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

TL;DR

This paper presents a novel methodology using Hidden Markov Models to analyze and cluster user behavioral trajectories across different social media platforms, enabling platform-agnostic insights.

Contribution

It introduces a new approach combining HMMs and spectral clustering to model and group user behaviors in social media, applicable across multiple platforms.

Findings

Effective clustering of user behaviors on Facebook and YouTube

Platform-agnostic modeling of behavioral trajectories

Discussion of approach's strengths and limitations

Abstract

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.