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Using the Social Media Data in Disaster Management: Heavy Rainstorm in Zhengzhou, China

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This paper aims to study Weibo posts about the 2021 Zhengzhou rainstorm disaster to demonstrate the usefulness of social media data in disaster management. Social media data, as an emerging source of big data is increasingly being used to disseminate important information about disasters to the public. This paper explores the usefulness of Weibo data in disaster management by taking the Zhengzhou rainstorm in 2021 as a case. First, based on web crawlers and Weibo API, this study obtained Weibo posts related to topics of rainstorm. A temporal analysis of related Weibo posts shows that the temporal trend of the number of rainstorm related Weibo posts coincided with the developmental stages of the rainstorm. Second, topic extraction from Weibo content was performed, which found that people discuss different topics on social media in different disaster stages. Finally, the hot keywords of Weibo posts were extracted, which most intuitively expresses people's information exchange during rainstorm. This study demonstrates that disaster information extracted form social media could reflect the public's disaster risk perception and material needs well and have potential to disaster management resource.

Abstract

This paper aims to study Weibo posts about the 2021 Zhengzhou rainstorm disaster to demonstrate the usefulness of social media data in disaster management. Social media data, as an emerging source of big data is increasingly being used to disseminate important information about disasters to the public. This paper explores the usefulness of Weibo data in disaster management by taking the Zhengzhou rainstorm in 2021 as a case. First, based on web crawlers and Weibo API, this study obtained Weibo posts related to topics of rainstorm. A temporal analysis of related Weibo posts shows that the temporal trend of the number of rainstorm related Weibo posts coincided with the developmental stages of the rainstorm. Second, topic extraction from Weibo content was performed, which found that people discuss different topics on social media in different disaster stages. Finally, the hot keywords of Weibo posts were extracted, which most intuitively expresses people's information exchange during rainstorm. This study demonstrates that disaster information extracted form social media could reflect the public's disaster risk perception and material needs well and have potential to disaster management resource.

참고문헌 (61)

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