Economists and psychologists investigate associations between socioeconomic and psychological aspects of an individual’s life, for example in the area of health and wellbeing. The analysis is classically enriched in regression frameworks by accounting for several factors such as standard demographic, socioeconomic and behavioural characteristics of the individual and society in general. Alternatively, data-driven approaches may lead to unforeseen connections between the data, resulting in improved models, better able to predict reality.

• Data-driven Economic Forecasting
• Improving the Diagnosis of Gaming Disorder: An application of machine learning in Psychopathology
• Optimization of neurofeedback treatments for patients after stroke

Location-social data, generated by online social networks, provides opportunities to study the fundamental role of social and mobility networks in information diffusion, study user engagement in social media, and investigate socio-spatial patterns and divisions in cities and ageing societies. It can be expected that machine learnt models will be able to predict the rise and fall of social trends.

• Mining Instagram Data: Information Diffusion, Social Engagement and Urban Structure
• Using mobile and social media data for smart mobility services
• Predicting wellbeing in old age