Periodic-Aware Intelligent Prediction Model for Information Diffusion in Social Networks

Due to the rapid development of information and communication technologies with several emerging computing paradigms, such as ubiquitous computing, social computing, and mobile computing, modeling of information diffusion becomes an increasingly significant issue in the big data era. In this study, we focus on a periodic-aware intelligent prediction method based on a comprehensive modeling of user and contagion features, which can be applied to support information diffusion across social networks in accordance with users' adoption behaviors. In particular, the Dynamically Socialized User Networking (DSUN) model and sentiment-Latent Dirichlet Allocation (LDA) topic model, which consider a series of social factors, including user interests and social roles, semantic topics and sentiment polarities, are constructed and integrated together to facilitate the information diffusion process. A periodic-aware preception mechanism usingreinforcement learning with a newly designed reward rule based on topic distribution is then designed to detect and classify different periods into the so-called routine period and emergency period. Finally, a deep learning scheme based on multi-factor analysis is developed for adoption behavior prediction within the identified different periods. Experiments using the real-world data demonstrate the effectiveness and usefulness of our proposed model and method in heterogenous social network environments.