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<p>Multiple pieces of information regularly propagate in a social network. Different political party supporters utilize social systems not only for campaigning, publicity but also for opposing the opinions of other parties. They always try to create some agenda against the opposition. It is interesting to recognize the pattern when two conflicting pieces of information interact on social networks. Here, we present a nonlinear model of opposite information spread in a homogeneous network system. We considered two kinds of users, supporting two conflicting news stories at a time with the ability to protect their opinions from others. We obtained fixed points, their existence, and stability conditions. Here, we watch that social network system experience flip bifurcation and hopf bifurcation. We had chaos in the dynamics, which shows the uncertainty in the observation. Moreover, we suggested a strategy for controlling the complex dynamics of information spread on social networks in emergencies.</p>

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Dynamical behavior and chaos control of the conflicting information propagation on a homogeneous network system

  • 期刊论文

  • 发表日期：

    2025年1月1日

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  • Ankur Jain,
  • Joydip Dhar,
  • Vijay K. Gupta

摘要

Multiple pieces of information regularly propagate in a social network. Different political party supporters utilize social systems not only for campaigning, publicity but also for opposing the opinions of other parties. They always try to create some agenda against the opposition. It is interesting to recognize the pattern when two conflicting pieces of information interact on social networks. Here, we present a nonlinear model of opposite information spread in a homogeneous network system. We considered two kinds of users, supporting two conflicting news stories at a time with the ability to protect their opinions from others. We obtained fixed points, their existence, and stability conditions. Here, we watch that social network system experience flip bifurcation and hopf bifurcation. We had chaos in the dynamics, which shows the uncertainty in the observation. Moreover, we suggested a strategy for controlling the complex dynamics of information spread on social networks in emergencies.