Modelling location specific behaviours on social media

GLOBAL RESEARCH INSTITUTES

  • Modelling location specific behaviours on social media

Modelling location specific behaviours on social media 

Principal Supervisor: Dr. Deepak Padmanabhan

Second Supervisor: Dr. Anna Jurek

+ Project Description

Social media is one of the most significant information exchange technology of the 21st century. Social media channels, such as Twitter and Facebook, provide very convenient and efficient ways of communicating and sharing information publically. Making sense of the high-volume data generated through social media can help to identify various types of real-life events as they happen or shortly after (e.g. street demonstrations, traffics, social events etc.). Consequently, social media are rapidly becoming a source of information about emerging situations or public events for city government, security services, journalists or even individuals looking for events to attend in their local area. 

The PhD project aims to develop scalable algorithms and models for collecting, analysing and visualising massive amount of social media data in a real-time environment. This will involve undertaking research in the area of information retrieval, machine learning and natural language processing.

The PhD project aims to develop scalable algorithms and models for collecting, analysing and visualising massive amount of social media data in a real-time environment. This will involve undertaking research in the area of information retrieval, machine learning and natural language processing.

+ Objectives

The main focus of the project will be to develop novel methods for social media analysis, with the following specific objectives:

-        Review and understand the current state-of-the-art research in the area of modelling and detecting behaviours on social media

-        Develop methods for modelling location specific behaviours on social media

-        Develop mechanism for local events detection through identification of uncommon behaviours on social media

Evaluate the proposed methodologies using historical and real-time data

+ Academic Requirements

A minimum 2.1 honours degree or equivalent in Computer Science, Computer Engineering, Mathematics, Statistics or other relevant degree is required. Applications that show relevant research experience (e.g. at Masters level) will be preferred. English Language Testing System (IELTS) 6.0 with a minimum of 5.5 in all four elements of the test or equivalent.

+ How to Apply

Applicants should apply electronically through the Queen’s online application portal at: https://dap.qub.ac.uk/portal/

+ Contact Details

Supervisor Name: Dr. Deepak Padmanbhan 
Email:

D.Padmanabhan@qub.ac.uk

Tel:

+44 (0)28 9097 4874