Towards the Modeling of Behavioral Trajectories of Users in Online Social Media
This work addresses the challenge of understanding user behavior patterns in online social media, which is incremental as it adapts existing HMM and clustering techniques to this domain.
The authors tackled the problem of modeling user behavioral trajectories in online social media by using Hidden Markov Models (HMMs) to embed temporal action sequences and clustering users based on similarity, applying the method to Facebook and YouTube for platform-agnostic results.
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.