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Temporal evolution of online extremist support

Author

Listed:
  • Cao, Zhenfeng
  • Zheng, Minzhang
  • Manrique, Pedro D.
  • He, Zhou
  • Johnson, Neil F.

Abstract

There is a significant amount of online human activity which is either clandestine or illicit in nature, and hence where individuals operate under fear of exposure or capture. Yet there is little theoretical understanding of what models best describe the resulting dynamics. Here we attempt to address this gap, by analyzing the evolutionary dynamics of the supporters behind the 95 pro-ISIS online communities (i.e. self-organized social media groups) that appeared recently on a global social media site. We show that their dynamical evolution can be explained by a model that incorporates effects of heterogeneity and network locality. Our analysis contributes to the understanding of online extremist support, and may also shed light on a broader spectrum of online human activities which are either clandestine or illicit in nature, and hence where individuals also operate under fear of exposure or capture.

Suggested Citation

  • Cao, Zhenfeng & Zheng, Minzhang & Manrique, Pedro D. & He, Zhou & Johnson, Neil F., 2019. "Temporal evolution of online extremist support," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 169-180.
    • Handle: RePEc:eee:phsmap:v:519:y:2019:i:c:p:169-180
    DOI: 10.1016/j.physa.2018.12.033
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    Cited by:

    1. Havrda, Marek & Klocek, Adam, 2023. "Well-being impact assessment of artificial intelligence – A search for causality and proposal for an open platform for well-being impact assessment of AI systems," Evaluation and Program Planning, Elsevier, vol. 99(C).
    2. Kim, Woojung & Wang, Xiaokun Cara, 2022. "The adoption of alternative delivery locations in New York City: Who and how far?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 127-140.

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