# Realistic Aspects of Simulation Models for Fake News Epidemics over Social Networks
## 2 Related Work
### Epidemic Modeling  
Historically, the spreading of misinformation has been modeled using the same epidemic models employed for common viruses [10].  
In [36], the authors proposed a fake news spreading model based on differential equations and studied the parameters of the model that keep a fake news item circulating in a social network after its onset. Separately, the authors suggested which parameters would make the spreading die out over time. In [37], the authors modeled the engagement onset and decrease for fake news items on Twitter as two subsequent cascade events affecting a Poisson point process. After training the parameters of the model through fake news spreading data from Twitter, the authors showed that the model predicts the evolution of fake news engagement better than linear regression, or than models based on reinforced Poisson processes.  
In [9], the authors simulated the spreading of a hoax and its debunking at the same time. They built upon a model for the competitive spreading of two rumors, in order to describe the competition among believers and fact-checkers. Users become fact-checkers through the spreading process, or if they already know that the news is not true, or because they decide to verify the news by themselves. The authors also took forgetfulness into account by making a user lose interest in the fake news item with a given probability. They studied the existence of thresholds for the fact-checking probability that guarantees the complete removal of the fake news from the network and proved that such a threshold does not depend on the spreading rate, but only on the gullibility and forgetting probability of the users. The same authors extended their previous study assessing the role of network segregation in misinformation spreading [38] and comparing different fact-checking strategies on different network topologies to limit the spreading of fake news [39].  
In [40], the authors proposed a mixed-method study: they captured the personality of users on a social network through a questionnaire and then modeled the agents in their simulations according to the questionnaire, in order to understand how the different personalities of the users affect the epidemic spreading. In [41], the authors studied the influence of online bots on a network through simulations, in an opinion dynamics setting. The clusterization of opinions in networks was the focus of [42], who observed the emergence of echo chambers that amplify the influence of a seeded opinion, using a simplified agent interaction model that does not include time-dynamical settings. In [43], the authors proposed an agent-based model of two separate, but interacting spreading processes: one for the physical disease and a second one for low-quality information about the disease. The spreading of false information was shown to worsen the spreading of the disease itself. In [44], the authors studied how the presence of heterogeneous agents affects the competitive spreading of low- and high-quality information. Theyalso proposed methods to mitigate the spreading of false information without affecting a system's information diversity.  
In Table 1, we summarize the contributions in the literature that are most related to our approach based on the realistic agent-based simulation of fake news and debunking spreading over OSNs. The references appear in order of citation within the manuscript. We observe that while several contributions exist, most of them consider analytical models of the spreading process or simplified agent interactions that may not convey the behavior of a true OSN user. This prompted us to propose an improved simulation model including different types of agents, time dynamical events affecting the agent interactions, and non-uniform node trust. Moreover, some of the works in Table 1 focus just on the spreading of the fake news itself, and neglect debunking. Instead, we explicitly model both competing processes.