Citation
Abstract
Analysing social media data is crucial for crisis management organisations to make timely decisions. Researchers in crisis informatics have devised various methods and systems to process and classify large volumes of crisis-related social media data for effective crisis response and recovery. However, the complexity of previous solutions hampers the timely processing of this data, its visualisation, and its interpretation, which is necessary for effective crisis response. Hence, this study addresses this challenge by employing visualisation of similarities to analyse and visualise crisis datasets to aid crisis management and decision-making. The results reveal a "nine-cluster community” of relevant keywords comprising “Green, Brown, Red, Blue, Pink, Purple, Yellow, Orange, and Cyan” colours, in both binary and full count. Specifically, the findings reveal various keywords such as the needs for food, water, shelter, medicine, and electricity. Thereafter, the study discusses the implications of VOSviewer for analysing crisis data theoretically and practically.
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Official URL or Download Paper: https://linkinghub.elsevier.com/retrieve/pii/S2667...
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Additional Metadata
| Item Type: | Article |
|---|---|
| Divisions: | Institute for Mathematical Research Universiti Putra Malaysia |
| DOI Number: | https://doi.org/10.1016/j.jjimei.2024.100314 |
| Publisher: | Elsevier B.V. |
| Keywords: | Co-occurrence networks; Data analysis; Decision-making;Disaster management; Social media; Visualisation; VOSviewer |
| Depositing User: | Mohamad Jefri Mohamed Fauzi |
| Date Deposited: | 13 Nov 2025 07:01 |
| Last Modified: | 13 Nov 2025 07:01 |
| Altmetrics: | http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1016/j.jjimei.2024.100314 |
| URI: | http://psasir.upm.edu.my/id/eprint/121679 |
| Statistic Details: | View Download Statistic |
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