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A Multimodal Data Analysis Approach to Social Media during Natural Disasters

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  • Mengna Zhang

    (School of Management, Guizhou University, Guiyang 550025, China
    Academic Affairs Office, Guizhou University of Finance and Economics, Guiyang 550025, China)

  • Qisong Huang

    (Guizhou Minzu University, Guiyang 550025, China)

  • Hua Liu

    (Faculty of Law, Guizhou University, Guiyang 550025, China)

Abstract

During natural disasters, social media can provide real time or rapid disaster, perception information to help government managers carry out disaster response efforts efficiently. Therefore, it is of great significance to mine social media information accurately. In contrast to previous studies, this study proposes a multimodal data classification model for mining social media information. Using the model, the study employs Late Dirichlet Allocation (LDA) to identify subject information from multimodal data, then, the multimodal data is analyzed by bidirectional encoder representation from transformers (Bert) and visual geometry group 16 (Vgg-16). Text and image data are classified separately, resulting in real mining of topic information during disasters. This study uses Weibo data during the 2021 Henan heavy storm as the research object. Comparing the data with previous experiment results, this study proposes a model that can classify natural disaster topics more accurately. The accuracy of this study is 0.93. Compared with a topic-based event classification model KGE-MMSLDA, the accuracy of this study is improved by 12%. This study results in a real-time understanding of different themed natural disasters to help make informed decisions.

Suggested Citation

  • Mengna Zhang & Qisong Huang & Hua Liu, 2022. "A Multimodal Data Analysis Approach to Social Media during Natural Disasters," Sustainability, MDPI, vol. 14(9), pages 1-21, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5536-:d:808754
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    References listed on IDEAS

    as
    1. Pablo Aznar-Crespo & Antonio Aledo & Joaquín Melgarejo-Moreno & Arturo Vallejos-Romero, 2021. "Adapting Social Impact Assessment to Flood Risk Management," Sustainability, MDPI, vol. 13(6), pages 1-27, March.
    2. E. Piatyszek & G. Karagiannis, 2012. "A model-based approach for a systematic risk analysis of local flood emergency operation plans: a first step toward a decision support system," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 61(3), pages 1443-1462, April.
    3. Ragini, J. Rexiline & Anand, P.M. Rubesh & Bhaskar, Vidhyacharan, 2018. "Big data analytics for disaster response and recovery through sentiment analysis," International Journal of Information Management, Elsevier, vol. 42(C), pages 13-24.
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    Keywords

    multimodal data; LDA; Bert; VGG-16;
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