Towards the Modeling of Behavioral Trajectories of Users in Online Social Media
arXiv:1611.05778
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.
References in corpus (11)
- Science vs Conspiracy: collective narratives in the age of (mis)information
- Computational fact checking from knowledge networks
- Hoaxy: A Platform for Tracking Online Misinformation
- Users Polarization on Facebook and Youtube
- Emotional Dynamics in the Age of Misinformation
- Trend of Narratives in the Age of Misinformation
- Modeling Radicalization Phenomena in Heterogeneous Populations
- Emergence of metapopulations and echo chambers in mobile agents
- On the statistical properties of viral misinformation in online social media
- Debunking in a World of Tribes
- The Anatomy of Brexit Debate on Facebook