2020 applicants
Postgraduate Research Opportunity

Modelling beliefs dynamics of social media users with machine learning methodologies


Title: SH19: Modelling beliefs dynamics of social media users with machine learning methodologies

Funding: A full Home/EU fee waiver (£4,630 in 2019-20) (further fee details) plus a doctoral stipend equivalent to the RCUK Minimum Doctoral Stipend (£15,009 in 2019-20).

Application deadline: 30 August 2019

Start date: October 2019

Duration: 3 years (full time)

Location: Colchester Campus

Based in: School of Computer Science and Electronic Engineering


This studentship is now closed to applicants. View our current opportunities.


Machine learning offers powerful tools to understand, predict and hopefully ameliorate the diffusion and effects of toxic content on social media that is recently having a high impact on our society.

This PhD scholarship is part of the research project “COURAGE: A Social Media Companion Safeguarding and Educating Students”, which is an international collaboration funded by VolkswagenStiftung (Volkswagen Foundation) as part of the Artificial Intelligence and the Society of the Future funding initiative. The project partners include the Universitat Pompeu Fabra (Spain), the Istituto per le Tecnologie Didattiche of the National Council of Research ITD-CNR (Italy), Hochschule Ruhr West (Germany) and the Rhine-Ruhr Institute for System Innovation (Germany).

The project aims to develop a Virtual Social Media Companion that educates and supports teenage school students facing the threats of social media such as discrimination and biases as well as hate speech, bullying, fake news and other toxic content. The companion will raise awareness of potential threats in social media among students without being intrusive. It will apply gamification strategies and educative information selection algorithms. 

The Essex team will be involved in developing Bayesian computational models of beliefs temporal dynamics of social media users to support governance and educational strategies. These models will also be applied to evaluate socially relevant variables, such as trust and inclusion. We will build on and implement state-of-the-art NLP & AI methods to provide measurements of sentiment, bias, hatefulness, veracity, polarisation, and sensationalism of social media content.

In addition, we will drive forward the state of the art in detecting hate speech and biased content. The companion will actively counteract this kind of content, balancing it with opposite perspectives and proposing specifically themed challenges adopting ideas used in games.

The project

The PhD studentship aims to address these challenges combining dynamic network modelling with  automated content analyses (textual or multimedia) using modern machine learning methods, such as Deep Learning and Hierarchical Bayesian Models.

The student may extract relevant content features, topics and events from online discussions to (a) predict short and long term responses of multiple users, (b) estimate the different effects of diverse information suggestion strategies in such context, and (c) define different interventions to improve model accuracy.

As such, we are particularly interested in PhD candidates that like to work on one or multiple of the following topics:

  • Modelling temporal dynamics of social media users beliefs
  • Model Based Reinforcement Learning algorithms for governance of social networks
  • Semantic analysis of unstructured textual or multimodal data, including sentiment analysis and detection of biased or fake content, violent language and cyberbullying.

The successful applicant will join the Essex COURAGE team — formed by Dr Dimitri Ognibene (PI), Professor Ansgar Scherp (Co-I), Dr Aline Villavicencio (Co-I), and Visiting Professor Udo Kruschwitz (Co-I).


A full Home/EU fee waiver (£4,630 in 2019-20 - further fee details).

A doctoral stipend equivalent to the Research Councils UK National Minimum Doctoral Stipend (£15,009 in 2019-20), plus £2,500 training bursary via Proficio funding, which may be used to cover the cost of advanced skills training including conference attendance and travel.


At a minimum, the successful applicant will have a good honours BSc degree (1st class or high 2:1, or equivalent) in computer science or related subjects. An MSc with Merit or Distinction is desirable (but not essential for students with a first class degree).

Strong analytical and mathematical skills are required, as well as good programming skills.

Essential skills

This role requires the following capabilities:

  • Qualified to graduate level with experience in computer science, artificial intelligence, data science. 
  • A creative and innovative approach to solving complex technical problems.
  • Good to very good programming skill in any modern language (e.g., Python, Java, …) is essential

Understanding and/or experience in/of:

  • Research problems and application of rigid scientific methodologies to problem solving Deep Learning methods, preferably in text analysis
  • Software engineering principles, technologies and frameworks in developing reusable components.
  • An inquiring mind with a preference for working within less defined boundaries, excellent creativity, self-motivation and good written and oral communication skills, and must be passionate about achieving excellent results.

How to apply

You can apply for this postgraduate research opportunity online.

Please include your CV, covering letter, personal statement, and transcripts of UG and Masters degrees in your application.

The University has moved to requiring only one reference for PhD applications and these can be received after a conditional offer has been made so the absence of these will not hold up the recruitment process.

Instruction to applicants

When you apply online you will be prompted to fill out several boxes in the form:

  • For "Course title" please put "PhD Computer Science"
  • For "Proposed research topic or area of research" please put the title of this studentship (SH19: Modelling beliefs dynamics of social media users with machine learning methodologies)
  • For "If you have contacted a potential supervisor..." please put Dr Dimitri Ognibene.

If you have any informal queries about this opportunity please email the supervisor, Dr Dimitri Ognibene (dimitri.ognibene@essex.ac.uk)