Paper Info
Title
Towards the Modeling of Behavioral Trajectories of Users in Online Social Media.
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.
Year
Venue
Field
2016
arXiv: Computers and Society
Spectral clustering,Social media,Embedding,Computer science,Homogeneous,Artificial intelligence,Hidden Markov model,Machine learning,Probabilistic framework
DocType
Volume
Citations 
Journal
abs/1611.05778
0
PageRank 
References 
Authors
0.34
0
1
Name
Order
Citations
PageRank
Alessandro Bessi11509.87