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Engineering and Physical Sciences Research Council Featured PhD Programmes
University of Sheffield Featured PhD Programmes
FindA University Ltd Featured PhD Programmes
University of Sheffield Featured PhD Programmes

Automatic detection of corporate third-parties operating online using digital methods

This project is no longer listed on and may not be available.

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  • Full or part time
    Dr Karen Evans-Reeves
    Dr Ayush Joshi
    Dr Tom Fincham Haines
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

The tobacco industry is the biggest barrier to the implementation of effective tobacco control policies. Given their long history of deceit, tobacco companies are not considered trustworthy by many, particularly in countries which have implemented strong tobacco control regulations. Consequently, tobacco companies utilise the third-party technique. Tobacco companies provide funds to third-party groups who then promote tobacco company arguments and support the tobacco industry position against tobacco control regulations. In many cases third parties make industry arguments in the public domain without disclosing their funding.

As well as in person at meetings and events, a large network of tobacco industry third-party organisations and individuals promote industry arguments online on social media, in blogs, news and trade publications.

The STOP project aims to Stop Tobacco Organisations and Products around the world with a focus on Low and Middle Income Countries (LMICs) In many LMICs tobacco control regulation is in its infancy and implementation of the WHO Framework Convention on Tobacco Control (FCTC) including Article 5.3, is weak. It is likely, that as these countries make moves towards implementing effective tobacco control policies, they will experience opposition from the tobacco industry and third-party organisations and individuals.

A PhD investigation will make a significant contribution to the development of algorithms that can identify a tobacco industry third-party organisation which will enable tobacco industry actions to be identified and exposed in LMIC markets. The precise research questions will be determined on the appointment of the best candidate but could include:
• Creating a taxonomy of the common features of known tobacco industry third parties by:
- Analysing documents and media articles published by third-party organisations
- Exploring third-party networks using social media sites;
- Examining official documents from parliamentary websites/public consultations;
• Developing and testing algorithms that identify third-party organisations from newspaper articles, Social Media accounts or lobbying documents.

Digital methods skills and a background in data science would be essential for this role. Knowledge of Python/R or other scripting languages for data scraping and analysis purposes and previous experience of machine learning would also be important.

We anticipate this research would utilise the knowledge already gathered by the Tobacco Control Research Group to help build a classifier to identify third-party organisations operating in LMICs. The focus would be on a small number of LMICs to start with, in the hope that this could be expanded to other LMICs later on.

It will be an interdisciplinary project combining skills in two or more of the following areas, depending on the particular focus: data science, computing science, public health, political science.

This project is associated with the UKRI CDT in Accountable, Responsible and Transparent AI (ART-AI), which is looking for its first cohort of at least 10 students to start in September 2019. Students will be fully funded for 4 years (stipend, UK/EU tuition fees and research support budget). Further details can be found here:

Applicants are likely to have a good 2(i) in a related social science subject, such as sociology, politics and/or geography. A relevant Master’s degree would be highly desirable.

You should have good quantitative skills, with an ‘A’ level in maths, to enable you to take full advantage of the interdisciplinary training. Prior knowledge and work on computer-based decision-making would be an advantage, but is not essential. Students will receive training tailored to their background and project. This is likely to include: programming, digital data and advanced quantitative methods (social statistics), AI ethics, AI and government.

Since we anticipate this research would focus on LMICs, the candidate may be expected to spend time in that country/ those countries to gather an understanding of the policy making/lobbying process in those countries.

Informal enquiries about the project should be directed to the STOP project team on [Email Address Removed].

Enquiries about the application process should be sent to [Email Address Removed].

Formal applications should be made via the University of Bath’s online application form for the Integrated PhD in Accountable, Responsible & Transparent Artificial Intelligence (full-time):

Course start date: 30 September 2019.
Induction week: week commencing 23 September 2019.

1. Applications may close earlier than the advertised deadline if a suitable candidate is found; therefore, early application is strongly recommended.
2. It is anticipated that interviews will take place at the end of July, if possible.

Funding Notes

UK and EU candidates who apply for this project will be considered for a studentship funded by the University of Bath and the STOP project. Funding will cover UK/EU tuition fees as well as providing maintenance at the UKRI doctoral stipend rate (£15,009 per annum for 2019/20) and a training support fee of £1,000 per annum for a period of up to 4 years.

We also welcome all-year-round applications from self-funded candidates and candidates who can source their own funding.

How good is research at University of Bath in Computer Science and Informatics?

FTE Category A staff submitted: 24.00

Research output data provided by the Research Excellence Framework (REF)

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