# Realistic Aspects of Simulation Models for Fake News Epidemics over Social Networks
## 6 Conclusions  
In this work, we argue that most modeling efforts for fake news epidemics over social networks rely on analytical models that capture the general trends of the epidemics (with some necessary approximations) or on simple simulation approaches that often lack sufficiently realistic settings. We fill this gap by extending the simple susceptible believer fact-checker (SBFC) model to make it more realistic. We propose adding time dynamical features to the simulations (including variable access times to the network and news engagement decay), node-dependent attributes, and a richer context for mutual node trust through weighted network edges. Moreover, we analyze the impact of influencers, bots, and eternal fact-checkers on the spreading of a fake news item. Our simulations confirm that the above realistic factors are very impactful, and neglecting them (e.g., via a model that considers synchronous OSN access for all agents, or a network where all agents trust one another equally) masks several key outcomes. The importance of a realistic simulation tool lies in the reliability of the analyses it enables. For example, the designers of marketing campaigns could assess how much fake news would damage a company or product by spreading into a population of interest. Similarly, managers and policy-makers may assess opinion spreading and polarization (which follows similar mechanisms as fake news spreading) by means of realistic simulations. In practical terms, our results suggest that reducing the impact of bots and increasing the presence and impact of authoritative personalities [11] are key to limiting fake news spreading. This may entail educating OSN users to recognize bot behavior or by implementing forms of automatic recognition that flag suspicious activity patterns as bot-like. The impact of influencers is also very relevant, as they significantly speed up the spreading of the news online and amplify the \"small-world effect\" [49], avoiding influencers falling prey of fake news, and rather increasing their involvement in stopping fake news and/or promoting their debunking. The above methods would further help improve the healthiness of interactions and opinion communications over social networks, reducing the concerns of online users. The main limitations of this study concern the test scenarios, which should be extended to comprise a greater number of nodes and to include agents with multiple levels of popularity, besides common users and influencers. We leave this investigation for a future extension of our work. Similarly, our agent-based simulation model recreates network access patterns (hence, the storyboard of social interactions) according to a mathematical model. While this is still better than having all agents interact synchronously, we could further improve the level of realism by testing our model on social network datasets that include temporal annotations. Moreover, we could consider additional dynamics in the network model. For example, we plan to extend our interaction model through an unfollowing functionality (i.e., social connection removal) in order to amplify the circulation of some news pieces in specific communities. Conceptualization, Q.F.L. and D.Z. ; methodology, Q.F.L., D.Z. and P.C. ; software, Q.F.L. and D.Z. ; writing--original draft preparation, Q.F.L., D.Z. and P.C. ; writing--review and editing, Q.F.L. and P.C. All authors read and agreed to the present version of the manuscript. **Funding:** This research was supported in part by the Italian Ministry for Education (MIUR) under the initiative _Departments of Excellence_ (Law 232/2016). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.