ABSTRACT. The wealth of data generated by our digital society, when combined with computational methods like agent-based modeling and natural language understanding, provides a new window to study human behavior at new scales and resolutions. This enables the analysis of complex social phenomena in which temporal dynamics and network structures require the use of large and detailed data. I will present an overview of complex social phenomena that have been analyzed through social media data, one of the most accessible and powerful data sources in our digital society. First, I will show how social media data can be used to analyze collective emotions and their long-term effects in terms of solidarity. Second, social media data can capture states of multidimensional polarization that can be explained by cognitive science models. Third, social media data can capture gender inequality across countries and illustrates the role of positive externalities, also known as network effects. And fourth, I will show how the presence of intelligent technologies in online platforms generate the phenomenon of complex privacy, by which the individual decision to share data with an online platform is affected by the decisions of others.

ABSTRACT. Network science has been very successful in investigations of a wide variety of applications from biology and the social sciences to physics, technology, and more. In many situations, it is already insightful to use a simple (and typically naive) representation as a simple, binary graph in which nodes are entities and unweighted edges encapsulate the interactions between those entities. This allows one to use the powerful methods and concepts for example from graph theory, and numerous advances have been made in this way. However, as network science has matured and (especially) as ever more complicated data has become available, it has become increasingly important to develop tools to analyse more complicated structures. For example, many systems that were typically initially studied as simple graphs are now often represented as time-dependent networks, networks with multiple types of connections, or interdependent networks. This has allowed deeper and more realistic analyses of complex networked systems, but it has simultaneously introduced mathematical constructions, jargon, and methodology that are specific to research in each type of system. The concept of « multilayer networks » was developed in order to unify the aforementioned disparate language (and disparate notation) and to bring together the different generalised network concepts that included layered graphical structures. In this tutorial talk, I will introduce multilayer networks and discuss how to study their structure. Generalisations of the clustering coefficient for multiplex networks and graph isomorphism for general multilayer networks are used as illustrative examples.