Inference of the Russian drug community from one of the largest social networks in the Russian Federation
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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’.
KeywordsIllicit drug use Drug use Social network LiveJournal Power-law Russian Federation
The authors thank Dr. Sergey Mityagin from the Saint Petersburg Information and Analytical Center (SPb IAC) for fruitful discussions on the drug addiction profiles in the Russian Federation. In addition, the authors would like to express their gratitude to Prof. Dr. T.K. Dijkstra from the University of Groningen (RUG) and the Free University Amsterdam (VU) for introducing us with false discovery rate control and his useful remarks. This work is supported by the Leading Scientist Program of the Russian Federation, contract 11.G34.31.0019, as well as by the Complexity program of NTU, Singapore. Peter Sloot also acknowledges the support from the FET-Proactive Grant TOPDRIM, Number FP7-ICT-318121.
- Agar, M.: Agents in living color: towards emic agent-based models. J. Artif. Soc. Soc. Simul. 8(4). http://jasss.soc.surrey.ac.uk/8/1/4.html (2005). Accessed 13 May 2013
- Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 57(1), 289–300 (1995)Google Scholar
- Bernades, D.F., Latapy, M., Tarissan, F.: Relevance of SIR model for real-world spreading phenomena: experiments on a large-scale p2p system. In: Proceedings of the International Conference on Advances in Social Network Analysis and Mining (ASONAM), Istanbul (2012)Google Scholar
- Everitt, B., Landau, S., Leese, M.: Cluster Analysis. Arnold, London (2001)Google Scholar
- Gallos, L.K., Barttfield, P., Havlin, S., Sigman, M., Makse, H.A.: Collective behavior in the spatial spreading of obesity. Sci. Rep. 2(45), 1–9 (2012)Google Scholar
- Mityagin, S.A.: Modeling the spread of drug-addiction through the population on the basis of complex networks (in Russian—Modelirovanie processov narkotizatsiya nasileniya na osnove kompleksnix cetei). Dissertation, National Research University of Information Technologies, Mechanics and Optics (2012)Google Scholar
- Porter, M.F.: Stemming algorithms for various European languages. http://www.snowball.tartarus.org/texts/stemmersoverview.html (2006). Accessed 19 November 2012
- Sunami, A.N.: Drug-conflict management in the context of information warfare (in Russian—Politika upravleniya narkokonfliktom v kontekste informatsionnoi voiny). Saint Petersburg State University, Saint Petersburg (2007)Google Scholar
- White, T.: Hadoop: The Definitive Guide. O’Reilly Media, Yahoo! Press, New York (2009)Google Scholar