Organised crime and social media: detecting and corroborating weak signals of human trafficking online

ANDREWS, Simon, BREWSTER, Benjamin and DAY, Tony (2016). Organised crime and social media: detecting and corroborating weak signals of human trafficking online. In: HAEMMERLÉ, Ollivier, STAPLETON, Gem and FARON-ZUCKER, Catherine, (eds.) Graph-based representation and reasoning. Lecture notes in computer science (9717). Heidelberg, Springer, 137-150. (In Press)

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This paper describes an approach for detecting the presence or emergence of Organised Crime (OC) signals on Social Media. It shows how words and phrases, used by members of the public in Social Media, can be treated as weak signals of OC, enabling information to be classified according to a taxonomy of OC. Formal Concept Analysis is used to group information sources, according to Crime and Location, thus providing a means of corroboration and creating OC Concepts that can be used to alert police analysts to the possible presence of OC. The analyst is able to `drill down' into an OC Concept of interest, discovering additional information that may be pertinent to the crime. The paper describes the implementation of this approach into a fully-functional prototype software system, incorporating a Social Media Scanning System and a map-based user interface. The approach and system are illustrated using the Trafficking of Human Beings as an example. Real data is used to obtain results that show that weak signals of OC have been detected and corroborated, thus alerting to the possible presence of OC. Keyword : organised crime, social media, formal concept analysis.

Item Type: Book Section
Additional Information: Series ISSN 0302-9743
Research Institute, Centre or Group: Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
Departments: Faculty of Science, Technology and Arts > Department of Computing
Identification Number:
Depositing User: Simon Andrews
Date Deposited: 29 Apr 2016 12:01
Last Modified: 28 Jan 2019 16:00

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