Online social media networks have become extremely influential sources of news and information. Given the large audience and the ease of sharing content online, the content that spreads on online social networks can have important consequences on public opinion, policy, and voting. To better understand the online content spread, mathematical modeling of opinion dynamics is becoming an increasingly popular field of study. In this talk, I will introduce you to a special class of models of opinion dynamics on networks called bounded-confidence models. I will then discuss some of the applications and theory that my collaborators and I have been developing with these models, including the impact of media, opinion dissemination, mean-field dynamics, and extensions to hypergraphs and multilayer networks. This talk will also include some unsolved questions for future work.
Applied Math Talk: Bounded-confidence models for opinion dynamics on online social networks given by Professor Heather Zinn Brooks (HMC)
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