We focus on studying prediction problems from event sequences. The latter are ubiquitous in several scenarios involving human activities, including especially information diffusion in social media.

The scope of the MP is to investigate methods for learning deep probabilistic models based on latent representations that can explain and predict event evolution within social media. Latent variables are particularly promising in situations where the level of uncertainty is high, due to their capabilities in modeling the hidden causal relationships that characterize data and ultimately guarantee robustness and trustability in decisions. In addition, probabilistic models can efficiently support simulation, data generation, and different forms of collaborative human-machine reasoning.

There are several reasons why this problem is challenging. We plan to study these challenges and provide an overview of the current advances, as well as a repository of available techniques and datasets that can be exploited for research and study.