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In this paper, we introduce a methodology that allows to model behavioraltrajectories of users in online social media. First, we illustrate how toleverage the probabilistic framework provided by Hidden Markov Models (HMMs) torepresent users by embedding the temporal sequences of actions they performedonline. We then derive a model-based distance between trained HMMs, and we usespectral clustering to find homogeneous clusters of users showing similarbehavioral trajectories. To provide platform-agnostic results, we apply theproposed approach to two different online social media --- i.e. Facebook andYouTube. We conclude discussing merits and limitations of our approach as wellas future and promising research directions.

Towards the Modeling of Behavioral Trajectories of Users in Online Social Media