Impact of Confirmation Bias on Competitive Information Spread in Social Networks

Yanbing Mao, Emrah Akyol, Naira Hovakimyan

Research output: Contribution to journalArticlepeer-review

Abstract

This paper investigates the impact of confirmation bias on competitive information spread in the social network that comprises individuals in a social network and competitive information sources at a cyber layer. We formulate the problem of information spread as a zero-sum game, which admits a unique Nash equilibrium in pure strategies. We characterize the dependence of pure Nash equilibrium on the public's innate opinions, the social network topology, as well as the parameters of confirmation bias. We uncover that confirmation bias moves the equilibrium towards the center only when the innate opinions are not neutral, and this move does not occur for the competitive information sources simultaneously. Numerical examples in the context of well-known Krackhardt's advice network are provided to demonstrate the correctness of theoretical results.

Original languageEnglish (US)
JournalIEEE Transactions on Control of Network Systems
DOIs
StateAccepted/In press - 2021

Keywords

  • Analytical models
  • Competitive information spread
  • confirmation bias
  • Context modeling
  • Games
  • innate opinion
  • Nash equilibrium
  • Nash equilibrium
  • social network topology
  • Social networking (online)
  • Steady-state
  • Symmetric matrices
  • zero-sum game

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications
  • Control and Optimization

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