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.
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