Network theory and computational social science allow us to understand collective human behaviour by analysing data and making models of the digital interactions we have online. The closing gap between off- and online activities lets us perform this task better than ever, bringing both knowledge of the large-scale structure of society, and challenges in predicting the future behaviour of individuals. Here are some of my research lines in this amazing field
We use large-scale data from online social platforms to study and predict the temporal evolution of social contagion
processes and cascading behaviour related to the use of innovations and new digital markets.
By considering data features like weighted or multiplex social interactions, product competition,
and temporal social networks, we aim at increasing the explanatory and predictability power of social contagion models
We validate opinion-formation models with data to emulate the
temporal evolution of conflicts between editors in Wikipedia. We find three typical scenarios for the way editors disagree while writing articles: short conflicts, plateaus of consensus among fights, and uninterrupted controversy. We wish not only to identify the ways by which people manage to collaborate despite their differences, but to propose efficient measures of prevention and control to minimise the harmful effects of social conflict
Opinions synthesize our perceptions and the knowledge we have of the external world, other people, and ourselves. To better understand the impact opinions have on social interactions and decision-making, we perform small controlled experiments and analyse idealized models of opinion formation, deception, and network change, letting us gauge how opinions influence the structure and dynamics of society