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.
We would like to thank the participants of the 2017 NBER Summer Institute on Political Economy (Boston), 22nd Coalition Theory NetworkWorkshop (Glasgow), 27th International Conference on Game Theory (Stony Brook), 43rd Eastern Economic Association Conference (New York) and seminar series at London Business School/Government (London), Warwick, Harris Public School (Chicago). In particular we are grateful for the valuable comments from Jesse Shapiro, Daron Acemoglu, Ernesto Dal Bo, Matthew Jackson, Yair Tauman and Helios Herrera. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.