Using Epidemiological Models to Predict the Spread of Information on Twitter

Abstract

In this article, we analyze the spread of information on social media (Twitter) and purpose a strategy based on epidemiological models. It is well known that social media represent a strong tool to spread news and, in particular, fake news, due to the fact that they are free and easy to use. First, we propose an algorithm to create a proper dataset in order to employ the ignorants–spreaders–recovered epidemiological model. Then, we show that to use this model to study the diffusion of real news, parameter estimation is required. We show that it is also possible to accurately predict the evolution of news spread and its peak in terms of the maximum number of people who share it and the time when the peak occurs trough a process of data reduction, i.e., by using only a part of the built dataset to optimize parameters. Numerical results based on the analysis of real news are also provided to confirm the applicability of our proposed model and strategy.