SEPM: rapid seism emergency information processing based on social media
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SEPM: rapid seism emergency information processing based on social media Xuesong Bai1 · Xiaoxue Liu1 · Shuhan Lu2 · Xiaodong Zhang1 · Wei Su1 · Xiaohui Su3 · Lin Li1 Received: 17 January 2019 / Accepted: 14 July 2020 © Springer Nature B.V. 2020
Abstract 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. Keywords Social media · Earthquake emergency information · Information classification · Thesaurus weight calculation · TF–IDF
1 Introduction Earthquake disasters have caused huge losses to people around the world. China is one of the countries with the strongest earthquake activity and the worst earthquake disaster in the world. The 2008 Wenchuan earthquake in Sichuan Province, the 2010 Yushu * Lin Li [email protected] 1
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
2
University of Michigan, Lansing 43210, USA
3
College of Information, Beijing Forestry University, Beijing 100083, China
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Natural Hazards
earthquake in Qinghai Province, and the 2013 Lushan earthquake in Sichuan Province all caused huge loss of life and property to China. According to statistics, the Wenchuan earthquake caused the death of 70,000 people, more than 370,000 people were injured, and the direct economic loss was 845.1 billion yuan (Cui et al. 2008). Qinghai Province earthquake, which had the highest magnitude of 7.1, affected a total of 67,000 households and 223,000 people (Zhang et al. 2010). Population urbanization is accelerating, and the loss of social disasters caused by destructive earthquakes is becoming increasingly serious. To minimize earthquake loss, earthquake emergency work is important, and earthquake emergency information plays a central role in the rescue work after earthquake emergen
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