Abstract
AbstractThe problem of fake news continues to worsen in today’s online social networks. Intuitively, it seems effective to send corrections as a countermeasure. However, there in actuality corrections can, ironically, strengthen attention to fake news, which worsens the situation. In this paper, we model the interaction between fake news and the corrections as a reaction-diffusion system and propose a framework that describes the mechanism that can make corrections increase attention to fake news. In this framework, the emergence of groups of users who believe in fake news is understood as a Turing pattern that appears in the activator-inhibitor model. Numerical calculations show that even if the network structure has no spatial bias, the interaction between fake news and the corrections creates a group that is strongly interested in discussing fake news.KeywordsOnline social networkFake newsReaction-diffusion systemActivator-inhibitor systemTuring pattern
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