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Analysis of public emotion on flood disasters in southern China in 2020 based on social media data

Author

Listed:
  • Mingjun Ma

    (Tianjin Normal University)

  • Qiang Gao

    (Tianjin Normal University)

  • Zishuang Xiao

    (Tianjin Normal University)

  • Xingshuai Hou

    (Tianjin Normal University)

  • Beibei Hu

    (Tianjin Normal University)

  • Lifei Jia

    (Tianjin Normal University)

  • Wenfang Song

    (Tianjin Normal University)

Abstract

The exploding popularity of social networks provides a new opportunity to study disasters and public emotion. Among the social networks, Weibo is one of the largest microblogging services in China. Taking Guangdong and Guangxi in the south of China as a case, Web Scraper was used to obtain Weibo texts related to floods in 2020. The spatial distribution of floods was analyzed using Kernel Density Estimation. Public emotion was analyzed using Natural Language Processing tools. The association between floods and public emotion was explored through correlation analysis methods. The results indicated that: (1) Weibo texts could be utilized as effective data to identify urban waterlogging risk in Guangdong and Guangxi. (2) The waterlogging was mainly distributed in the southern part of Guangdong and Guangxi, especially in the provincial capitals and coastal cities. (3) Public emotion was predominantly negative, especially during periods of heavy precipitation. (4) There was a strong correlation between public emotion and floods in spatial–temporal variation. The degree of negative public emotion was significantly influenced by the number of waterlogging points. The presented results serve as the preliminary data for future planning and designing of emergency management. Graphical abstract

Suggested Citation

  • Mingjun Ma & Qiang Gao & Zishuang Xiao & Xingshuai Hou & Beibei Hu & Lifei Jia & Wenfang Song, 2023. "Analysis of public emotion on flood disasters in southern China in 2020 based on social media data," 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. 118(2), pages 1013-1033, September.
    • Handle: RePEc:spr:nathaz:v:118:y:2023:i:2:d:10.1007_s11069-023-06033-7
    DOI: 10.1007/s11069-023-06033-7
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    References listed on IDEAS

    as
    1. Luoyang Wang & Yao Li & Hao Hou & Yan Chen & Jinjin Fan & Pin Wang & Tangao Hu, 2022. "Analyzing spatial variance of urban waterlogging disaster at multiple scales based on a hydrological and hydrodynamic model," 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. 114(2), pages 1915-1938, November.
    2. Hua Bai & Guang Yu, 2016. "A Weibo-based approach to disaster informatics: incidents monitor in post-disaster situation via Weibo text negative sentiment analysis," 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. 83(2), pages 1177-1196, September.
    3. Yan Wang & John E. Taylor, 2018. "Coupling sentiment and human mobility in natural disasters: a Twitter-based study of the 2014 South Napa Earthquake," 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. 92(2), pages 907-925, June.
    4. Qing Deng & Yi Liu & Hui Zhang & Xiaolong Deng & Yefeng Ma, 2016. "A new crowdsourcing model to assess disaster using microblog data in typhoon Haiyan," 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. 84(2), pages 1241-1256, November.
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