ABSTRACT
Over the past few months, COVID-19 has emerged to the world as a new threat to humanity and communities, expanding from a few small infected cities to hundreds of countries around the world impacting businesses, education, economics, and almost every activity associated with human life. This had led many researchers and scientists to analyze and study different factors and variables that obtain timely information on the outbreak of COVID-19. One of the main factors that helped in spreading the corona-virus is human mobility. Since detailed information about human movement during outbreaks are difficult to obtain, social networks comes as an alternative with its massive volume of publicly available data. In this research, we propose mobility detection and identification of social media's spatio-temporal data, as a proxy for human mobility. We aim to discover and explore an in-depth level of mobility data extracted from social media applications to uncover the relation between COVID-19 spread and daily mobility ratio in Kuwait regional area. With the use of the latest mobility data extracted from Twitter users, we have shown that user mobility is linked to the positive cases of COVID-19, with a relatively high correlation coefficient. Moreover, we have analyzed and discussed how the impact of COVID-19 affected user behavior and mobility habits.
- Munairah Al-Jeri. Towards human mobility detection scheme for location-based social network. In 2019 IEEE Symposium on Computers and Communications (ISCC), pages 1--7. IEEE, 2019.Google ScholarCross Ref
- Shweta Bansal, Gerardo Chowell, Lone Simonsen, Alessandro Vespignani, and Cécile Viboud. Big data for infectious disease surveillance and modeling. The Journal of infectious diseases, 214(suppl_4):S375--S379, 2016.Google Scholar
- Donal Bisanzio, Moritz UG Kraemer, Isaac I Bogoch, Thomas Brewer, John S Brownstein, and Richard Reithinger. Use of twitter social media activity as a proxy for human mobility to predict the spatiotemporal spread of covid-19 at global scale. Geospatial health, 15(1), 2020.Google Scholar
- Xiao Huang, Zhenlong Li, Yuqin Jiang, Xiaoming Li, and Dwayne Porter. Twitter, human mobility, and covid-19. arXiv preprint arXiv:2007.01100, 2020.Google Scholar
- Kia Jahanbin and Vahid Rahmanian. Using twitter and web news mining to predict covid-19 outbreak. Asian Pacific Journal of Tropical Medicine, 13, 2020.Google Scholar
- Shengjie Lai, Andrea Farnham, Nick W Ruktanonchai, and Andrew J Tatem. Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mhealth for travel medicine. Journal of travel medicine, 26(3):taz019, 2019.Google Scholar
- Stephen A Lauer, Kyra H Grantz, Qifang Bi, Forrest K Jones, Qulu Zheng, Hannah R Meredith, Andrew S Azman, Nicholas G Reich, and Justin Lessler. The incubation period of coronavirus disease 2019 (covid-19) from publicly reported confirmed cases: estimation and application. Annals of internal medicine, 172(9):577--582, 2020.Google Scholar
- Elizabeth C Lee, Jason M Asher, Sandra Goldlust, John D Kraemer, Andrew B Lawson, and Shweta Bansal. Mind the scales: Harnessing spatial big data for infectious disease surveillance and inference. The Journal of infectious diseases, 214(suppl_4):S409--S413, 2016.Google Scholar
- Jiawei Li, Qing Xu, Raphael Cuomo, Vidya Purushothaman, and Tim Mackey. Data mining and content analysis of the chinese social media platform weibo during the early covid-19 outbreak: retrospective observational infoveillance study. JMIR Public Health and Surveillance, 6(2):e18700, 2020.Google Scholar
- Lifang Li, Qingpeng Zhang, Xiao Wang, Jun Zhang, Tao Wang, Tian-Lu Gao, Wei Duan, Kelvin Kam-fai Tsoi, and Fei-Yue Wang. Characterizing the propagation of situational information in social media during covid-19 epidemic: A case study on weibo. IEEE Transactions on Computational Social Systems, 7(2):556--562, 2020.Google Scholar
- Sijia Li, Yilin Wang, Jia Xue, Nan Zhao, and Tingshao Zhu. The impact of covid-19 epidemic declaration on psychological consequences: a study on active weibo users. International journal of environmental research and public health, 17(6):2032, 2020.Google Scholar
- Yuan Liao, Sonia Yeh, and Gustavo S Jeuken. From individual to collective behaviours: exploring population heterogeneity of human mobility based on social media data. EPJ Data Science, 8(1):34, 2019.Google ScholarCross Ref
- Kuwait ministry of health. Kw corona-virus updates. https://corona.e.gov.kw/, august 2020.Google Scholar
- World Health organization. corona-virus updates. https://www.who.int/health-topics/coronavirus, 2020.Google Scholar
- Dr Prabhakar Kaila, Dr AV Prasad, et al. Informational flow on twitter--corona virus outbreak--topic modelling approach. International Journal of Advanced Research in Engineering and Technology (IJARET), 11(3), 2020.Google Scholar
- Leonard Schild, Chen Ling, Jeremy Blackburn, Gianluca Stringhini, Yang Zhang, and Savvas Zannettou. " go eat a bat, chang!": An early look on the emergence of sinophobic behavior on web communities in the face of covid-19. arXiv preprint arXiv:2004.04046, 2020.Google Scholar
- Spyridon Spyratos, Michele Vespe, Fabrizio Natale, Ingmar Weber, Emilio Zagheni, and Marzia Rango. Quantifying international human mobility patterns using facebook network data. PloS one, 14(10):e0224134, 2019.Google ScholarCross Ref
- Paiheng Xu, Mark Dredze, and David A Broniatowski. The twitter social mobility index: Measuring social distancing practices from geolocated tweets. arXiv preprint arXiv:2004.02397, 2020.Google Scholar
- Takahiro Yabe, Yoshihide Sekimoto, Kota Tsubouchi, and Satoshi Ikemoto. Cross-comparative analysis of evacuation behavior after earthquakes using mobile phone data. PLoS one, 14(2):e0211375, 2019.Google Scholar
- Yuxin Zhao and Huilan Xu. Chinese public attention to covid-19 epidemic: Based on social media. medRxiv, 2020.Google Scholar
- Zhiyuan Zhao, Shih-Lung Shaw, Ling Yin, Zhixiang Fang, Xiping Yang, Fan Zhang, and Sheng Wu. The effect of temporal sampling intervals on typical human mobility indicators obtained from mobile phone location data. International Journal of Geographical Information Science, 33(7):1471--1495, 2019.Google Scholar
Index Terms
- The Impact of COVID-19 Confinement on Regional Mobility of Spatial-Temporal Social Networks
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