Collective attention in online social networks

Mass attention in online social media can be powerful. In politics, it can influence election campaigns or foster extremist ideologies; in business, it can boost product sales through viral advertising or damage brands by spreading bad news stories; in social discourse, it can highlight issues and change public perceptions. Yet the mechanisms by which online attention is gathered around a particular event or topic are not understood.

The complexity of networked online communication demands advanced mathematical and computational analyses. This project will build on recent methodological advances by the supervisors that allow measurement and forecasting of collective attention events in online social media. It will develop new mechanistic models of collective attention in social networks, to provide a theoretical basis for understanding online communication.
In recent work sponsored by a leading commercial data science company, we have developed a network-based methodology for analysis of collective attention events in social media. By representing the interactions of millions of social media users with digital content in the form of dynamic bipartite networks, we have shown that collective focus on a given topic can be accurately measured. Furthermore, machine learning methods applied to network timeseries have shown that future collective attention events can be predicted with some accuracy.

This PhD project will develop mechanistic models of the interaction of social media users with online content. Drawing on our existing methods, large archived datasets and ongoing research with our commercial sponsor, the student will refine the machine learning techniques used to derive the predictive signal from network timeseries. They will then draw on recent advances in the literature to develop process-based models of the "social physics" of large numbers of networked social media users interacting with content. They will then apply these models to improve predictions of imminent collective attention events, combining network analysis with natural language processing and machine learning.