Discovering the relationship of disasters from big scholar and social media news datasets
The construction method for chains of disasters or events is still one of the core scientific questions in studying the common rules of disaster’s evolution. Especially when dealing with the complexity and diversity of disasters, it is critical to make a further investigation on reducing the depende...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2019-11-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17538947.2018.1514082 |
| _version_ | 1797678585124749312 |
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| author | Liang Zheng Fei Wang Xiaocui Zheng Binbin Liu |
| author_facet | Liang Zheng Fei Wang Xiaocui Zheng Binbin Liu |
| author_sort | Liang Zheng |
| collection | DOAJ |
| description | The construction method for chains of disasters or events is still one of the core scientific questions in studying the common rules of disaster’s evolution. Especially when dealing with the complexity and diversity of disasters, it is critical to make a further investigation on reducing the dependency of prior knowledge and supporting the comprehensive chains of disasters. This paper tries to propose a novel approach, through collecting the big scholar and social news data with disaster-related keywords, analysing the strength of their relationships with the co-word analysis method, and constructing a complex network of all defined disaster types, in order to finally intelligently extract the unique disaster chain of a specific disaster type. Google Scholar, Baidu Scholar and Sina News search engines are employed to acquire the needed data, and the respectively obtained disaster chains are compared with each other to show the robustness of our proposed approach. The achieved disaster chains are also compared with the ones concluded from existing research methods, and the very reasonable result is demonstrated. There is a great potential to apply this novel method in disaster management domain to find more secrets about disasters. |
| first_indexed | 2024-03-11T23:02:02Z |
| format | Article |
| id | doaj.art-d6769849ec024ed98cdf53d5d9bb7d20 |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-03-11T23:02:02Z |
| publishDate | 2019-11-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj.art-d6769849ec024ed98cdf53d5d9bb7d202023-09-21T14:57:08ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552019-11-0112111341136310.1080/17538947.2018.15140821514082Discovering the relationship of disasters from big scholar and social media news datasetsLiang Zheng0Fei Wang1Xiaocui Zheng2Binbin Liu3Tsinghua UniversityTsinghua UniversityTsinghua UniversityTsinghua UniversityThe construction method for chains of disasters or events is still one of the core scientific questions in studying the common rules of disaster’s evolution. Especially when dealing with the complexity and diversity of disasters, it is critical to make a further investigation on reducing the dependency of prior knowledge and supporting the comprehensive chains of disasters. This paper tries to propose a novel approach, through collecting the big scholar and social news data with disaster-related keywords, analysing the strength of their relationships with the co-word analysis method, and constructing a complex network of all defined disaster types, in order to finally intelligently extract the unique disaster chain of a specific disaster type. Google Scholar, Baidu Scholar and Sina News search engines are employed to acquire the needed data, and the respectively obtained disaster chains are compared with each other to show the robustness of our proposed approach. The achieved disaster chains are also compared with the ones concluded from existing research methods, and the very reasonable result is demonstrated. There is a great potential to apply this novel method in disaster management domain to find more secrets about disasters.http://dx.doi.org/10.1080/17538947.2018.1514082disaster chainco-occurrence analysisco-word analysiscommunity divisioncomplex networkdata miningdisaster management |
| spellingShingle | Liang Zheng Fei Wang Xiaocui Zheng Binbin Liu Discovering the relationship of disasters from big scholar and social media news datasets International Journal of Digital Earth disaster chain co-occurrence analysis co-word analysis community division complex network data mining disaster management |
| title | Discovering the relationship of disasters from big scholar and social media news datasets |
| title_full | Discovering the relationship of disasters from big scholar and social media news datasets |
| title_fullStr | Discovering the relationship of disasters from big scholar and social media news datasets |
| title_full_unstemmed | Discovering the relationship of disasters from big scholar and social media news datasets |
| title_short | Discovering the relationship of disasters from big scholar and social media news datasets |
| title_sort | discovering the relationship of disasters from big scholar and social media news datasets |
| topic | disaster chain co-occurrence analysis co-word analysis community division complex network data mining disaster management |
| url | http://dx.doi.org/10.1080/17538947.2018.1514082 |
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