## 2 Related Work
### Information Diffusion Models
[19] developed two models, namely the delay independent cascade (DIC) model and the delay linear threshold (DLT) model. Compared with the general models, a latent active state is added to these two models. [PERSON] et al. [20] proposed the opportunistic one-activate-one (OOAO) model and the deterministic one activation multiple (DOAM) model. In the OOAO model, everyone can only communicate with one another at the same time; the DOAM model has a broadcasting mechanism. [PERSON] et al. [21] developed the asynchronous IC (AsIC) and asynchronous LT (AsLT) models based on previous work. These learning algorithms can estimate the influence of nodes in a network based on relatively few information diffusion results. [PERSON] et al. [22] also proposed an asynchronous model, called the time-based AsIC model, to simulate the information diffusion. [PERSON] et al. [23] added the time and trust factors to the IC model, and proposed a trust-based latency-aware independent cascade (TLIC) model, which considered the trust factor in the IC model for the first time. [PERSON] et al. [24] extend the LT model to establish the diffusion-containment model, abbreviated as D-C model, by incorporating the realistic specialties and characteristics of the containment of competitive influence spread. Other studies [25; 26; 27; 28] have improved the classical models in specific aspects as well. Additionally, [PERSON] et al. [29] presented a topologically biased random walk for the diffusion process in social networks, displaying how topological properties affect the diffusion process. [PERSON] et al. [30] proposed a diffusion model based on the genetic algorithm, which assumes that individuals in the networks exchange information with others to improve the value of the information held by themselves. [PERSON] et al. (2018) proposed a particle swarm optimization model (PSO) to simulate the process of information diffusion. [PERSON] et al. (2018) recognized the fact that in the process of information diffusion, individuals can also receive information from the media in addition to receiving it from neighbors, which inspired his work on a multisource information perception model (MSIP) to simulate the process. The diffusion process of information on OSNs is closely related to the content, the users, and the structure of the OSNs. Information that matches the users' background is the most attractive. As the information is time sensitive, the discover time will also be an important factor that may influence the interest of users. However, the macroscopic models, which consider individuals having exactly the same characteristics and the same abilities to influence other users, may ignore the individual differences in the process. Microscopic models focus on the interaction between individuals, but overlook another important factor in the process: information. These micromodels assume that all information shares the same effect on nodes. Individuals may become interested in information when they are activated. However, due to the timeliness of information, individuals tend to receive the latest information, which could explain why the attractiveness of information may vary on account of the discover time.