Digital platforms generate unprecedented traces of human behaviour, offering new methodological approaches to understanding collective action, polarisation, and social dynamics. Through analysis of millions of digital traces across multiple studies, we demonstrate how online behaviours predict offline action: Brexit-related tribal discourse responds to real-world events, machine learning models achieve 80% accuracy in predicting real-world protest attendance from digital signals, and social validation through "likes" emerges as a key driver of mobilization. Extending this approach to conspiracy narratives reveals how digital traces illuminate psychological mechanisms of belief and community formation. Longitudinal analysis of YouTube conspiracy content demonstrates how narratives systematically address existential, epistemic, and social needs, while examination of alt-tech platforms shows how emotions of anger, contempt, and disgust correlate with violence-legitimating discourse, with significant differences between narratives associated with offline violence versus peaceful communities. This work establishes digital traces as both methodological innovation and theoretical lens, demonstrating that computational social science can illuminate fundamental questions about polarisation, mobilisation, and collective behaviour across contexts from electoral politics to conspiracy communities.

What does an executive at a South African Bank, a machine learning lead, and a CEO of an AI company have in common? They all will be on a panel talking about careers in Data Science, Machine Learning and Artificial Intelligence

Tutorial on Notebook workflows for reproducible data science.

A single person can be the source of billions of data points, whether these are generated from everyday internet use, healthcare records, wearable sensors or participation in experimental research. This vast amount of data can be used to make predictions about people and systems: what is the probability this person will develop diabetes in the next year? Will commit a crime? Will be a good employee? Is of a particular ethnicity? Predictions are simply represented by a number, produced by an algorithm. A single number in itself is not biased. How that number was generated, interpreted and subsequently used are all processes deeply susceptible to human bias and prejudices. This session will explore a philosophical perspective of data ethics and discuss practical steps to reducing statistical bias. There will be opportunity in the last section of the session for attendees to discuss and troubleshoot ethical questions from their own analyses in a ‘Data Clinic’.

In urban environments, social boundaries are the areas that emerge from processes of economic inequality and social segregation. These boundaries are important, as they serve both as areas of interaction and conflict. By applying geographical thinking to classic methods in data science, we can better understand where these boundaries emerge and how they delineate communities. In this talk, I’ll explain a bit about the basics of “boundary detection” in urban analytics. I’ll present a new method, the “geosilhouette,” that builds on previous methods of identifying the boundaries between clusters. And, finally, I’ll show how this can change our understanding of urban community.

The presentation will present the work of the Space Enabled Research Group at the MIT Media Lab. The mission of the Space Enabled Research Group is to advance justice in Earth’s complex systems using designs enabled by space. Our message is that six types of space technology are supporting societal needs, as defined by the United Nations Sustainable Development Goals. These six technologies include satellite earth observation, satellite communication, satellite positioning, microgravity research, technology transfer, and the infrastructure related to space research and education. While much good work has been done, barriers remain that limit the application of space technology as a tool for sustainable development. The Space Enabled Research Group works to increase the opportunities to apply space technology in support of the Sustainable Development Goals and to support space sustainability. Our research applies six methods, including design thinking, art, social science, complex systems, satellite engineering and data science. We pursue our work by collaborating with development leaders who represent multilateral organizations, national and local governments, non-profits and entrepreneurial firms to identify opportunities to apply space technology in their work. We strive to enable a more just future in which every community can easily and affordably apply space technology. The work toward our mission covers three themes: 1) Research to apply existing space technology to support the United Nations Sustainable Development Goals; 2) Research to design space systems that are accessible and sustainable; and 3) Research to study the relationship between technology design and justice. The presentation will give examples of research projects within each of these themes.