## 4 Experiments and Discussion
### Result and Discussion  
In the first experiment to assess the performance of the IDBESW model, the impacts of \(\alpha\), \(W\), and \(f\) on information diffusion in the Facebook network are illustrated through changing one while fixing the other two factors. The values of \(\alpha\), \(W\), and \(f\) are selected from the corresponding normal distribution. The variance of the normal distribution is set to 0.1, with different mean values set according to the studied interval. The results of these experiments are shown in Figures 4 and 5, illuminating the impact of these factors on thepopularity of the information and the rate of infected individuals. Note that for avoiding randomness in the results, we performed 100 simulations. Overall, the results indicate that these factors have an important impact on the popularity of information and the rate of infected nodes. We found that \(\alpha\) has the greatest impact on the rate of infected nodes. The larger \(\alpha\) is, the greater the difference between the information and the individuals, and the fewer infected nodes there will be. Since the individual has the largest probability of sending information when he or she has just received it, when \(f\) is small, the increase in \(f\) has an obvious impact on the rate of infected nodes. When \(f\) exceeds 0.7, the change in \(f\) has less impact on the result. Information of different values, \(W\), spreads at the same speed, but information with a high value of \(W\) lasts for a long time, and the rate of infected nodes increases accordingly. From Figure 5, we can observe that \(W\) has the greatest impact on the popularity of information. The higher the popularity of the information, the higher the activity of nodes on OSNs will be. Since \(\alpha\) affects the rate of infected nodes, it also has a significant impact on the popularity of information. Figure 4: The impact of individual features on the rate of infected individuals in the Facebook dataset: (**a**) the impact of \(\alpha\) on the rate of infected individuals, (**b**) the impact of \(f\) on the rate of infected individuals, and (**c**) the impact of \(W\) on the rate of infected individuals. In order to explore the performance of the IDBESW model, the trends of information diffusion among the IDBESW, IC, and the Epidemic models are compared at a macro level. The classic SIR model is chosen as the baseline of the Epidemic model, and we set the model parameters according to [10]. For the IC model, we choose the model of [39], which is an improvement of the IC model that takes personal preferences into account. The results, shown in Figure 6a, indicate the evolution of propagator normalized density for the three models, which represents the trend of information dissemination. We demonstrate an actual event in Figure 6b, the Ferguson event on Twitter [40]. Through careful observation and comparison, the evolution of information diffusion usually reveals a pattern of rise and fall, with a rapid increase and a slight decrease. The experimental results show that the Epidemic model describes the two phases of growth and decline in the process. However, the rise and fall trend is basically the same in the two stages. Second, the IC model can only simulate the rising phase of the diffusion process because it assumes that once a person is activated, the state will remain unchanged. Finally, the model we proposed includes two stages of rapid growth and slow decline in diffusion. This information diffusion model is consistent not only with the diffusion trend described in the literature, but also with the real-world diffusion process, as shown in Figure 6. Therefore, our model would be the more truthfully model that describes the mode of information diffusion. Figure 5: The impact of individual features on the popularity of the information in the Facebook dataset: (**a**) the impact of \(\alpha\) on the popularity of the information, (**b**) the impact of \(f\) on the popularity of the information, and (**c**) the impact of \(W\) on the popularity of the information.