et al., 2022), spatiotemporal analysis of individual air pollution exposure 4) Differential Privacy (DP)…”
BackgroundImproving prediction of COVID-19 evolution by fusing epidemiological and mobility data
García-Cremades, S., Morales-García, J., Hernández-Sanjaime, R., Martínez‐España, R., Bueno-Crespo, A., Hernández‐Orallo, E., López-Espín, J. J., & Cecilia, J. M. (2021) · Scientific Reports · doi:10.1038/s41598-021-94696-2 · Cited by 48 · see citing works
“Huang et al. 54 showed that mobility patterns obtained from Twitter can quantitatively reflect the mobility dynamics.”
BackgroundSocial Media Analysis to Enhance Sustainable Knowledge Management: A Concise Literature Review
Alghamdi, A. M., Pileggi, S. F., & Sohaib, O. (2023) · Sustainability · doi:10.3390/su15139957 · Cited by 59 · see citing works
“Finally, social media platforms enable the utilization of geographic and demographic data, which can inform medical practices and research [130].”
BackgroundYang, D., Yang, A., Yang, J., Xu, R., & Qiu, H. (2021) · GeoHealth · doi:10.1029/2021gh000395 · Cited by 44 · see citing works
“Human mobility, physical activities, and travels were limited given the concerns of COVID-19 infection, social distancing COVID-19 control strategy, and unhealthy outdoor air quality (Archer et al., 2020; Huang et al., 2020).”
BackgroundKar, A., Le, H., & Miller, H. J. (2021) · Annals of the American Association of Geographers · doi:10.1080/24694452.2021.1956876 · Cited by 49 · see citing works
The emergence of urban heat traps and human mobility in 20 US cities
Huang, X., Jiang, Y., & Mostafavi, A. (2024) · npj Urban Sustainability · doi:10.1038/s42949-024-00142-3 · Cited by 44 · see citing works
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“The methodology of extracting daily population movement (origin-destination flows) from geotagged tweets is discussed elsewhere [26,27].”
Self-citationAn absorptive capacity-based systems view of Covid-19 in the small business economy
Caiazza, R., Phan, P., Lehmann, E. E., & Etzkowitz, H. (2021) · International Entrepreneurship and Management Journal · doi:10.1007/s11365-021-00753-7 · Cited by 47 · see citing works
ODT FLOW: Extracting, analyzing, and sharing multi-source multi-scale human mobility
Li, Z., Huang, X., Hu, T., Ning, H., Ye, X., Huang, B., & Li, X. (2021) · PLoS ONE · doi:10.1371/journal.pone.0255259 · Cited by 40 · see citing works
“In this study, instead of computing the movement distance as [30], we are interested in each user’s origin location and destination location on a daily basis to construct the entity-level 4D cube, denoted as (user, origin, destination, day).”
BackgroundInfluentialSelf-citationEhsani, J. P., Michael, J. P., Duren, M. L., Mui, Y., & Porter, K. M. P. (2021) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2021.01.011 · Cited by 39 · see citing works
Hu, X., Zhou, Z., Sun, Y., Kersten, J., Klan, F., Fan, H., & Wiegmann, M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3150967 · Cited by 40 · see citing works
“After its happening, geotagged tweets were utilized to analyze the mental health status of the public [10], [11], to track and visualize the spread and diffusion of COVID-19 [12], and to reveal human mobility dynamics during the pandemic [13], [14].”
MethodSu, Y., Venkat, A., Yadav, Y., Puglisi, L. B., & Fodeh, S. J. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104336 · Cited by 38 · see citing works
“Twitter has been used to assess mitigation strategies such as social distancing [8,9] and estimate mobility dynamics within and across states [10].”
MethodLi, X., Huang, X., Li, D., & Xu, Y. (2022) · Sustainable Cities and Society · doi:10.1016/j.scs.2022.103869 · Cited by 37 · see citing works
“Paired sample t -test, also called the dependent samples t -test, is one of the most used statistical methods to examine whether the mean difference between two datasets is significant, which yields a p -value indicating the significance level of difference (Li et al., 2020; Mishra et al., 2019).”
Self-citationUsing geospatial social media data for infectious disease studies: a systematic review
Jing, F., Li, Z., Qiao, S., Zhang, J., Olatosi, B., & Li, X. (2023) · International Journal of Digital Earth · doi:10.1080/17538947.2022.2161652 · Cited by 37 · see citing works
“…diseases, including the transmission of COVID-19 (Kraemer et al. 2018; Chang et al. 2021; Cowley et al. 2021; Shepherd et al. 2021) and the e ff ects of governmental lock-down in response to the pandemic (Huang et al. 2020; Gibbs et al. 2021; Gulnerman 2021; Iranma-nesh and Alpar Atun 2021).”
BackgroundSelf-citationDeterminants of Small Business Reopening Decisions After COVID Restrictions Were LiftedPreprint
Balla‐Elliott, D., Cullen, Z. N., Glaeser, E. L., Luca, M., & Stanton, C. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3634162 · Cited by 37 · see citing works
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Analysis of Geotagging Behavior: Do Geotagged Users Represent the Twitter Population?
Karami, A., Kadari, R. R., Panati, L., Nooli, S. P., Bheemreddy, H., & Bozorgi, P. (2021) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi10060373 · Cited by 35 · see citing works
“…data, such as exploring the diversity of human mobility patterns among individuals and within/between cities [14] and during the COVID-19 pandemic [15,16]; identifying spatiotemporal patterns of tweets during floods and hurricanes [17,18]; analyzing tweets containing discussions on climate change…”
BackgroundYang, Y., Zhang, L., Wu, L. L., & Li, Z. (2022) · Journal of Travel Research · doi:10.1177/00472875221077978 · Cited by 35 · see citing works
“…in applied econometrics have embraced various types of big data, such as air passenger searches and selection (Gallego and Font 2021), foot traffic data (Yang, Liu, and Chen 2020), and geotagged social media data (Huang et al. 2020), to estimate COVID-19’s economic impact on the tourism industry.”
BackgroundSelf-citationJiang, N., Crooks, A. T., Kavak, H., Burger, A., & Kennedy, W. G. (2022) · Computational Urban Science · doi:10.1007/s43762-022-00034-1 · Cited by 35 · see citing works
“Coinciding with this growth is proliferation of data from various sources (e.g., cell phones, social media, GPS logs) which are being used in geographical urban simulations in order to capture reality better (e.g., Ye et al., 2021; Huang et al., 2020; Manley et al., 2015).”
BackgroundMethodTwo-Step Cluster Analysis of Passenger Mobility Segmentation during the COVID-19 Pandemic
Harantová, V., Mazanec, J., Štefancová, V., Mašek, J., & Foltýnová, H. B. (2023) · Mathematics · doi:10.3390/math11030583 · Cited by 35 · see citing works
Qiao, S., Li, Z., Chen, L., Li, X., & Rudisill, C. (2022) · Risk Analysis · doi:10.1111/risa.13993 · Cited by 34 · see citing works
“It has become a critical source for understanding information exchange and the public’s opinions, experiences, and feelings about the COVID-19 pandemic (Hussain et al., 2021; Kurten & Beullens, 2021).”
Self-citationWang, J., Kaza, N., McDonald, N., & Khanal, K. (2022) · Transport Policy · doi:10.1016/j.tranpol.2022.05.012 · Cited by 34 · see citing works
“People with low income and low educational attainment tended to show less reduction in mobility because of their inability to work remotely (Dasgupta et al., 2020; Pepe et al., 2020; McLaren, 2021; Brough et al., 2020; Huang et al., 2020).”
InfluentialYao, Y., Guo, Z., Huang, X., Ren, S., Hu, Y., Dong, A., & Guan, Q. (2023) · Cities · doi:10.1016/j.cities.2023.104361 · Cited by 32 · see citing works
Self-citationJärv, O., Tominga, A., Müürisepp, K., & Silm, S. (2021) · Journal of Location Based Services · doi:10.1080/17489725.2021.1887526 · Cited by 32 · see citing works
“Third, data sources can cover whole countries (Santamaria et al. 2020), or even have global coverage (Google 2020; Huang et al. 2020).”
BackgroundInfluentialRauchfleisch, A., Vogler, D., & Eisenegger, M. (2021) · Javnost - The Public · doi:10.1080/13183222.2021.1923622 · Cited by 31 · see citing works
“This included medical and epidemiological research, which uses Twitter data to model the spread of the pandemic (e.g. Huang et al. 2020) but also 4 A. RAUCHFLEISCH ET AL. studies by communication scholars (Park, Park, and Chong 2020), often focusing on the spread of misinformation (Gallotti et al.…”
BackgroundTopoBERT: a plug and play toponym recognition module harnessing fine-tuned BERT
Zhou, B., Zou, L., Hu, Y., Qiang, Y., & Goldberg, D. W. (2023) · International Journal of Digital Earth · doi:10.1080/17538947.2023.2239794 · Cited by 23 · see citing works
“Huang et al. scrutinized the human mobility patterns during the COVID-19 pandemic at multiple scales based on geotagged Twitter data (Huang et al. 2020).”
BackgroundZhou, J., Wu, J., & Ma, H. (2021) · Applied Geography · doi:10.1016/j.apgeog.2021.102504 · Cited by 29 · see citing works
Chen, M., Liu, Y., Ye, Z., Wang, S., & Zhang, W. (2024) · Sustainable Cities and Society · doi:10.1016/j.scs.2024.105823 · Cited by 28 · see citing works
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Self-citationHow to evaluate urban emotions using twitter social media?
Dorostkar, E., & Najarsadeghi, M. (2022) · Cities · doi:10.1016/j.cities.2022.103713 · Cited by 27 · see citing works
Müürisepp, K., Järv, O., Sjöblom, F., Toger, M., & Östh, J. (2023) · Applied Geography · doi:10.1016/j.apgeog.2023.102926 · Cited by 27 · see citing works
“A vast body of research has shown how the COVID-19 pandemic and its mitigation strategies resulted in an unprecedented decline in people’s mobility internationally, regionally and within cities (Huang et al., 2020; Järv et al., 2021; Toger et al., 2021; Willberg et al., 2021).”
Unmasking People’s Opinions behind Mask-Wearing during COVID-19 Pandemic—A Twitter Stance Analysis
Cotfas, L., Delcea, C., Gherai, R., & Roxin, I. (2021) · Symmetry · doi:10.3390/sym13111995 · Cited by 25 · see citing works
Wang, R., Wang, Y., & Li, N. (2023) · Cities · doi:10.1016/j.cities.2023.104636 · Cited by 25 · see citing works
Mikula, P., Jokimäki, J., Kaisanlahti‐Jokimäki, M., Markó, G., Morelli, F., Møller, A. P., Szakony, S., Yosef, R., Albrecht, T., & Tryjanowski, P. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.148672 · Cited by 25 · see citing works
Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility
Rosa, L., Silva, F., & Analide, C. (2021) · Smart Cities · doi:10.3390/smartcities4020046 · Cited by 24 · see citing works
“Findings reveal that the intent to adopt internet banking services could be predicted by variables directly linked to the existence of capable communications networks.”
BackgroundSocio-spatial inequality and the effects of density on COVID-19 transmission in US cities
Kontokosta, C. E., Hong, B., & Bonczak, B. J. (2024) · Nature Cities · doi:10.1038/s44284-023-00008-2 · Cited by 24 · see citing works
Viviani, M., Crocamo, C., Mazzola, M., Bartoli, F., Carrà, G., & Pasi, G. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.06.044 · Cited by 23 · see citing works
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Self-citationManout, O., & Ciari, F. (2021) · Frontiers in Built Environment · doi:10.3389/fbuil.2021.654279 · Cited by 21 · see citing works
Wang, Y., Zhong, C., Gao, Q., & Cabrera-Arnau, C. (2022) · Urban Informatics · doi:10.1007/s44212-022-00018-w · Cited by 22 · see citing works
“…official statistics (Fielding & Ishikawa, 2021; ONS, 2021b), multiple emerging data sources have been explored for migration analysis including public transport data, mobile phone data (Kang et al., 2020; Pullano et al., 2020) and social media data (Huang et al., 2020; Terroso-Saenz et al., 2022).”
BackgroundWang, S., Huang, X., Hu, T., Zhang, M., Li, Z., Ning, H., Corcoran, J., Khan, A., Liu, Y., Zhang, J., et al. (2022) · BMJ Global Health · doi:10.1136/bmjgh-2021-007081 · Cited by 21 · see citing works
Self-citationWankhade, M., & Rao, A. C. S. (2022) · Scientific Reports · doi:10.1038/s41598-022-21604-7 · Cited by 21 · see citing works
Using human mobility data to detect evacuation patterns in hurricane Ian
Xiang, L., Qiang, Y., & Cervone, G. (2024) · Annals of GIS · doi:10.1080/19475683.2024.2341703 · Cited by 21 · see citing works
Wang, S., Tong, Y., Fan, Y., Liu, H., Wu, J., Wang, Z., & Fang, C. (2021) · Scientific Reports · doi:10.1038/s41598-021-94060-4 · Cited by 20 · see citing works
Wu, C., Zhu, S., Yang, J., Hu, M., Du, B., Zhang, L., Zhang, L., Han, C., & Lan, M. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3078611 · Cited by 19 · see citing works
“Some researches used mobile locations or social media data to study human mobility [23]–[26].”
BackgroundGlobal Fisheries Responses to Culture, Policy and COVID-19 from 2017 to 2020
He, B., Yan, F., Yu, H., Su, F., Lyne, V. D., Cui, Y., Kang, L., & Wu, W. (2021) · Remote Sensing · doi:10.3390/rs13224507 · Cited by 19 · see citing works
Song, Y. H., Lee, S., Park, A. H., & Lee, C. (2022) · Environment and Planning B Urban Analytics and City Science · doi:10.1177/23998083221124930 · Cited by 19 · see citing works
“The spread of coronavirus disease 2019 (COVID-19) has disrupted people’s daily travel and social activities, as reported by many recent studies (Beck and Hensher, 2020; Saha et al., 2020; Bourdas and Zacharakis, 2020; Huang et al., 2020).”
BackgroundZhou, S., Zhou, S., Jing, F., Qi, L., & Li, J. (2024) · Journal of Transport & Health · doi:10.1016/j.jth.2024.101788 · Cited by 19 · see citing works
Transmission in home environment associated with the second wave of COVID-19 pandemic in India
Tang, L., Liu, M., Ren, B., Chen, J., Liu, X., Wu, X., Huang, W., & Tian, J. (2021) · Environmental Research · doi:10.1016/j.envres.2021.111910 · Cited by 19 · see citing works
DETERMINANTS OF SMALL BUSINESS REOPENING DECISIONS AFTER COVID RESTRICTIONS WERE LIFTED
Balla‐Elliott, D., Cullen, Z. N., Glaeser, E. L., Luca, M., & Stanton, C. (2022) · Journal of Policy Analysis and Management · doi:10.1002/pam.22355 · Cited by 17 · see citing works
“The literature on the 2020 pandemic seems to confirm that consumer demand inmany industries fell dramatically because of COVID-19, and many firms closed (Abu-Rayash & Dincer, 2020; Alexander & Karger, 2020; Baker et al., 2020; Brinkman & Mangum, 2020; Chetty et al., 2020; Cintia et al., 2020; Coibion, Gorodnichenko, &…”
New avenues for second home tourism research using big data: prospects and challenges
Raun, J., Järv, O., Okkonen, P., Rantanen, M., Hyyryläinen, T., Ryynänen, T., & Toivonen, T. (2022) · Current Issues in Tourism · doi:10.1080/13683500.2022.2138282 · Cited by 17 · see citing works
“Social media data have been used widely in mobility studies (Huang et al., 2020; Jurdak et al., 2015) and urban studies (Boy & Uitermark, 2016; Heikinheimo et al., 2020).”
MethodTian, S., Li, W., Zhong, Z., Wang, F., & Xiao, Q. (2023) · Animal Genetics · doi:10.1111/age.13293 · Cited by 17 · see citing works
Twitter mobility dynamics during the COVID-19 pandemic: A case study of London
Zhong, C., Morphet, R., & Yoshida, M. (2023) · PLoS ONE · doi:10.1371/journal.pone.0284902 · Cited by 17 · see citing works
“Twitter-based mobility data has been proposed in various forms to monitor human mobility dynamics during the COVID-19 pandemic [9, 33], with case studies predominantly in the US [32, 34] or globally at a regional scale [8].”
BackgroundDeterminants of Small Business Reopening Decisions After COVID Restrictions Were Lifted
Balla‐Elliott, D., Cullen, Z. N., Glaeser, E. L., Luca, M., & Stanton, C. (2020) · National Bureau of Economic Research · doi:10.3386/w27362 · Cited by 17 · see citing works
Rajput, A. A., Li, Q., Gao, X., & Mostafavi, A. (2022) · Frontiers in Built Environment · doi:10.3389/fbuil.2021.654409 · Cited by 17 · see citing works
Chen, T., BOWERS, K. J., Zhu, D., Gao, X., & Cheng, T. (2022) · Computational Urban Science · doi:10.1007/s43762-022-00041-2 · Cited by 17 · see citing works
“Specifically, it is evident that the outbreak and spread of the COVID-19 pandemic has influenced human activities and population dynamics in global cities in an unprecedented way (Bonaccorsi et al. 2020; Kissler et al. 2020; Huang et al. 2020).”
BackgroundGarcia, C., Rabadi, G., Abujaber, D., & Seck, M. (2023) · Journal of Homeland Security and Emergency Management · doi:10.1515/jhsem-2021-0042 · Cited by 19 · see citing works
Mikula, P., Bulla, M., Blumstein, D. T., Benedetti, Y., Floigl, K., Jokimäki, J., Kaisanlahti‐Jokimäki, M., Markó, G., Morelli, F., Møller, A. P., et al. (2024) · Communications Biology · doi:10.1038/s42003-024-06387-z · Cited by 15 · see citing works
Mobilitas Manusia dan Tingkat Penyebaran Covid-19: Sebuah Analisis Kuantitatif
Nugroho, L. E., & Rakhman, A. Z. (2021) · Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI) · doi:10.22146/jnteti.v10i2.1519 · Cited by 15 · see citing works
Huang, X., Xu, Y., Liu, R., Wang, S., Wang, S., Zhang, M., Kang, Y., Zhang, Z., Gao, S., Zhao, B., et al. (2022) · Transactions in GIS · doi:10.1111/tgis.12918 · Cited by 13 · see citing works
“…(Cartenì, Di Francesco, & Martino, 2020; Gao, Rao, Kang, Liang, & Kruse, 2020; Huang, Li, Jiang, Li, & Porter, 2020; Huang, Li, Lu, et al., 2020; Li et al., 2020), and many pieces of evidence have proved that geographical differences in transmission and death rates are linked to how strictly…”
BackgroundSelf-citationSpatial social network research: a bibliometric analysis
Wu, L., Peng, Q., Lemke, M., Hu, T., & Gong, X. (2022) · Computational Urban Science · doi:10.1007/s43762-022-00045-y · Cited by 15 · see citing works
Multiwave COVID-19 Prediction from Social Awareness Using Web Search and Mobility Data
Xue, J., Yabe, T., Tsubouchi, K., Ma, J., & Ukkusuri, S. V. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539172 · Cited by 15 · see citing works
“One critical input for these models is the mobility data [6, 19, 22], which describes population movements and is positively related to the disease infections [49].”
BackgroundHu, S., Luo, W., Darzi, A., Pan, Y., Zhao, G., Liu, Y., & Xiong, C. (2021) · PLoS ONE · doi:10.1371/journal.pone.0259803 · Cited by 14 · see citing works
Detecting space–time patterns of disease risk under dynamic background population
Hohl, A., Tang, W., Casas, I., Shi, X., & Delmelle, E. M. (2022) · Journal of Geographical Systems · doi:10.1007/s10109-022-00377-7 · Cited by 14 · see citing works
“…data are becoming available at 1 3 Detecting space–time patterns of disease risk under dynamic… finer spatial and temporal resolutions, and given current technological advances, it is foreseeable that this development will continue (Bhaduri et al. 2007; Huang et al. 2020; Kang et al. 2020).”
BackgroundA comparative analysis of epidemiological characteristics of MERS-CoV and SARS-CoV-2 in Saudi Arabia
Althobaity, Y. M., Wu, J., & Tildesley, M. J. (2022) · Infectious Disease Modelling · doi:10.1016/j.idm.2022.07.002 · Cited by 14 · see citing works
“In addition, low-risk contacts were put under active surveillance and contacted daily to monitor their health status (Huang et al., 2020).”
The disturbance of urban mobility in the context of COVID-19 pandemic
You, G. (2022) · Cities · doi:10.1016/j.cities.2022.103821 · Cited by 13 · see citing works
“However, despite a growing number of relevant studies, the interpretation of the pandemic effect diverges; some studies identify a negative relationship between the number of COVID-19 cases and urban mobility (Balbontin et al., 2021; Borkowski et al., 2021; Chan et al., 2020; Huang et al., 2020; Long & Ren, 2022; Shak…”
Modelling the propagation of infectious disease via transportation networks
Anupriya, Bansal, P., & Graham, D. J. (2022) · Scientific Reports · doi:10.1038/s41598-022-24866-3 · Cited by 13 · see citing works
Kostandova, N., Schluth, C. G., Arambepola, R., Atuhaire, F., Bérubé, S., Chin, T., Cleary, E., Cortes-Azuero, O., García‐Carreras, B., Grantz, K. H., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54895-7 · Cited by 13 · see citing works
Tantoush, M., Sengupta, U., & Han, L. (2025) · Cities · doi:10.1016/j.cities.2025.105894 · Cited by 12 · see citing works
An evaluation of geo-located Twitter data for measuring human migration
Yin, J., Gao, Y., & Chi, G. (2022) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2022.2075878 · Cited by 12 · see citing works
“For example, geo-located Twitter data have been used to explore human mobility dynamics during the COVID-19 pandemic (Huang et al. 2020) and estimate refugee migration patterns following natural disasters (H€ubl et al. 2017).”
Human mobility forecasting with region-based flows and geotagged Twitter data
Terroso-Sáenz, F., Flores, R., & Muñoz, A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117477 · Cited by 12 · see citing works
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Analyzing changes in travel patterns due to Covid-19 using Twitter data in India
Shende, S., Bhaduri, E., & Goswami, A. K. (2023) · Case Studies on Transport Policy · doi:10.1016/j.cstp.2023.100992 · Cited by 12 · see citing works
- Authors
- Huang, X., Li, Z., Jiang, Y., Li, X., & Porter, D.
- Affiliations
- University of South Carolina (US)
- Corresponding author
- Li, Zhenlong
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2020-11-10
- Volume
- 15
- Issue
- 11
- Pages
- e0241957
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC7654838
- OpenAlex
- W3105667161
- Cited by
- 242
- References
- 56
- Citations by year
- 2026: 12 · 2025: 21 · 2024: 22 · 2023: 49 · 2022: 72 · 2021: 61 · 2020: 5
- FWCI
- 41.68
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundatio (2028791)
- University of South Carolina Office of the Vice President for Research (135400-20-54176)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Keywords
- COVID-19 pandemic, human mobility, Twitter, social media data, travel behavior, mitigation measures, epidemic phases
- MeSH terms
- Betacoronavirus, COVID-19, SARS-CoV-2, Humans, Movement, Pneumonia, Viral, Coronavirus Infections, Pandemics, Social Media
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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