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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Andrews, S.: In-Close2, a high performance formal concept miner. In: Andrews, S., Polovina, S., Hill, R., Akhgar, B. (eds.) ICCS-ConceptStruct 2011. LNCS, vol. 6828, pp. 50–62. Springer, Heidelberg (2011)
Brewster, B., Ingle, T., Rankin, G.: Crawling open-source data for indicators of human trafficking. In: Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, pp. 714–719 (2014)
Chakraborty, G., Pagolu, M., Garla, S.: Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS. SAS Institute Inc., Cary (2014)
ePOOLICE. The epoolice project (2015)
Europol. Eu organised crime threat assessment: Octa 2011. file no. 2530–274. Technical report, Europol, O2 Analysis & Knowledge, The Hague (2011)
Ganter, B., Wille, R., Analysis, F.C.: Formal Concept Analysis: Mathematical Foundations. Springer-Verlag, New York (1998)
Twitter Inc., Twitter search api (2015)
Laczko, F., Gramegna, M.A.: Developing better indicators of human trafficking. Brown J. World Aff. 10, 179 (2003)
Her Majesty’s Inspectorate of Constabulary. The rules of engagement: A review of the disorders, August 2011
Omand, D., Bartlett, J., Miller, C.: Introducing social media intelligence (SOCMINT). Intell. Natl. Secur. 27(6), 801–823 (2012)
Owoputi, O., O’Connor, B., Dyer, C., Gimpel, K., Schneider, N., Smith, N.A.: Improved part-of-speech tagging for online conversational text with word clusters. Association for Computational Linguistics (2013)
Perrin, A.: Social media usage: 2005–2015. Technical report, Pew Research Center (2015)
Ritter, A., Etzioni, O., Clark, S., et al.: Open domain event extraction from twitter. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1104–1112. ACM (2012)
CISC Strategic Criminal Analytical Services: Strategic early warning for criminal intelligence. Technical report, Criminal Intelligence Service Canada (CISC) (2007)
United Nations. United nations convention against transnational organized crime and the protocols thereto
UNODC. Anti-human trafficking manual for criminal justice practitioners (2009)
UNODC. Global report on trafficking in persons (2009)
Williams, P., Godson, R.: Anticipating organized and transnational crime. Crime Law Soc. Change 37(4), 311–355 (2002)
Acknowledgments
This project has received funding from European Union Seventh Framework Pro-gramme FP7/2007 - 2013 under grant agreement n FP7-SEC-2012-312651.
Legal and Ethical Disclaimer. No data that can or may be considered sensitive or personal has been handled as a result of the research undertaken. The authors do however acknowledge, despite being outside of the scope of the research present, that in practice the operational utility of such a system would be dependant on the use of data they may be considered personal and/or sensitive.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Andrews, S., Brewster, B., Day, T. (2016). Organised Crime and Social Media: Detecting and Corroborating Weak Signals of Human Trafficking Online. In: Haemmerlé, O., Stapleton, G., Faron Zucker, C. (eds) Graph-Based Representation and Reasoning. ICCS 2016. Lecture Notes in Computer Science(), vol 9717. Springer, Cham. https://doi.org/10.1007/978-3-319-40985-6_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-40985-6_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-40984-9
Online ISBN: 978-3-319-40985-6
eBook Packages: Computer ScienceComputer Science (R0)