Fake News and Bots: Using Machine Learning and Network Analysis to Diagnose Novel Pathologies of Online News Media - Computer Science - EPSRC DTP funded PhD Studentship Ref: 2942

About the award

This project is one of a number funded by the Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Partnership to commence in September 2018. This project is in direct competition with others for funding; the projects which receive the best applicants will be awarded the funding.

The studentships will provide funding for a stipend which is currently £14,553 per annum for 2017-2018. It will provide research costs and UK/EU tuition fees at Research Council UK rates for 42 months (3.5 years) for full-time students, pro rata for part-time students.

Please note that of the total number of projects within the competition, up to 15 studentships will be filled.

Dr Hywel Williams

Streatham Campus, Exeter 

Project Description
Most people access news through online platforms and social media is now the primary news source for a large and increasing proportion of the UK population. The rapid growth in use of online news sources, rather than traditional sources such as TV and print media, has created a number of novel challenges to public communication.

So-called “fake news” is a phenomenon whereby false news stories are created and propagated online, often by small-scale digital media platforms which are not subject to effective regulation and do not subscribe to normal standards of professional journalism. Fake news stories can be hard to spot; they are typically designed to appear plausible, commonly mimic the appearance of articles from reputable sources, and are disseminated through the same channels (e.g. social media) as “real” news. Fake news is argued to have large negative effects on public information and to disrupt political processes, including elections. 

Meanwhile, “bots” are automated social media accounts which act within online social networks, exchange messages with other users, and often build up large networks of friends/followers. Bots can be hard to distinguish; as with fake news, bots are designed to mimic human activity and appear plausible. Bots can play a number of roles in online communication. For example, bots can spread a particular kind of content, promote a particular political view, or support/attack particular individuals. The number of bots is hard to estimate because robust measures for their detection are not available. Bots and detection measures co-evolve in an ongoing “arms race”.

This PhD project will develop computational methods to detect and monitor fake news and bot activity in online news and social media. The tools created will be applied to understand the prevalence and importance of fake news and bots in the digital media ecosystem, especially as they relate to public debate about political issues and election campaigns. Effective techniques are likely to combine complex network analysis, machine learning and text mining. The raw data analysed will be social media content, web pages, and relational data describing networks of interaction between news sources and social media user accounts. 

This project will require a strong mathematical and computational background. The large majority of the research will focus on development of computational tools to detect, track and characterise fake news sources and automated bot accounts. Some understanding of social processes and online behaviours is necessary to help position the research within the wider context of online communications. Therefore this project also requires a willingness to engage with interdisciplinary research, for example, relevant computational work in quantitative social sciences, communications science and political science.

Entry Requirements
he project will suit a motivated student with a strong quantitative background (e.g. computer science, mathematics, physics) and an interest in how the Web is changing our society.  

The majority of the studentships are available for applicants who are ordinarily resident in the UK and are classed as UK/EU for tuition fee purposes.  If you have not resided in the UK for at least 3 years prior to the start of the studentship, you are not eligible for a maintenance allowance so you would need an alternative source of funding for living costs. To be eligible for fees-only funding you must be ordinarily resident in a member state of the EU.  For information on EPSRC residency criteria click here.

Applicants who are classed as International for tuition fee purposes are NOT eligible for funding. International students interested in studying at the University of Exeter should search our funding database for alternative options.


Application deadline:10th January 2018
Value:3.5 year studentship: UK/EU tuition fees and an annual maintenance allowance at current Research Council rate. Current rate of £14,553 per year.
Duration of award:per year
Contact: Doctoral Collegepgrenquiries@exeter.ac.uk

How to apply

You will be required to upload the following documents:
•       CV
•       Letter of application outlining your academic interests, prior research 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.
•       If you are not a national of a majority English-speaking country you will need to submit evidence of your current
        proficiency in English.  For further details of the University’s English language requirements please see

The closing date for applications is midnight (GMT) on Wednesday 10 January 2018.  Interviews will be held at the University of Exeter in late February 2018.

If you have any general enquiries about the application process please email: pgrenquiries@exeter.ac.uk.
Project-specific queries should be directed to the supervisor.

During the application process, the University may need to make certain disclosures of your personal data to third parties to be able to administer your application, carry out interviews and select candidates.  These are not limited to, but may include disclosures to:

• the selection panel and/or management board or equivalent of the relevant programme, which is likely to include staff from one or more other HEIs;
• administrative staff at one or more other HEIs participating in the relevant programme.

Such disclosures will always be kept to the minimum amount of personal data required for the specific purpose. Your sensitive personal data (relating to disability and race/ethnicity) will not be disclosed without your explicit consent.