  In this paper, we introduce a methodology that allows to model behavioral
trajectories of users in online social media. First, we illustrate how to
leverage the probabilistic framework provided by Hidden Markov Models (HMMs) to
represent users by embedding the temporal sequences of actions they performed
online. We then derive a model-based distance between trained HMMs, and we use
spectral clustering to find homogeneous clusters of users showing similar
behavioral trajectories. To provide platform-agnostic results, we apply the
proposed approach to two different online social media --- i.e. Facebook and
YouTube. We conclude discussing merits and limitations of our approach as well
as future and promising research directions.
