Spread of (mis)information in social networks
Consider a social network, where individuals meet pairwise and exchange information. An agent is forceful if he influences the beliefs of other agents, but is not influenced by others. This setup induces a Markov chain. The main result of the paper characterizes the consensus distribution of that Markov chain. This distribution contains misinformation, the extent of which can be measured.
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