Rumor Transmission in Online Social Networks Under Nash Equilibrium of a Psychological Decision Game

Wenjia Liu, Jian Wang, Yanfeng Ouyang

Research output: Contribution to journalArticlepeer-review

Abstract

This paper investigates rumor transmission over online social networks, such as those via Facebook or Twitter, where users liberally generate visible content to their followers, and the attractiveness of rumors varies over time and gives rise to opposition such as counter-rumors. All users in social media platforms are modeled as nodes in one of five compartments of a directed random graph: susceptible, hesitating, infected, mitigated, and recovered (SHIMR). The system is expressed with edge-based formulation and the transition dynamics are derived as a system of ordinary differential equations. We further allow individuals to decide whether to share, or disregard, or debunk the rumor so as to balance the potential gain and loss. This decision process is formulated as a game, and the condition to achieve mixed Nash equilibrium is derived. The system dynamics under equilibrium are solved and verified based on simulation results. A series of parametric analyses are conducted to investigate the factors that affect the transmission process. Insights are drawn from these results to help social media platforms design proper control strategies that can enhance the robustness of the online community against rumors.

Original languageEnglish (US)
Pages (from-to)831–854
Number of pages24
JournalNetworks and Spatial Economics
Volume22
Issue number4
Early online dateJun 30 2022
DOIs
StatePublished - Dec 2022
Externally publishedYes

Keywords

  • Directed networks
  • Edge-based information model
  • Nash equilibrium
  • Rumor transmission
  • Societal behavior

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Computer Networks and Communications

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