Characterizing Information Spreading in Online Social Networks

TL;DR

This paper develops a comprehensive model to analyze how information spreads dynamically in online social networks, considering network topology, user behavior, and content popularity, validated with real-world data.

Contribution

It introduces a novel Interactive Markov Chains and mean-field theory-based model that captures complex spreading dynamics and predicts future trends in OSNs.

Findings

Model accurately characterizes video sharing dynamics in Renren.

Model predicts future spreading tendencies effectively.

Impacts of network topology on information diffusion are explicitly revealed.

Abstract

Online social networks (OSNs) are changing the way in which the information spreads throughout the Internet. A deep understanding of the information spreading in OSNs leads to both social and commercial benefits. In this paper, we characterize the dynamic of information spreading (e.g., how fast and widely the information spreads against time) in OSNs by developing a general and accurate model based on the Interactive Markov Chains (IMCs) and mean-field theory. This model explicitly reveals the impacts of the network topology on information spreading in OSNs. Further, we extend our model to feature the time-varying user behaviors and the ever-changing information popularity. The complicated dynamic patterns of information spreading are captured by our model using six key parameters. Extensive tests based on Renren's dataset validate the accuracy of our model, which demonstrate that it can characterize the dynamic patterns of video sharing in Renren precisely and predict future spreading tendency successfully.