DisDSS: a novel Web-based smart disaster management system for determining the nature of a social media message for decision-making using deep learning – case study of COVID-19
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| Wydane w: | Global Knowledge, Memory and Communication vol. 73, no. 8/9 (2024), p. 1044-1065 |
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Emerald Group Publishing Limited
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| Dostęp online: | Citation/Abstract Full Text Full Text - PDF |
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| 024 | 7 | |a 10.1108/GKMC-07-2022-0180 |2 doi | |
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| 045 | 2 | |b d20241010 |b d20241129 | |
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| 100 | 1 | |a Singla, Annie |u Centre of Excellence in Disaster Mitigation and Management, IIT Roorkee, Roorkee, India | |
| 245 | 1 | |a DisDSS: a novel Web-based smart disaster management system for determining the nature of a social media message for decision-making using deep learning – case study of COVID-19 | |
| 260 | |b Emerald Group Publishing Limited |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a PurposeThis paper aims to propose DisDSS: a Web-based smart disaster management (DM) system for decision-making that will assist disaster professionals in determining the nature of disaster-related social media (SM) messages. The research classifies the tweets into need-based, availability-based, situational-based, general and irrelevant categories and visualizes them on a web interface, location-wise.Design/methodology/approachIt is worth mentioning that a fusion-based deep learning (DL) model is introduced to objectively determine the nature of an SM message. The proposed model uses the convolution neural network and bidirectional long short-term memory network layers.FindingsThe developed system leads to a better performance in accuracy, precision, recall, F-score, area under receiver operating characteristic curve and area under precision-recall curve, compared to other state-of-the-art methods in the literature. The contribution of this paper is three fold. First, it presents a new covid data set of SM messages with the label of nature of the message. Second, it offers a fusion-based DL model to classify SM data. Third, it presents a Web-based interface to visualize the structured information.Originality/valueThe architecture of DisDSS is analyzed based on the practical case study, i.e. COVID-19. The proposed DL-based model is embedded into a Web-based interface for decision support. To the best of the authors’ knowledge, this is India’s first SM-based DM system. | |
| 610 | 4 | |a Ushahidi | |
| 653 | |a Earthquakes | ||
| 653 | |a Datasets | ||
| 653 | |a Coronaviruses | ||
| 653 | |a Social networks | ||
| 653 | |a COVID-19 | ||
| 653 | |a Social media | ||
| 653 | |a Deep learning | ||
| 653 | |a Case studies | ||
| 653 | |a Literature | ||
| 653 | |a Novels | ||
| 653 | |a Recall | ||
| 653 | |a Bidirectionality | ||
| 653 | |a Decision making | ||
| 653 | |a Disaster management | ||
| 653 | |a Disasters | ||
| 653 | |a Learning | ||
| 653 | |a Short term memory | ||
| 653 | |a Neural networks | ||
| 653 | |a Mass media | ||
| 653 | |a Computerized decision support systems | ||
| 700 | 1 | |a Agrawal, Rajat |u Department of Management Studies, IIT Roorkee, Roorkee, India | |
| 773 | 0 | |t Global Knowledge, Memory and Communication |g vol. 73, no. 8/9 (2024), p. 1044-1065 | |
| 786 | 0 | |d ProQuest |t Library Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3134993712/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3134993712/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3134993712/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |