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Predicting Popularity of Microblogs in Emerging Disease Event

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Web-Age Information Management (WAIM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8597))

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Abstract

During emerging disease outbreaks, massive information are disseminated through social network. In China, Sina microblog system as the biggest social network provide a novel way to monitoring the development of emerging disease and public awareness. However, only a small percentage of microblogs could wide spread. Therefore, predict popularity of microblogs timely are meaningful for emergency management. In this paper, a Judgment method for popularity level prediction of microblog is proposed and the temporal pattern between cases number and repost number is verified. Repost number is considered to measure the impact of microblogs. To predict the popularity of microblogs, Granger causality test was used to verify the temporal correlation pattern between development of disease and public concern while an Judgment method based on five classical classification models were proposed. Through analyses, case number of emerging disease are Granger causality of the popularity level of microblogs and the regression model got the best result when lag was three. By Judgment method, more than 86 % microblogs can be classified correctly. The proposed Judgment method based on user, microblog and emerging disease information could analysis the popularity level of microblogs speedily and accurately. This is important and meaningful for monitoring the development of future public health event.

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Acknowledgments

This study was funded by National Natural Science Foundation of China (Nos.90924302, 91224008, 91024030, 91324007) and Important National Science & Technology Specific Projects (Nos.2012ZX10004801, 2013ZX10004218).

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Authors and Affiliations

  1. The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Jiaqi Liu, Zhidong Cao & Daniel Zeng

  2. University of Arizona, Tucson, AZ, USA

    Daniel Zeng

Authors
  1. Jiaqi Liu
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  2. Zhidong Cao
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  3. Daniel Zeng
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Corresponding author

Correspondence to Zhidong Cao .

Editor information

Editors and Affiliations

  1. DEKE Lab., Renmin University of China, Beijing, China

    Yueguo Chen

  2. Institute for Information Systems, Technical University Braunschweig, Braunschweig, Germany

    Wolf-Tilo Balke

  3. Hong Kong Baptist University Dept. Computer Science, Kowloon Tong, Hong Kong SAR

    Jianliang Xu

  4. School of Information, Renmin University of China, Beijing, China

    Wei Xu

  5. School of Computer Science and Technology, Hefei, China

    Peiquan Jin

  6. Department of Computer Science, East China Normal University, Shanghai, China

    Xin Lin

  7. Department of Computer Science, Kean University, Wenzhou, China

    Tiffany Tang

  8. School of Electrical Engineering, Korea University, Seoul, Korea, Republic of (South Korea)

    Eenjun Hwang

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Liu, J., Cao, Z., Zeng, D. (2014). Predicting Popularity of Microblogs in Emerging Disease Event. In: Chen, Y., et al. Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science(), vol 8597. Springer, Cham. https://doi.org/10.1007/978-3-319-11538-2_1

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  • DOI: https://doi.org/10.1007/978-3-319-11538-2_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11537-5

  • Online ISBN: 978-3-319-11538-2

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