Abstract

The focus of this work is on developing probabilistic models for temporal activity of users in social networks (e.g., posting and tweeting) by incorporating the social network influence as perceived by the user. Although prior work in this area has developed sophisticated models for user activity, these models either ignore social network influence completely or incorporate it in an implicit manner. We overcome the nontransparency of the network in the model at the individual scale by proposing a coupled hidden Markov model (HMM), where each user's activity evolves according to a Markov chain with a hidden state that is influenced by the collective activity of the friends of the user. We develop generalized Baum-Welch and Viterbi algorithms for parameter learning and state estimation for the proposed framework. We then validate the proposed model using a significant corpus of user activity on Twitter. Our numerical …

Date

January 1, 1970

Authors

Vasanthan Raghavan, Greg Ver Steeg, Aram Galstyan, Alexander G Tartakovsky

Journal

IEEE Transactions on Computational Social Systems

Volume

1

Issue

1

Pages

89-107

Publisher

IEEE