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

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 L.J. Dijkstra and A.V. Yakushev share first authorship of this work. L.J. Dijkstra · P.M.A. Sloot Computational Science Department, University of Amsterdam (UvA), The Netherlands. E-mail: [email protected] A.V. Yakushev · L.J. Dijkstra · A.V. Boukhanovsky · P.M.A. Sloot High-Performance Computing Department, National Research University of Information Technologies, Mechanics and Optics (NRU ITMO), Saint Petersburg, Russia. E-mail: [email protected] P.A.C. Duijn Criminal Intelligence Analysis, Dutch Police, The Netherlands. E-mail: [email protected] A.V. Boukhanovsky E-mail: avb [email protected] P.M.A. Sloot (corresponding author) School of Computer Engineering (SCE), Nanyang Technological University (NTU), Singapore. Tel.: +31 (0) 20 525 7537 Fax: +31 (0) 20 525 7419 E-mail: [email protected] 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’.