DOI: 10.1007/s11069-020-04185-4
论文题名: SEPM: rapid seism emergency information processing based on social media
作者: Bai X. ; Liu X. ; Lu S. ; Zhang X. ; Su W. ; Su X. ; Li L.
刊名: Natural Hazards
ISSN: 0921030X
出版年: 2020
卷: 104, 期: 1 起始页码: 659
结束页码: 679
语种: 英语
中文关键词: Earthquake emergency information
; Information classification
; Social media
; TF–IDF
; Thesaurus weight calculation
英文关键词: classification
; disaster relief
; earthquake event
; information processing
; social media
; social network
英文摘要: With the development of network communication technology and the popularity of social media tools, earthquake-related information has been easily published and disseminated in social networks. This study focuses on obtaining this information and providing guidance for earthquake emergency work. A processing model is proposed to obtain earthquake information from social networks. First, a configuration-driven data acquisition module is designed to acquire earthquake information. Second, according to the characteristics of earthquake information in social media, a seismic emergency thesaurus is selected, and weight is calculated. To solve the low accuracy of inter-class classification, an improved mutual term frequency–inverse document frequency (MTF–IDF) algorithm is proposed. Finally, the thesaurus database is used to classify the acquired earthquake information. By taking the Lushan and Jiuzhaigou earthquakes as examples, the improved MTF–IDF algorithm shows a better effect on the selection of seismic keywords than the traditional TF–IDF algorithm; the F1-measure in classification has increased from 79.86 to 86.93%. The proposed model can rapidly and easily acquire and classify earthquake information according to different sources, which can provide timely information and support for disaster relief. © 2020, Springer Nature B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/168391
Appears in Collections: 气候变化与战略
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作者单位: College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China; University of Michigan, Lansing, 43210, United States; College of Information, Beijing Forestry University, Beijing, 100083, China
Recommended Citation:
Bai X.,Liu X.,Lu S.,et al. SEPM: rapid seism emergency information processing based on social media[J]. Natural Hazards,2020-01-01,104(1)