Scholarships & funding opportunities

Data Mining in Social Networks

  • Number of scholarships 1
  • Value £14,296 (pa for 3 years)
  • Opens 4 February 2016
  • Deadline 31 May 2016
  • Help with Tuition fees, Living costs
  • Duration 36 months


This PhD project requires a highly numerate graduate with skills and interests in computational science. Candidates should have at least a strong Honours degree or equivalent (a strong 2:1 Honours degree, or an undergraduate degree with 3.3 GPA in a 4.0 system), or preferably a Master’s degree in a quantitative discipline such as industrial engineering, operations research, mathematics or computer science (amongst others). Candidates who are not native English speakers will be required to provide evidence for their English skills (such as by IELTS or similar tests that are approved by UKVI, or a degree completed in an English speaking country)

Project Details

Social data from networks such as Twitter and Facebook can be represented as networks.  Similarly, various data about city living obtained through City Observatory housed in the Technology & Innovation Centre (TIC) Future Cities theme can be represented in a similar structure, using individuals as nodes of such a network and their connections as edges of the network. The proposed project will design efficient algorithms to mine opinions and sentiments in such networks, in particular in real-time and when big data is present. The problem of sentiment and opinion analysis involves studying the negative and positive expressions opined in social media on a specific subject matter. We will work on such algorithms that volume and velocity of
data accumulated in the context of social media is the most crucial design factor. This will also exploit some of the theoretical insights gained in an ongoing cross-faculty PhD project with Department of Mathematics & Statistics.

How to apply

Candidates are expected to submit a cover letter, a research proposal detailing their 3-year plan, CV, any university degree certificates and transcripts, English test results (if applicable), two recommendation letters (or contact details of two referees, if letters are not available to them), and any other supporting documents. 

Applications to: Alison Kerr (