Statistical models for social influence and contagion in dynamic, large-scale urban networks
Social influence and contagion occur in large-scale populations and have a crucial impact on a multitude of individual and societal outcomes, from political opinion to mental and physical health and from integration to inequality. The interpersonal networks connecting people in society affect how influence and contagion unfold, but the networks are subject to change over time. Understanding the joint evolution of networks and individual outcomes is one of the most exciting current challenges in social science research. To date, statistical models for network dynamics are sparse and only applicable to small groups, up to a few hundred actors. This limits researchers in their ability to analyse and explain large-scale societal processes, such as political polarization or the spread of depression in and across communities.
This project aims to develop and apply a novel extension to existing statistical models for dynamic networks, which are capable of separating and estimating the effects of social influence and contagion on individual and societal outcomes in large-scale social networks. In doing so, the methodology will account for the evolving network structure. To achieve this goal, the project builds on recent advances in dynamic network modelling (namely on Stochastic Actor-oriented Models), sampling in networks, and Bayesian approaches to network models.
Novel statistical approaches will be explored in the context of large empirical network datasets, such as:
• needle sharing and physical health among IV drug users in a major city,
• social support and mental health in town communities affected by a natural disaster,
• online interactions and engagement in food-sharing in smaller and larger cities.
Due to their scale, these datasets have so far been infeasible to analyse by available statistical models for dynamic networks. The project promises new insights into how social influence and contagion affect individuals’ attitudes, physical, and mental health, and into how these processes shape the studied communities. The approach and the results will, however, be transferable to other contexts, and may contribute to our understanding of key processes in society such as political polarization, social segregation or the reproduction of inequalities in health and well-being.
The project will be carried out jointly at the Mitchell Centre for Social Network Analysis at the University of Manchester, UK and the MelNet group at University of Melbourne, Australia. The main base of the project will be in Manchester. Both institutions have a track record of word-class research and teaching in social network analysis and they offer a vibrant and supportive international scientific environment for the PhD student who will carry out the project.
Johan Koskinen, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne
Dr Termeh Shafie, The University of Manchester