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

Author

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
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    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.

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