## 1 Introduction  
Online social networks (OSNs) have greatly transformed Internet ecosystems and penetrated into every aspect of people's daily lives. These OSNs, such as Weibo, Twitter, and Facebook, have shown impressive values in connecting massive numbers of users worldwide. Users can implement a series of online social activities on OSNs, including posting content, building friendships, and commenting. Based on these characteristics of OSNs, an extensive and real-time form of information diffusion (the process of information flowing from one node to another in the network [1]) featuring new ideas, advertisements, and breaking news can successfully come into reality. The OSNs provide fundamental access and platform for various types of information, opinions, rumors, and so on [2]. In 2020, since the outbreak of COVID-19, the rapid dissemination on the Internet has also caused panic among the people and influenced rising prices. People's livelihood is reassured only after the official information was released. This incident fully proves that OSNs have played important roles in our society; thus, it is necessary to conduct in-depth research in this field. Additionally, a more comprehensive understanding of the information dissemination process on OSNs is also needed so as to add more valuable insights into the control of online public opinion. In previous studies, OSNs are generally represented by graphs, while the users are represented by nodes, with the relationships between users represented by edges. On the one hand, most research studies show the results of diffusion from a macro level, which focuses on the number of nodes in different states at each moment. On the other hand, some studies may analyze the process of information transmission between nodes from a micro level. However, we found that some information diffusion processes on OSNs could have a long-lasting effect, such as topics of people's livelihood and education, while other processes may be short-lived, for example, entertainment news. These two processes may seem to have the same spreading scale, but in nature they are subject to different diffusion processes. Therefore, it would be difficult to distinguish these processes from the previous models. The major deficiency of the previous information diffusion models lies in the weakness in capturing human social behaviors on OSNs. An efficient model should be able to reflect the true diffusion of information and involve important aspects of the process so as to better understand this phenomenon. As reaching a consensus that the diffusion model plays a significant role in information propagation, we propose a novel one, the Information Diffusion Model based on Explosion Shock Wave Theory, (the IDBESW model), taking the social behaviors of individuals and the value of information within the diffusion process. Inspired by the explosion shock wave, the individuals will be affected at different distances from the explosion center, with the influence gradually declining with time. The value of information is equivalence to the power of the bomb. It is worth mentioning that we establish rules of information transmission between two individuals, and the advantages of our method are four-fold. First, it can simulate information diffusion on OSNs, considering the differences between individuals and individual social behaviors, with individual background knowledge and forgetting factors included. Second, it holds the view that the attractiveness of information to individuals is related to the value of information. Third, it recognizes the role of community in the diffusion process. The community with a higher level of trust among individuals may provide a more convenient platform for the spread of information. Finally, our model is applicable to different types of real online social network datasets. A number of experiments with different settings and specifications were carried out to examine the advantages of the model, which proves to be quite promising. The remainder of this paper is organized as follows.