Sociability-based Influence Diffusion Probability Model to evaluate influence of BBS post

Abstract

Abstract A Bulletin Board System (BBS) yields user-generated posts, which has enjoyed fast spreading speed. Significant events are often revealed by a post. It may then spread widely, thereby producing large influence in some specific social circles and sometimes the whole society. Hence, evaluating post influence becomes important. It can help Web service providers locate quickly those influential posts, users or communities, place right advertisements, expand an event’s influence, and explode a hot topic’s discussions. Recently, BBS has grown to have some new features, e.g., sociability. The existing studies use an Influence Diffusion Model (IDM) and its expanded versions for the analysis of influence. However, they suffer from such drawbacks as identical treatment of every comment or reply, and complete ignorance of relationships among users, thereby leading to the inaccurate assessment of post influence. To overcome the limitations, inspired by our prior user model for user participation in virtual communities, we propose a behavioral model for user participation in a post and give a Sociability-based Influence Diffusion Probability Model (S-IDPM) by utilizing user relationship and reply-chains to measure the responses of different users and evaluate post influence. Experiments with real data collected from a popular BBS. Our results show that S-IDPM outperforms IDM and its expanded version called Influence Diffusion Probability Model (IDPM). S-IDPM can be helpful to achieve better post influence diffusion evaluation than IDM and IDPM do.