Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/160694
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dc.contributor.authorChew, Alvin Wei Zeen_US
dc.contributor.authorPan, Yueen_US
dc.contributor.authorWang, Yingen_US
dc.contributor.authorZhang, Limaoen_US
dc.date.accessioned2022-08-01T03:43:13Z-
dc.date.available2022-08-01T03:43:13Z-
dc.date.issued2021-
dc.identifier.citationChew, A. W. Z., Pan, Y., Wang, Y. & Zhang, L. (2021). Hybrid deep learning of social media big data for predicting the evolution of COVID-19 transmission. Knowledge-Based Systems, 233, 107417-. https://dx.doi.org/10.1016/j.knosys.2021.107417en_US
dc.identifier.issn0950-7051en_US
dc.identifier.urihttps://hdl.handle.net/10356/160694-
dc.description.abstractIn this study, a hybrid deep-learning model termed as ODANN, built upon neural networks (NN) coupled with data assimilation and natural language processing (NLP) features extraction methods, has been constructed to concurrently process daily COVID-19 time-series records and large volumes of COVID-19 related Twitter data, as representative of the global community's aggregated emotional responses towards the current pandemic, to model the growth rate in the number of confirmed COVID-19 cases globally via a proposed G parameter. Overall, there were 3 key components to ODANN's development phase, namely: (i) data hydration and pre-processing were performed on COVID-19 related Twitter data ranging between 23 January 2020 and 10 May 2020, which amounted to over 100 million Tweets written in English language; (ii) multiple NLP features extraction methods were subsequently leveraged to encode the hydrated Twitter data into useful semantic word vectors for training ODANN under an optimal set of hyperparameters; and (iii) historical time-series data of defined characteristics were also assimilated into ODANN's selected hidden layer(s) to model the G parameter daily with a lead-time of 1 day. By far, our experimental results demonstrated that by adopting a rolling time-window size of 5 days, with respect to the number of historical time-series records for assimilating different data features, enabled ODANN to outperform other traditional time-series models and recent studies, in terms of the computed RMSE and MAE scores attained from the model's testing step. Overall, the summarized results from ODANN demonstrated its competitive edge in modelling and forecasting the growth rate in the number of COVID-19 cases globally.en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.language.isoenen_US
dc.relation04INS000423C120en_US
dc.relation.ispartofKnowledge-Based Systemsen_US
dc.rights© 2021 Elsevier B.V. All rights reserved.en_US
dc.subjectEngineering::Civil engineeringen_US
dc.titleHybrid deep learning of social media big data for predicting the evolution of COVID-19 transmissionen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Civil and Environmental Engineeringen_US
dc.identifier.doi10.1016/j.knosys.2021.107417-
dc.identifier.pmid34690447-
dc.identifier.scopus2-s2.0-85116318005-
dc.identifier.volume233en_US
dc.identifier.spage107417en_US
dc.subject.keywordsNatural Language Processingen_US
dc.subject.keywordsTime-Series Predictionen_US
dc.description.acknowledgementThis study was supported in part by Microsoft Corporation for the AI for Health COVID-19 Azure Compute Grant of ID:00011000272 and the Start-Up Grant at Nanyang Technological University, Singapore (No. 04INS000423C120).en_US
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item.grantfulltextnone-
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