Modeling information spread in polarized communities: Transitioning from legacy media to a Facebook world

Abstract

Rumors have played an important role in social life for centuries, with early examples including their use to steer Roman politics. Today's world includes entire industries focused on digital misinformation, whose rumors can spread quickly via social networks such as Facebook not only because of their structure (e.g., clustering) but also because individuals can place an excessively high trust in information originating from their friends. Relaying information from our friends and ignoring or being unaware of other opinions leads to polarized groups, such as liberals or conservatives in a political context. While numerous models of rumor spreads have been proposed, their focus was more often on the conditions to stop/verify one rumor than in accounting for a polarized context. In this paper, we develop a new model of rumor spread with two different susceptibility rates, which can be used to investigate cases in which the population can be sub-divided with respect to one rumor (e.g. based on political opinions or socio-economic factors such as educational attainment). We describe the dynamics of the model using differential equations, and present numerical results regarding the model behavior with respect to key parameters such as the rate with which rumors are forgotten. While our work took into account network features (e.g., average degree), it is of particular interest for future work to examine the interplay between the network structure and the distribution of susceptibility rates.