In the digital ecosystem, news and its repercussions spread almost instantaneously. We aim to understand the spatio-temporal patterns that emerge from this flow of information. To do so, we analyze massive databases of social networks and media and develop mathematical models to interpret our collective behaviors from basic interactions between subjects.
We develop theoretical models with tools from statistical mechanics, nonlinear dynamics and complex network theory. Our methodology for the treatment of experimental data includes a battery of machine learning algorithms.