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Characterizing information propagation patterns in emergencies: A case study with Yiliang Earthquake

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  • Li, Lifang
  • Zhang, Qingpeng
  • Tian, Jun
  • Wang, Haolin

Abstract

Social media has been playing an increasingly important role in information publishing and event monitoring in emergencies like natural disasters. The propagation of different types of information on social media is critical in understanding the reaction and mobility of social media users during natural disasters. In this research, we analyzed the dynamic social networks formed by the reposting (retweeting) behaviors in Weibo.com (the major microblog service in China) during Yiliang Earthquake. We developed a Multinomial Naïve Bayes Classifier to categorize the microblog posts into five types based on the content, and then characterized the information propagation patterns of the five types of information at different stages after the earthquake occurred. We found that the type of information has significant influence on the propagation patterns in terms of scale and topological features. This research revealed the important role of information type in the publicity and propagation of disaster-related information, thus generated data-driven insights for timely and efficient emergency management using the publicly available social media data.

Suggested Citation

  • Li, Lifang & Zhang, Qingpeng & Tian, Jun & Wang, Haolin, 2018. "Characterizing information propagation patterns in emergencies: A case study with Yiliang Earthquake," International Journal of Information Management, Elsevier, vol. 38(1), pages 34-41.
    • Handle: RePEc:eee:ininma:v:38:y:2018:i:1:p:34-41
    DOI: 10.1016/j.ijinfomgt.2017.08.008
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    4. Grover, Purva & Kar, Arpan Kumar & Davies, Gareth, 2018. "“Technology enabled Health” – Insights from twitter analytics with a socio-technical perspective," International Journal of Information Management, Elsevier, vol. 43(C), pages 85-97.
    5. Son, Jaebong & Lee, Hyung Koo & Jin, Sung & Lee, Jintae, 2019. "Content features of tweets for effective communication during disasters: A media synchronicity theory perspective," International Journal of Information Management, Elsevier, vol. 45(C), pages 56-68.
    6. Jamali, Mehdi & Nejat, Ali & Ghosh, Souparno & Jin, Fang & Cao, Guofeng, 2019. "Social media data and post-disaster recovery," International Journal of Information Management, Elsevier, vol. 44(C), pages 25-37.
    7. 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.
    8. Elbanna, Amany & Bunker, Deborah & Levine, Linda & Sleigh, Anthony, 2019. "Emergency management in the changing world of social media: Framing the research agenda with the stakeholders through engaged scholarship," International Journal of Information Management, Elsevier, vol. 47(C), pages 112-120.
    9. Fahim Sufi & Musleh Alsulami, 2025. "Disaster in the Headlines: Quantifying Narrative Variation in Global News Using Topic Modeling and Statistical Inference," Mathematics, MDPI, vol. 13(13), pages 1-23, June.
    10. Martínez-Rojas, María & Pardo-Ferreira, María del Carmen & Rubio-Romero, Juan Carlos, 2018. "Twitter as a tool for the management and analysis of emergency situations: A systematic literature review," International Journal of Information Management, Elsevier, vol. 43(C), pages 196-208.
    11. Dingde Xu & Linmei Zhuang & Xin Deng & Cheng Qing & Zhuolin Yong, 2020. "Media Exposure, Disaster Experience, and Risk Perception of Rural Households in Earthquake-Stricken Areas: Evidence from Rural China," IJERPH, MDPI, vol. 17(9), pages 1-20, May.

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