EPSRC DTP PhD studentship: Predicting collective attention in online social networks

University of Exeter - College of Engineering, Mathematics and Physical Sciences

Main supervisor:  Dr. Hywel Williams
Co-supervisor:  Prof. Tim Lenton

Project Description:

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.

The student will be based at the Streatham (Exeter) campus of the University of Exeter. They will interact with the growing data science research community at U. Exeter, working with colleagues in mathematics, computer science and relevant quantitative social science disciplines, as appropriate.

Candidates should have a strong background in a quantitative discipline, with programming skills and experience of data analysis. They will learn a variety of techniques in network analysis, machine learning and natural language processing, with excellent opportunities for research publications and further employment in both academic and industrial settings.

The main original contribution of the project will be adapting link prediction models to predict network evolution. Link prediction normally uses node properties to predict which network edges will be added next; such methods form the basis of (e.g.) friend recommendations on social network platforms. Here the link prediction paradigm will be extended to consider both users and content, seeking to predict which topical content will be popular with different users based on network exposure and historical preferences. Models will be evaluated based on their ability to capture dynamics of collective attention over time.

The application domain of online social media is an important interdisciplinary research area with many opportunities for academic research employment. Predicting collective attention in social media has clear commercial value and the supervisors' collaborations with commercial partners will provide exposure to commercial data science.

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South West England