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Towards Exploiting Social Networks for Detecting Epidemic Outbreaks

Author

Listed:
  • Sergio Di Martino

    (University of Naples “‘Federico II”’)

  • Sara Romano

    (CeRICT Scrl)

  • Michela Bertolotto

    (University College Dublin
    University College Dublin)

  • Nattiya Kanhabua

    (Aalborg University)

  • Antonino Mazzeo

    (University of Naples “‘Federico II”’)

  • Wolfgang Nejdl

    (Leibniz University of Hannover)

Abstract

Social networks are becoming a valuable source of information for applications in many domains. In particular, many studies have highlighted the potential of social networks for early detection of epidemic outbreaks, due to their capability to transmit information faster than traditional channels, thus leading to quicker reactions of public health officials. Anyhow, the most of these studies have investigated only one or two diseases, and consequently to date there is no study in the literature trying to investigate if and how different kinds of outbreaks may lead to different temporal dynamics of the messages exchanged over social networks. Furthermore, in case of a wide variability, it is not clear if it would be possible to define a single generic solution able to detect multiple epidemic outbreaks, or if specifically tailored approaches should be implemented for each disease. To get an insight into these open points, we collected a massive dataset, containing more than one hundred million Twitter messages from different countries, looking for those relevant for an early outbreak detection of multiple disease. The collected results highlight that there is a significant variability in the temporal patterns of Twitter messages among different diseases. In this paper, we report on the main findings of this analysis, and we propose a set of steps to exploit social networks for early epidemic outbreaks, including a proper document model for the outbreaks, a Graphical User Interface for the public health officials, and the identification of suitable sources of information useful as ground truth for the assessment of outbreak detection algorithms.

Suggested Citation

  • Sergio Di Martino & Sara Romano & Michela Bertolotto & Nattiya Kanhabua & Antonino Mazzeo & Wolfgang Nejdl, 2017. "Towards Exploiting Social Networks for Detecting Epidemic Outbreaks," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(1), pages 61-71, March.
    • Handle: RePEc:spr:gjofsm:v:18:y:2017:i:1:d:10.1007_s40171-016-0148-y
    DOI: 10.1007/s40171-016-0148-y
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    References listed on IDEAS

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    1. C. P. Farrington & N. J. Andrews & A. D. Beale & M. A. Catchpole, 1996. "A Statistical Algorithm for the Early Detection of Outbreaks of Infectious Disease," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 159(3), pages 547-563, May.
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    Cited by:

    1. Ashish Kumar Rathore & Santanu Das & P. Vigneswara Ilavarasan, 2018. "Social Media Data Inputs in Product Design: Case of a Smartphone," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 19(3), pages 255-272, September.
    2. Le Thanh Tam & Huong Xuan Ho & Dong Phong Nguyen & Arun Elias & Angelina Nhat Hanh Le, 2021. "Receptivity of Governmental Communication and Its Effectiveness During COVID-19 Pandemic Emergency in Vietnam: A Qualitative Study," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 22(1), pages 45-64, June.

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