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Microblog Topic-Words Detection Model for Earthquake Emergency Responses Based on Information Classification Hierarchy

Author

Listed:
  • Xiaohui Su

    (School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
    Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 100083, China)

  • Shurui Ma

    (School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
    Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 100083, China)

  • Xiaokang Qiu

    (School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China)

  • Jiabin Shi

    (School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
    Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 100083, China)

  • Xiaodong Zhang

    (College of Land Science and Technology, China Agricultural University, Beijing 100083, China)

  • Feixiang Chen

    (School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
    Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 100083, China)

Abstract

Social media data are constantly updated, numerous, and characteristically prominent. To quickly extract the needed information from the data to address earthquake emergencies, a topic-words detection model of earthquake emergency microblog messages is studied. First, a case analysis method is used to analyze microblog information after earthquake events. An earthquake emergency information classification hierarchy is constructed based on public demand. Then, subject sets of different granularities of earthquake emergency information classification are generated through the classification hierarchy. A detection model of new topic-words is studied to improve and perfect the sets of topic-words. Furthermore, the validity, timeliness, and completeness of the topic-words detection model are verified using 2201 messages obtained after the 2014 Ludian earthquake. The results show that the information acquisition time of the model is short. The validity of the whole set is 96.96%, and the average and maximum validity of single words are 78% and 100%, respectively. In the Ludian and Jiuzhaigou earthquake cases, new topic-words added to different earthquakes only reach single digits in validity. Therefore, the experiments show that the proposed model can quickly obtain effective and pertinent information after an earthquake, and the complete performance of the earthquake emergency information classification hierarchy can meet the needs of other earthquake emergencies.

Suggested Citation

  • Xiaohui Su & Shurui Ma & Xiaokang Qiu & Jiabin Shi & Xiaodong Zhang & Feixiang Chen, 2021. "Microblog Topic-Words Detection Model for Earthquake Emergency Responses Based on Information Classification Hierarchy," IJERPH, MDPI, vol. 18(15), pages 1-20, July.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:15:p:8000-:d:603539
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    References listed on IDEAS

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    1. Thomas J. Huggins & Raj Prasanna, 2020. "Information Technologies Supporting Emergency Management Controllers in New Zealand," Sustainability, MDPI, vol. 12(9), pages 1-16, May.
    2. Xu Liu & Bihan Tang & Hongyang Yang & Yuan Liu & Chen Xue & Lulu Zhang, 2015. "The Technical Efficiency of Earthquake Medical Rapid Response Teams Following Disasters: The Case of the 2010 Yushu Earthquake in China," IJERPH, MDPI, vol. 12(12), pages 1-10, December.
    3. Yates, Dave & Paquette, Scott, 2011. "Emergency knowledge management and social media technologies: A case study of the 2010 Haitian earthquake," International Journal of Information Management, Elsevier, vol. 31(1), pages 6-13.
    4. Xuesong Bai & Xiaoxue Liu & Shuhan Lu & Xiaodong Zhang & Wei Su & Xiaohui Su & Lin Li, 2020. "SEPM: rapid seism emergency information processing based on social media," 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. 104(1), pages 659-679, October.
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    1. Shasha Li & Xinyu Peng & Ruiqiu Pang & Li Li & Zixuan Song & Hongying Ye, 2021. "Information Preference and Information Supply Efficiency Evaluation before, during, and after an Earthquake: Evidence from Songyuan, China," IJERPH, MDPI, vol. 18(24), pages 1-28, December.

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