# Realistic Aspects of Simulation Models for Fake News Epidemics over Social Networks
## 1 Introduction  
The pervasiveness of online social networks (OSNs) has given people unprecedented power to share information and reach a huge number of peers, both in a short amount of time and at no cost. This has shaped the way people interact and access information: currently, it has become more common to access news on social media, rather than via traditional sources [1]. A drawback of this trend is that OSNs are very fertile grounds to spread false information [2]. To avoid being gulled, users therefore need to continuously check facts, and possibly establish reliable news access channels. Contrasing unverified information has become an important topic of discussion in our society. In this hugely inter-connected world, we constantly witness attempts at targeting and persuading users both for commercial [3] and political purposes [4]. A single piece of false or misinterpreted information can not just severely damage the reputation of an individual, but also influence public opinion and affect the life of millions of people [4]. In particular, social networking is a fundamental interaction channel for people, communities, and interest groups. Not only does social networking offer the chance to exchange opinions, information, discussion, as well as multimedia contents: it is also an invaluable source of data [3]. Unhealthy online social behaviors can damage this source by working as echo chambers of toxic contents and may reduce the trust of the users in social media by posing privacy breaching concerns [5, 6]. The very structure of OSNs, i.e., the underlying topology induced by the connections among the users, is key to amplifying the impact of fake news. The presence of \"influencers\" on social networks, such as celebrities or public figures with an uncommonly high number of followers, drastically speeds up information spreading [7]. In this context, another issue is the presence of bots, i.e., autonomous agents that aim to flood communities with malicious messages, in order to manipulate consensus or to influence user behavior [8]. Epidemic models [9] are viable and common tools to study the spreading of fake news on social networks. They were first introduced to model the spreading of diseases among interacting agents [10]. In these models, people are considered as the nodes of a social network graph, whose edges convey the social relationship between the corresponding nodes. We consider a variant to the classical compartmental susceptible, infected, recovered (SIR) model: the susceptible, believer, fact-checker (SBFC) model [9]. Here, a node is initially susceptible to being gulled by a fake news item; it may then become a believer (i.e., it believes in the fake news item) when getting in contact with another believer; it may also become a fact-checker (i.e., it believes that the news item is fake) if it verifies the news item, or if it comes in contact with another fact-checker node. The key question driving our work is the following: Can we perform an agent-based simulation of an online social network that reproduces key aspects of fake news injection and spreading and at the same time is realistic enough to enable a reliable analysis of multiple OSN scenarios? In fact, the spreading of fake news has several peculiar characteristics that differentiate it from the spreading of common diseases. These aspects include the different connectedness of agents in an OSN (e.g., bots, influencers, and common agents), the presence of fact-checkers, the spreading of debunking information that reduces the engagement of fake news, and the time-varying aspects of OSN user activity. Such peculiarities need to be taken into account in order to accurately characterize fake news spreading. To answer the above research question, in this study, we build on the SBFC model to introduce some realistic extensions.