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Award details

Using Complex Networks and Machine Learning to Detect Situational Inauthentic Behaviour Online. EMPS College Home fees Studentship, PhD in Computer Science. Ref: 4307

About the award

Supervisors

Lead Supervisor:

Dr Diogo PachecoStreatham Campus, University of Exeter

Co-supervisor:

Dr Marcos Oliveira, Streatham Campus, University of Exeter

Location:

Department of Computer Science, Streatham Campus, Devon, University of Exeter

The University of Exeter’s College of Engineering, Mathematics and Physical Sciences  is inviting applications for a fully-funded PhD studentship to commence in January 2022 or as soon as possible thereafter. The studentship will cover Home tuition fees plus an annual tax-free stipend of at least £15,609 for 3.5 years full-time, or pro rata for part-time study. 

This College studentship is open to UK and Irish nationals, who if successful in their application will receive a full studentship including payment of university tuition fees at the home fees rate.

Project Description:

Online social media have revolutionised how people consume information, and form and share their opinions. In a perfect world, this easy-cheap access to information would boost the global economy and the democratic processes. However, there is growing evidence showing the destructive power of malicious actors exploiting platforms' vulnerabilities and human's cognitive biases to nurture the current infodemic crisis.

The arms race between social media platforms and inauthentic accounts has already a long list of cycles. As usual, as technology develops increasing the capacity of detection, evaders also implement counter measurements creating more sophisticated deceivable agents. The literature already offers methodologies to detect different facets of this problem. For instance, (i) automated vs. organic, (ii) benign vs. malicious, (iii) coordinated vs. uncoordinated, and (iv) true vs. fake. These methodologies already empower us (the general public and the platforms) to combat fringe actors exploiting our vulnerabilities online. Unfortunately, none of these technologies is perfect, and bad actors often employ a hybrid approach mixing most of the above-mentioned facets.

Most of this technology relies on large amounts of historical data not only to build models for detection but also to perform “live” detection. Moreover, they tend to focus on labelling accounts rather than actions. For instance, Botometer, one of the most famous bot detection tools uses up to the last 200 tweets from an account in other to assess its “botiness”. Even though bot scores change over time, there is no association between an action or time leading to inauthentic behaviour. The project aims to evolve online inauthentic behaviour detection similarly as criminology has evolved from Lombroso's classical criminology school of thought which focused on traits of criminals to environmental criminology.  Can we develop a methodology to detect situational misbehaviour online?

The applicant should be willing to work with data science models, machine learning, network science, python programming language (desirable), statistical modelling and have a strong enough background in computer science and maths to enable the applicant to carry independent research.

Essential Criteria:

1. Strong data analysis skills demonstrated by academic excellence or practical experiences
2. Strong mathematical or statistical background, with the ability to conduct modelling
3. Programming skills, with a very good knowledge of Python or R

Desirable Criteria:

1. Experience of or ability to work with a large, varied dataset
2. Strong team player, communicator, and problem solver
3. Experience in generating, processing and modelling synthetic data

Entry requirements

This studentship is open to UK and Irish nationals, who if successful in their application will receive a full studentship including payment of university tuition fees at the home fees rate.

Applicants for this studentship must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of science or technology.

If English is not your first language you will need to have achieved at least 6.0 in IELTS and no less than 6.0 in any section by the start of the project. 

Alternative tests may be acceptable (see http://www.exeter.ac.uk/postgraduate/apply/english/).

How to apply

In the application process you will be asked to upload several documents.  Please note our preferred format is PDF, each file named with your surname and the name of the document, eg. “Smith – CV.pdf”, “Smith – Cover Letter.pdf”, “Smith – Transcript.pdf”.

• CV
• Letter of application (outlining your academic interests, prior project work experience and reasons for
  wishing to undertake the project).
• Transcript(s) giving full details of subjects studied and grades/marks obtained (this should be an
   interim transcript if you are still studying)
• Two references from referees familiar with your academic work. If your referees prefer, they can email
   the reference direct to pgrenquiries@exeter.ac.uk quoting the studentship reference number.
• If you are not a national of a majority English-speaking country you will need to submit evidence of your proficiency in English.  Please see the entry requirements information above.

The closing date for applications is midnight on 10th January 2022.  Interviews will be held online on the week commencing 7th February 2022.

If you have any general enquiries about the application process please email pgrenquiries@exeter.ac.uk

Project-specific queries should be directed to the main supervisor at d.pacheco@exeter.ac.uk

Summary

Application deadline:10th January 2022
Value:Home tuition fees plus an annual tax-free stipend of at least £15,609 for 3.5 years full-time, or pro rata for part-time study.
Duration of award:per year
Contact: PGR Admissions Office pgrenquiries@exeter.ac.uk