In this paper, a new modeling and learning approach is presented which is based on two assumptions from the field of psychology: 1. The number of Tweets mainly depends on previous dynamics of the discussion, i.e. a state-space modeling approach is used for the first time. 2. Humans mainly react to emotional stimuli, i.e. Tweets are automatically characterized by their emotional content. Therefore, the emotions of conversations are extracted and used for system identification and parameter estimation of a state space model, which deals with events and its transitions.

The proposed approach is further evaluated with an example discussion about the Spanish corruption affair held on Twitter during summer 2013. The experimental results show a method to model and learn the evolution of social media discussions based on emotions.