Experimental Psychology Seminar - Sensing, Modelling and Understanding Human Behaviour from Social Media and Mobile Data
Speaker: Mirco Musolesi, UCL Geography
In the recent years, the emergence and widespread adoption of new technologies from social media to smartphones are rapidly changing the social sciences, since they allow researchers to analyse, study and model human behavior at a scale and at a granularity that were unthinkable just a few years ago. These developments can be seen as the emergence of a new data-driven and computation-based approach to social science research, usually referred to as "computational social science”.
In this talk I will discuss the work of my lab in a key area of this emerging discipline, namely the analysis and modelling of human behavioural patterns from mobile and sensor data. I will also give an overview of our work on mobile sensing for human behaviour modelling and prediction. I will present our ongoing projects in the area of mobile systems for mental health. In particular, I will show how mobile phones can be used to collect and analyse mobility patterns of individuals in order to quantitatively understand how mental health problems affect their daily routines and behaviour and how potential changes can be automatically detected. will also discuss our current work on the problem of modelling behavioural patterns from data extracted from large-scale online platforms and user-generated content at a scale.
Bio: Mirco Musolesi is a Reader (equivalent to an Associate Professor in the North-American system) in Data Science at the Department of Geography at University College London and a Turing Fellow at the Alan Turing Institute, the UK national institute for data science. At UCL he leads the Intelligent Social Systems Lab. He held research and teaching positions at Dartmouth, Cambridge, St Andrews and Birmingham. He is a computer scientist with a strong interest in sensing, modelling, understanding and predicting human behaviour and social dynamics in space and time, at different scales, using the "digital traces" we generate daily in our online and offline lives. He is interested in developing mathematical and computational models as well as implementing real-world systems based on them. This work has applications in a variety of domains, such as intelligent systems design, ubiquitous computing, digital health, security&privacy, and data science for social good. More details about his research profile can be found at: http://www.ucl.ac.uk/~ucfamus/