Social Media Networks, Fake News, and Polarization

We study how the structure of social media networks and the presence of fake news might affect the degree of misinformation and polarization in a society. For that, we analyze a dynamic model of opinion exchange in which individuals have imperfect information about the true state of the world and are partially bounded rational. Key to the analysis is the presence of internet bots: agents in the network that do not follow other agents and are seeded with a constant flow of biased information. We characterize how the flow of opinions evolves over time and evaluate the determinants of long-run disagreement among individuals in the network. To that end, we create a large set of heterogeneous random graphs and simulate a long information exchange process to quantify how the bots’ ability to spread fake news and the number and degree of centrality of agents susceptible to them affect misinformation and polarization in the long-run.

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