Cluster Structure of Online Users Generated from Interaction Between Fake News and Corrections

The problem caused by fake news continues to worsen in today's online social networks. Intuitively, it seems effective to issue corrections as a countermeasure. However, corrections can, ironically, strengthen attention to fake news, which worsens the situation. This paper proposes a model for describing the interaction between fake news and the corrections as a reaction-diffusion system; this yields the mechanism by which corrections increase attention to fake news. In this model, 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 groups that are strongly interested in discussing fake news. Also, we propose and evaluate a basic strategy to counter fake news.

Automated summary

The problem of fake news is worsening in online social networks, and issuing corrections as a countermeasure may ironically strengthen attention to fake news. This paper proposes a reaction-diffusion model to describe the interaction between fake news and corrections, explaining how corrections increase attention to fake news. Numerical calculations show that even without spatial bias, the interaction between fake news and corrections creates groups interested in discussing fake news. Additionally, a basic strategy to counter fake news is proposed and evaluated.