Silence behavior mining on online social networks

Keeping silence is a behavior that widely exists in human society and has been studied in social science for a long time. After a new event occurs, instead of expressing opinions towards it immediately, individuals may choose to remain silence. Similar to a real social network, in online social networks, after observing an interesting event from their friends, users may not decide whether to share it at once due to different reasons. In influence propagation process, we observe that there are three states regarding to one's reaction on an event: activated state (shared), inactivated state (not shared) and silent state (take longer than usual time to make decisions). Silent state is an intermediate status before turning into inactivated or activated state. In this paper, we provide a mathematical definition of “silence” based on the length of hesitating time before a user makes decisions. However, during the hesitation period, silent users behave exactly like those users who already entered the inactivated state. In order to differentiate them in this case, we develop an iterative algorithm, Similarity Interest (SI) model, to identify possible silent users by quantifying the interest of users toward the event. Furthermore, comparing to real social networks, we reveal different behavior of silent users in online social networks and use the Transient Influence Principle to explain it. At last, based on experimental results, we design a new model (Diffusion with Silence (DS) model) incorporating Similarity Interest model and two widely used diffusion models (Independent Cascade (IC) model and Linear Threshold (LT) model), in order to capture the silence behavior. Our experiment shows that DS model can more accurately depict the process of information propagation than IC model and LT model do.