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A Community Perspective on Resilience Analytics: A Visual Analysis of Community Mood

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  • Armando López‐Cuevas
  • José Ramírez‐Márquez
  • Gildardo Sanchez‐Ante
  • Kash Barker

Abstract

Social networks are ubiquitous in everyday life. Although commonly analyzed from a perspective of individual interactions, social networks can provide insights about the collective behavior of a community. It has been shown that changes in the mood of social networks can be correlated to economic trends, public demonstrations, and political reactions, among others. In this work, we study community resilience in terms of the mood variations of the community. We have developed a method to characterize the mood steady‐state of online social networks and to analyze how this steady‐state is affected under certain perturbations or events that affect a community. We applied this method to study community behavior for three real social network situations, with promising results.

Suggested Citation

  • Armando López‐Cuevas & José Ramírez‐Márquez & Gildardo Sanchez‐Ante & Kash Barker, 2017. "A Community Perspective on Resilience Analytics: A Visual Analysis of Community Mood," Risk Analysis, John Wiley & Sons, vol. 37(8), pages 1566-1579, August.
    • Handle: RePEc:wly:riskan:v:37:y:2017:i:8:p:1566-1579
    DOI: 10.1111/risa.12788
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    References listed on IDEAS

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    Cited by:

    1. Wen, Tao & Gao, Qiuya & Chen, Yu-wang & Cheong, Kang Hao, 2022. "Exploring the vulnerability of transportation networks by entropy: A case study of Asia–Europe maritime transportation network," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    2. Tsan‐Ming Choi & James H. Lambert, 2017. "Advances in Risk Analysis with Big Data," Risk Analysis, John Wiley & Sons, vol. 37(8), pages 1435-1442, August.
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    5. Shital A. Thekdi & Joost Santos, 2019. "Decision‐Making Analytics Using Plural Resilience Parameters for Adaptive Management of Complex Systems," Risk Analysis, John Wiley & Sons, vol. 39(4), pages 871-889, April.

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