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Estimating the growth models of news stories on disasters

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  • Jiuchang Wei
  • Dingtao Zhao
  • Liang Liang

Abstract

Understanding the growth models of news stories on disasters is a key issue for efficient disaster management. This article proposes a method to identify three growth models: the Damped Exponential Model, the Normal Model, and the Fluctuating Model. This method is proven to be valid using the 112 disasters occurring between 2003 and 2008. The factors that influence the likelihood of the growth models include disaster types, newsworthy material, disaster severity, and economic development of the affected area. This article suggests that disaster decision‐makers can identify the respective likelihood of the three growth models of news stories when a disaster happens, and thereby implement effective measures in response to the disaster situation.

Suggested Citation

  • Jiuchang Wei & Dingtao Zhao & Liang Liang, 2009. "Estimating the growth models of news stories on disasters," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(9), pages 1741-1755, September.
    • Handle: RePEc:bla:jamist:v:60:y:2009:i:9:p:1741-1755
    DOI: 10.1002/asi.21109
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    Cited by:

    1. Weiwei Zhu & Gaorong Zhang & Qi Shen & Chuanhui Liao, 2022. "The Dynamics of Public Attention to Online Disaster Information," International Journal of Social Science Studies, Redfame publishing, vol. 10(3), pages 56-66, May.
    2. Kim, Taekyung, 2014. "Observation on copying and pasting behavior during the Tohoku earthquake: Retweet pattern changes," International Journal of Information Management, Elsevier, vol. 34(4), pages 546-555.
    3. David P. Durham & Elizabeth A. Casman & Steven M. Albert, 2012. "Deriving Behavior Model Parameters from Survey Data: Self‐Protective Behavior Adoption During the 2009–2010 Influenza A(H1N1) Pandemic," Risk Analysis, John Wiley & Sons, vol. 32(12), pages 2020-2031, December.
    4. Vincenza Capone & Daniela Caso & Anna Rosa Donizzetti & Fortuna Procentese, 2020. "University Student Mental Well-Being during COVID-19 Outbreak: What Are the Relationships between Information Seeking, Perceived Risk and Personal Resources Related to the Academic Context?," Sustainability, MDPI, vol. 12(17), pages 1-17, August.
    5. Yunhong Xu & Dehu Yin & Duanning Zhou, 2019. "Investigating Users’ Tagging Behavior in Online Academic Community Based on Growth Model: Difference between Active and Inactive Users," Information Systems Frontiers, Springer, vol. 21(4), pages 761-772, August.
    6. Zhao, Dingtao & Wang, Feng & Wei, Jiuchang & Liang, Liang, 2013. "Public reaction to information release for crisis discourse by organization: Integration of online comments," International Journal of Information Management, Elsevier, vol. 33(3), pages 485-495.
    7. Chiung-wen Hsu, 2013. "The emergence of “star disaster-affected areas” and its implications to disaster and communication interdisciplinary study: a Taiwan example from Typhoon Morakot," 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. 69(1), pages 39-57, October.
    8. Yangyang Fan & Liangdong Lu & Jia Xu & Fenge Wang & Fei Wang, 2022. "Air Pollution Control and Public Health Risk Perception: Evidence from the Perspectives of Signal and Implementation Effects," IJERPH, MDPI, vol. 19(5), pages 1-15, March.
    9. Wei, Jiuchang & Bu, Bing & Liang, Liang, 2012. "Estimating the diffusion models of crisis information in micro blog," Journal of Informetrics, Elsevier, vol. 6(4), pages 600-610.

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