Organised crime and social media: a system for detecting, corroborating and visualising weak signals of organised crime online

Andrews, Simon; Brewster, Ben; Day, Tony

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 posts, can be treated as weak signals of OC, enabling information to be classified according to a taxonomy. Formal concept analysis is used to group information sources, according to crime-type 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 human trafficking and modern slavery 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.

Citation

Andrews, S., Brewster, B., & Day, T. (2018). Organised crime and social media: a system for detecting, corroborating and visualising weak signals of organised crime online. Security Informatics, 7(1), Article 3. https://doi.org/10.1186/s13388-018-0032-8

• Journal Article Type: Article
• Acceptance Date: Nov 27, 2018
• Online Publication Date: Dec 13, 2018
• Publication Date: 2018-12
• Deposit Date: Jan 21, 2020
• Publicly Available Date: Jan 21, 2020
• Journal: Security Informatics
• Electronic ISSN: 2190-8532
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Issue: 1
• Article Number: 3
• DOI: https://doi.org/10.1186/s13388-018-0032-8
• Public URL: https://nottingham-repository.worktribe.com/output/3774106
• Publisher URL: https://security-informatics.springeropen.com/articles/10.1186/s13388-018-0032-8
• Additional Information: Received: 25 October 2017; Accepted: 27 November 2018; First Online: 13 December 2018