IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v48y2014i5p2739-2755.html
   My bibliography  Save this article

Inference of the Russian drug community from one of the largest social networks in the Russian Federation

Author

Listed:
  • L. Dijkstra
  • A. Yakushev
  • P. Duijn
  • A. Boukhanovsky
  • P. Sloot

Abstract

The criminal nature of narcotics complicates the direct assessment of a drug community, while having a good understanding of the type of people drawn or currently using drugs is vital for finding effective intervening strategies. Especially for the Russian Federation this is of immediate concern given the dramatic increase it has seen in drug abuse since the fall of the Soviet Union in the early nineties. Using unique data from the Russian social network ‘LiveJournal’ with over 39 million registered users worldwide, we were able for the first time to identify the on-line drug community by context sensitive text mining of the users’ blogs using a dictionary of known drug-related official and ‘slang’ terminology. By comparing the interests of the users that most actively spread information on narcotics over the network with the interests of the individuals outside the on-line drug community, we found that the ‘average’ drug user in the Russian Federation is generally mostly interested in topics such as Russian rock, non-traditional medicine, UFOs, Buddhism, yoga and the occult. We identify three distinct scale-free sub-networks of users which can be uniquely classified as being either ‘infectious’, ‘susceptible’ or ‘immune’. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • L. Dijkstra & A. Yakushev & P. Duijn & A. Boukhanovsky & P. Sloot, 2014. "Inference of the Russian drug community from one of the largest social networks in the Russian Federation," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(5), pages 2739-2755, September.
    • Handle: RePEc:spr:qualqt:v:48:y:2014:i:5:p:2739-2755
    DOI: 10.1007/s11135-013-9921-6
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11135-013-9921-6
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11135-013-9921-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
    2. Crucitti, Paolo & Latora, Vito & Marchiori, Massimo & Rapisarda, Andrea, 2003. "Efficiency of scale-free networks: error and attack tolerance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 320(C), pages 622-642.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hyun Baek & Sun-Kyoung Park, 2015. "Sustainable Development Plan for Korea through Expansion of Green IT: Policy Issues for the Effective Utilization of Big Data," Sustainability, MDPI, vol. 7(2), pages 1-21, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhou, Yaoming & Wang, Junwei, 2018. "Efficiency of complex networks under failures and attacks: A percolation approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 658-664.
    2. Zhao, Jianyu & Wei, Jiang & Yu, Lean & Xi, Xi, 2022. "Robustness of knowledge networks under targeted attacks: Electric vehicle field of China evidence," Structural Change and Economic Dynamics, Elsevier, vol. 63(C), pages 367-382.
    3. Viljoen, Nadia M. & Joubert, Johan W., 2016. "The vulnerability of the global container shipping network to targeted link disruption," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 396-409.
    4. Morehead, Raymond & Noore, Afzel, 2007. "Novel hybrid mitigation strategy for improving the resiliency of hierarchical networks subjected to attacks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(2), pages 603-612.
    5. Jalili, Mahdi, 2011. "Synchronizability of dynamical scale-free networks subject to random errors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4588-4595.
    6. Jalili, Mahdi, 2011. "Error and attack tolerance of small-worldness in complex networks," Journal of Informetrics, Elsevier, vol. 5(3), pages 422-430.
    7. Kashyap, G. & Ambika, G., 2019. "Link deletion in directed complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 631-643.
    8. Kashin Sugishita & Yasuo Asakura, 2021. "Vulnerability studies in the fields of transportation and complex networks: a citation network analysis," Public Transport, Springer, vol. 13(1), pages 1-34, March.
    9. Lv, Changchun & Yuan, Ziwei & Si, Shubin & Duan, Dongli, 2021. "Robustness of scale-free networks with dynamical behavior against multi-node perturbation," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    10. Aymeric Vié & Alfredo J. Morales, 2021. "How Connected is Too Connected? Impact of Network Topology on Systemic Risk and Collapse of Complex Economic Systems," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1327-1351, April.
    11. Zhang, Jianhua & Wang, Shuliang & Wang, Xiaoyuan, 2018. "Comparison analysis on vulnerability of metro networks based on complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 72-78.
    12. Milena Oehlers & Benjamin Fabian, 2021. "Graph Metrics for Network Robustness—A Survey," Mathematics, MDPI, vol. 9(8), pages 1-48, April.
    13. Shen, Dongqin & Cao, Shanshan, 2018. "An efficient immunization strategy based on transmission limit in weighted complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 1-7.
    14. Liu, Qing & Yang, Yang & Ng, Adolf K.Y. & Jiang, Changmin, 2023. "An analysis on the resilience of the European port network," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    15. Gao, Yan-Li & Chen, Shi-Ming & Nie, Sen & Ma, Fei & Guan, Jun-Jie, 2018. "Robustness analysis of interdependent networks under multiple-attacking strategies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 495-504.
    16. Stefano Martinazzi & Andrea Flori, 2020. "The evolving topology of the Lightning Network: Centralization, efficiency, robustness, synchronization, and anonymity," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-18, January.
    17. Lekha, Divya Sindhu & Balakrishnan, Kannan, 2020. "Central attacks in complex networks: A revisit with new fallback strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    18. Xia Cao & Chuanyun Li & Wei Chen & Jinqiu Li & Chaoran Lin, 2020. "Research on the invulnerability and optimization of the technical cooperation innovation network based on the patent perspective—A case study of new energy vehicles," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-19, September.
    19. Sohn, Insoo, 2019. "A robust complex network generation method based on neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 593-601.
    20. Matteo Cinelli & Giovanna Ferraro & Antonio Iovanella, 2017. "Resilience of Core-Periphery Networks in the Case of Rich-Club," Complexity, Hindawi, vol. 2017, pages 1-12, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:qualqt:v:48:y:2014:i:5:p:2739-2755. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.