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.
Highlights
The vociferous proliferation of the Internet, and more recently social media, into society and the everyday lives of its citizens has, over the last fifteen or so years, resulted in a sea-change in the behaviours and perceptions we have in relation to the information that is shared freely online [1]
Evaluation difficult to evaluate in an operational sense it is possible to say something about the quality of the results in terms of the accuracy of the weak signals identified
Conclusion and further work In this paper we have demonstrated how formal concept analysis (FCA) can be used in combination with map based visualisation, data extraction and natural language processing (NLP) techniques to extract and classify data to detect, through social media, the presence of corroborated organised crime threats
Summary
The vociferous proliferation of the Internet, and more recently social media, into society and the everyday lives of its citizens has, over the last fifteen or so years, resulted in a sea-change in the behaviours and perceptions we have in relation to the information that is shared freely online [1]. To provide one such approach to enable this, the tools described here facilitate the identification, extraction, processing, analysis and presentation of data from open sources, such as social media, that can reveal insight into the emergence and presence of crime both in an operational sense; by identifying specific phenomena that are linked to discrete types of crime, and from a strategic perspective in the identification and visualisation of strategic trends through the corroboration of different crime indicators.
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