Despite the predominant role played by Internet in the distribution of doping substances, little is currently known about the online offer of doping products. Therefore, the study focuses on the detection of doping substances and suppliers discussed in Internet forums. It aims at having a comprehensive understanding of products and sellers to lead an operational monitoring of the online doping market. Thirteen community forums on the Internet were investigated and one million topics were extracted with source code scrappers. Then, a semantic analysis was conducted with a semi-automatic process to classify the relevant words according to doping matters. Additionally, the ranking of doping products, active substances and suppliers in regards to the number of contributors to the forums were established and analyzed over time. Finally, promotion methods of suppliers were evaluated. The results show that anabolic androgenic steroids, used to enhance body image and performance, are the most discussed type of products. A temporal analysis illustrates the stability of the most popular products as well as the emergence of new products such as peptides (e.g. CJC-1295). 327 suppliers were detected, mostly with dedicated websites or direct sales by e-mail as selling methods. Globally, the implemented methodology shows its ability to detect products and suppliers as well as to follow their temporal trends. The intelligence will serve the definition of online monitoring strategies (e.g. the selection of appropriate keywords). Additionally, it also allows the adjustment of customs inspection strategies and anti-doping analysis by monitoring the popular and emerging substances.

No abstract

No abstract

Grouping events having similarities has always been interesting for analysts. Actually, when a label is put on top of a set of events to denote they share common properties, the automation and the capability to conduct reasoning with this set drastically increase. This is particularly true when considering criminal events for crime analysts; conjunction, interpretation and explanation can be key success factors to apprehend criminals. In this paper, we present the CriLiM methodology for investigating both serious and high-volume crime. Our artifact consists in implementing a tailored computerized crime linkage system, based on a fuzzy MCDM approach in order to combine spatio-temporal, behavioral, and forensic information. As a proof of concept, series in burglaries are examined from real data and compared to expert results.

No abstract