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)
|
PDF
Organised_Crime_and_Social_Media_ICCS16.pdf - Accepted Version All rights reserved. Download (936kB) | Preview |
|
PDF (Acceptance e-mail)
Andrews - 12178.pdf Restricted to Repository staff only Download (129kB) | Contact the author |
Abstract
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 - Does NOT include content added after October 2018: | Cultural Communication and Computing Research Institute > Communication and Computing Research Centre |
Departments - Does NOT include content added after October 2018: | Faculty of Science, Technology and Arts > Department of Computing |
Identification Number: | https://doi.org/10.1007/978-3-319-40985-6_11 |
Page Range: | 137-150 |
Depositing User: | Simon Andrews |
Date Deposited: | 29 Apr 2016 12:01 |
Last Modified: | 18 Mar 2021 06:47 |
URI: | https://shura.shu.ac.uk/id/eprint/12133 |
Actions (login required)
View Item |
Downloads
Downloads per month over past year