Use of technology in human trafficking networks and sexual exploitation: A cross-sectional multi-country study

Sarkar, Siddhartha

doi:10.1080/21931674.2014.991184

Cited by 28 publications

(16 citation statements)

References 22 publications

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“…In [18], it is revealed that attackers use sophisticated software and techniques such as encrypted communication, or strongly protected online servers, to avoid tracking and remain an unknown status. Some authors [19], recommend analyzing: inconsistency of the age, variance in the alias, frequency of content, shared management, race, nationality, and third-party publications, in order to detect anomalies in the profiles of alleged followers which are online attackers.…”

Section: Related Workmentioning

confidence: 99%

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Detection of Possible Illicit Messages Using Natural Language Processing and Computer Vision on Twitter and Linked Websites

et al. 2020

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Human trafficking is a global problem that strips away the dignity of millions of victims. Currently, social networks are used to spread this crime through the online environment by using covert messages that serve to promote these illegal services. In this context, since law enforcement resources are limited, it is vital to automatically detect messages that may be related to this crime and could also serve as clues. In this paper, we identify Twitter messages that could promote these illegal services and exploit minors by using natural language processing. The images and the URLs found in suspicious messages were processed and classified by gender and age group, so it is possible to detect photographs of people under 14 years of age. The method that we used is as follows. First, tweets with hashtags related to minors are mined in real-time. These tweets are preprocessed to eliminate noise and misspelled words, and then the tweets are classified as suspicious or not. Moreover, geometric features of the face and torso are selected using Haar models. By applying Support Vector Machine (SVM) and Convolutional Neural Network (CNN), we are able to recognize gender and age group, taking into account torso information and its proportional relationship with the head, or even when the face details are blurred. As a result, using the SVM model with only torso features has a higher performance than CNN.INDEX TERMS CNN, features detection, image classification, natural language processing, SVM.

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Section: Related Workmentioning

confidence: 99%

“…Figure 1 shows the whole process from the tweet searching related to human traffic [17] or slavery [21] of people; download and processing of this information [18], [22]; until the extraction of characteristics and their classification. The main objective of this phase is to obtain a blacklist of suspicious websites related to tweets.…”

Section: System Proposalmentioning

confidence: 99%

Detection of Possible Illicit Messages Using Natural Language Processing and Computer Vision on Twitter and Linked Websites

et al. 2020

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“…Indeed, many virtual convergence settings are used by the actors in this market to either recruit victims or sell sexual services. In addition, the Internet is also being used to communicate with clients, to gather information on available services, and even to provide reviews of escort services by the clients (Kunze 2010;Sarkar 2015).…”

Section: Trafficking In Human Beings: the Case Of Sexual Exploitationmentioning

confidence: 99%

Internet traces and the analysis of online illicit markets

Rossy¹,

Décary‐Hétu²

2017

The Routledge International Handbook of Forensic Intelligence and Criminology

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“…The two, however, should not be confused as the different goals and processes of each activity can be neatly distinguished, certainly in theory, but also, typically, in actuality (Salt and Hogarth, 2000). Due to the ascendancy of human trafficking as an issue, more is known about the role and use of ICT in the human trafficking business, particularly sex trafficking, and in this context, the literature appears to grow at a more certain pace (Di Nicola, Cauduro and Falletta, 2013;Latonero, Berhane, Hernandez, Mohebi and Movius, 2011;Latonero et al, 2012;Sykiotou, 2007;Myria, 2017;Sarkar, 2015). Conversely, the knowledge base on the use of ICT in human smuggling has rarely gone beyond the rather generic observation that the Internet and mobile technologies are available to and are used by both smugglers and migrants.…”

Section: Introduction: Situating Ict In Human Smugglingmentioning

confidence: 99%

The digital routes of human smuggling? Evidence from the UK

Diba

Papanicolaou

Antonopoulos

2019

Crime Prev Community Saf

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There are justified concerns but little empirical evidence about the implications of the use of Information and Communication Technologies (ICT) in the business of human smuggling. The knowledge base on the use of ICT in human smuggling has rarely gone beyond the rather generic observation that the Internet and mobile technologies are available to and are used by both smugglers and migrants, and there is a concrete knowledge gap regarding the extent and the mode in which the use of ICT is integrated in the process of smuggling. In this paper, which is part of a wider research effort concerned with the role of the Internet in human smuggling in the European Union, we interrogate the outlook and implications of the use of contemporary mobile technology and of social media in the organisation and conduct of human smuggling to the United Kingdom (UK).

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Use of technology in human trafficking networks and sexual exploitation: A cross-sectional multi-country study

Cited by 28 publications

References 22 publications

Detection of Possible Illicit Messages Using Natural Language Processing and Computer Vision on Twitter and Linked Websites

Detection of Possible Illicit Messages Using Natural Language Processing and Computer Vision on Twitter and Linked Websites

Internet traces and the analysis of online illicit markets

The digital routes of human smuggling? Evidence from the UK

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