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The Spontaneous Behavior in Extreme Events: A Clustering-Based Quantitative Analysis

  • Ning Shi
  • Chao Gao
  • Zili Zhang
  • Lu Zhong
  • Jiajin Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8346)

Abstract

Social media records the pulse of social discourse and drives human behaviors in temporal and spatial dimensions, as well as the structural characteristics. These online contexts give us an opportunity to understand social perceptions of people in the context of certain events, and can help us improve disaster relief. Taking Twitter as data source, this paper quantitatively measures exogenous and endogenous social influences on collective behaviors in different events based on standard fluctuation scaling method. Different from existing studies utilizing manual keywords to denote events, we apply a clustering-based event analysis to identify the core event and its related episodes in a hashtag network. The statistical results show that exogenous factors drive the amount of information about an event and the endogenous factors play a major role in the propagation of hashtags.

Keywords

Social Medium Extreme Event Collective Behavior Core Node Spontaneous Emergence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ning Shi
    • 1
  • Chao Gao
    • 1
  • Zili Zhang
    • 1
    • 2
  • Lu Zhong
    • 1
  • Jiajin Huang
    • 3
  1. 1.College of Computer and Information ScienceSouthwest UniversityChongqingChina
  2. 2.School of Information TechnologyDeakin UniversityAustralia
  3. 3.International WIC InstituteBeijing University of TechnologyBeijingChina

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