CrossSimON: A Novel Probabilistic Approach to Cross-Platform Online Social Network Simulation

Abstract

The increasing popularity and diversity of online social networks (OSNs) have attracted more and more people to participate in multiple OSNs. Learning users' behavior and information diffusion across platforms is critical for cyber threat detection, but it is still a challenge due to the surge of users participating in multiple social platforms. Existing research on profile matching requires user identity information to be available, which may not be realistic. Little prior research payed attention to mapping behavioral patterns across platforms. We designed and implemented an efficient two-level probabilistic approach called CrossSimON to mapping user-group behavior across platforms. CrossSimON considers the activity level and network position at both individual user level and group level to correlate activities across social platforms. To evaluate the effectiveness of CrossSimON in modeling social activity across platforms, we conducted experiments on three online social platforms: GitHub, Reddit and Twitter. Our experimental results show that CrossSimON outperformed the Benchmark in 3 out of 5 simulation metrics. CrossSimON achieved better performance in user activity prediction. The research provides new strategy for cross-platform online social network simulation, and new findings on simulating OSNs and predictive analytics for understanding online social network behavior.