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Published in: Quality & Quantity 5/2014

01-09-2014

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

Authors: L. J. Dijkstra, A. V. Yakushev, P. A. C. Duijn, A. V. Boukhanovsky, P. M. A. Sloot

Published in: Quality & Quantity | Issue 5/2014

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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’.

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Appendix
Available only for authorised users
Footnotes
1
LiveJournal is available at http://​www.​livejournal.​com (English) and http://​www.​livejournal.​ru (Russian).
 
2
Facebook is available at http://​www.​facebook.​com.
 
3
Twitter is available at http://​www.​twitter.​com.
 
4
LiveJournal’s own statistics page can be found at http://​www.​livejournal.​com/​stats.​bml.
 
5
The homepage of SPb IAC can be found at http://​iac.​spb.​ru (in Russian).
 
6
The full drug-dictionary is freely available and can be downloaded at http://​escience.​ifmo.​ru/​?​ws=​sub48.
 
7
The number of phrases (8,359) is rather high in comparison to the number of words (368) in this dictionary. This is due to the fact that we consider a phrase consisting, for example, of the words ‘injecting’, ‘heroin’ and the phrase with the words ‘injection’, ‘heroin’ and ‘needle’ as two separate expressions (where the latter is associated with a higher weight than the former).
 
8
A \(\chi ^2\) test originally designed for \(2 \times 2\) contingency tables by Sir R. A. Fisher (1922).
 
9
Strictly speaking, the expected false discovery rate is only upper bounded when the \(m\) test statistics are independent, which does not hold in this particular case. B. Efron makes the case in his book Large-Scale Inference (2010) that this independency constraint is not strong.
 
10
The governmental statistics agency of the Russian Federation. They can be found at http://​www.​gks.​ru (in Russian) with links to their rather extensive database.
 
11
A rank/frequency log–log plot is the plot of the occurrence frequency versus the rank on logarithmically scaled axes. For a more elaborate description on how to construct such a plot, see the paper by Newman (2005), Appendix.
 
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Metadata
Title
Inference of the Russian drug community from one of the largest social networks in the Russian Federation
Authors
L. J. Dijkstra
A. V. Yakushev
P. A. C. Duijn
A. V. Boukhanovsky
P. M. A. Sloot
Publication date
01-09-2014
Publisher
Springer Netherlands
Published in
Quality & Quantity / Issue 5/2014
Print ISSN: 0033-5177
Electronic ISSN: 1573-7845
DOI
https://doi.org/10.1007/s11135-013-9921-6

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