Wars, economic crisis, climate change are examples of the main challenges that our society is facing nowadays. In this context, the fight against the spread of false information plays a crucial role. If not immediately blocked, fake news can have disastrous consequences, being e.g. the cause of social tensions [1,4]. Information diffusion can be described using epidemiological models, that divide the population in classes and analyze their evolution in time using differential equations [3]. Population is partitioned by considering the following classes [4]: Ignorant, i.e. individuals who can share news, Spreaders who contribute to the process of news sharing and Recovered, who are not interested in the news anymore. However, in the case of fake news these classes are not sufficient. Indeed, a class of individuals who contrast the fake news spread, called the Counter-spreader class, should be considered. The main aim of this talk is to introduce a new mathematical model of ISCR type for the analysis of fake news diffusion [2]. Moreover, by deriving an explicit, elementary stable, positivity preserving numerical method based on NonStandard Finite Differences (NSFDs), and combining its use with a parameter estimation strategy [1], we show the reliability of the proposed model for the prediction of the spread of some fake news recently shared on X. REFERENCES 1. M. Castiello, D. Conte, S. Iscaro, Using Epidemiological Models to Predict the Spread of Information on Twitter. Algorithms, (2023) 16, 391. 2. D. Conte, S. Iscaro, G. Pagano, B. Paternoster: Adapted numerical modeling for fake news diffusion on social networks. Submitted 3. R. D’Ambrosio, G. Giordano, S. Mottola, B. Paternoster, Stiffness analysis to predict the spread out of fake information. Future Internet, (2021) 13(9), 222. 4. M. Muhlmeyer, S. Agarwal, Information spread in a social media age. In Modelling and Control. CRC Press, Taylor and Francis Group (2021).