PhD Study : Data Analytic Technologies to Combat Human Trafficking

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Summary

As highlighted by the recent deaths of 39 Vietnamese citizens whilst being trafficked into the UK, human trafficking is a growing transnational crime that can lead to the most serious of consequences for its victims. Human trafficking often involves the movement of persons through several transit countries, orchestrated by organized crime groups (OCGs). In the eventual destination country, victims may be exploited again by becoming further victims of sex trafficking. Most commonly, individuals became victims of human trafficking by word-of-mouth in their home country. In the recruitment and trafficking process, OCGs can exploit the secrecy and anonymity that is available through a range of online and digital technologies.

Particularly in the world of sex trafficking, the Internet presents ever-new opportunities to exploit increasingly more victims: OCG networks can operate remotely to “advertise” sex trafficking victims anywhere in the world. Use by OCGs of online forums, message boards and online classified advertisements provide opportunities for law enforcement agencies to identify possible human trafficking activity and the perpetrators within OCGs.

Due to the scale of the problem, the ability of OCGs to operate remotely, and the difficulties associated with victims of trafficking giving evidence, automated analysis of online activity is a critical weapon in the fight against human trafficking. The Modern Slavery Evidence Review (Home Office, 2013) identified that law enforcement agencies should move away from the perceived necessity of victim testimony and focus on other forms of evidence. As such, being able to build a digitally strong and forensically sound case to enable victimless prosecutions is vital. The online activities of OCGs in their trafficking operations results in digital footprints scattered across various web platforms; it is this information that can be used both for successful prosecutions and to better understand the “business models” that OCGs adopt and the ways in which they adapt to new technologies.

This project will develop software tools that can enable law enforcement agencies to analyse images, videos and text automatically on a large scale from the vast network of websites that advertise adult services. The tools will incorporate novel machine learning and data analytic methods to identify both geographical and temporal patterns and links between website postings.

Such website content and data are available openly on a massive scale on the Internet. Machine learning methods combined with image, video and text analytics will be developed to automatically identify similarities and patterns in the content and style of advertisements, which can reveal that the advertisements are being posted by OCGs. With little or no manual intervention large-scale information can thus be extracted rapidly in terms of clustering criminal activities, mapping where victims are being trafficked (often frequently and in groups), and locating and ultimately identifying the perpetrators. The supervisory team combines expertise in image, video and content analytics and machine learning with expertise in criminology, with particular research experience in combatting human trafficking and exploitation.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

  • Sound understanding of subject area as evidenced by a comprehensive research proposal

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

  • Publications record appropriate to career stage
  • A comprehensive and articulate personal statement
  • Applicants will be shortlisted if they have an average of 75% or greater in a first (honours) degree (or a GPA of 8.75/10). For applicants with a first degree average in the range of 70% to 74% (GPA 3.3): If they are undertaking an Masters, then the average of their first degree marks and their Masters marks will be used for shortlisting.

Funding and eligibility

The University offers the following levels of support:

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

  • Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
  • Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
  • Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
  • Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living

The Doctoral College at Ulster University

Key dates

Submission deadline
Friday 7 February 2020
12:00AM

Interview Date
Late March 2020

Preferred student start date
September 2020

Applying

Apply Online  

Contact supervisor

Professor Bryan Scotney

Other supervisors