I am currently interested in the application of social network analysis to a whole host of interesting problems. In the TRIDEC project, we are looking at application of SNA to disaster data. One of my students, Maryam Fatemi is investigating the use of community detection algorithms and recommendation systems.

The image shown to the left depicts the co-present communities as detected by bluetooth of the Reality Mining dataset.

Social Network Analysis

Event detection, adaptive crawling, and sense making in social media is a fascinating area of research. Related to the TRIDEC project, we are currently analysing Twitter datasets (over 60 million tweets) collected during the London 2012 Olympics. This research covers a host of different topics including event detection, social event characterisation and modelling and adaptive data collection.

Social Media

We are developing an interactive app which allows audiences of large events like festivals which incorporates aspects like location based information, social media and movement information. Apart from offering our users a good time, we are also looking at characterising participants by their movements, co-presence, etc. 

Getting jiggy: Crowds, the audience and what makes them tick.

Can we generalise these behaviours to "event types"?

Can we correlate social behaviours to the clique crowd models?

Can we influence behaviours causing a) change of membership b) change of behaviour c) utilisation of space?

This page contains a small selection of stuff I have been working on lately with my students/project/RAs/colleagues:

Developed by Xinyue Wang, the adaptive crawler automatically updates search terms, to generate a better, more comprehensive set of search terms based upon correlating the traffic patterns of new key words against the original predefined words. We validated the Twitter crawler on the Olympic 2012, Glastonbury (UK) music festival 2013 events.


Capturing emergent information: getting the most of your Twitter Crawls.

An efficient, open-source, client-server system that supports both iOS and Android mobile devices. Developed by Kleomenis Katevas, SensingKit is capable of continuous sensing the device’s motion (Accelerometer, Gyroscope, Magnetometer), location

SensingKit: A Multi-Platform Mobile Sensing Framework for Large-Scale Experiments.

(GPS) and proximity to other smartphones (Bluetooth Smart / iBeacon). We believe that this platform will be beneficial to all researchers and developers who need to perform mobile sensing in their applications and experiments.


Image by MKHMarketing