This paper proposes a novel competitive information dissemination macro model CISIR (Competitive Information Susceptible Infected Recovered) in online social network (OSN). Firstly, the Markov chain theory is used to analyze the intrinsic relationship between the competition mechanism among different types of information on the network and the rules of node state transition and information propagation evolution. The macro probability model of node state transition is constructed from the perspective of probability, and the macro information diffusion of network system is constructed from the perspective of statistics. Secondly, the equilibrium point and stability of the proposed model are solved to ensure that the model is reasonable and meaningful. Finally, the relationship between the parameters in the model is analyzed by numerical simulation experiment, and the dynamic simulation is carried out. The process of information competition and dissemination is analyzed, and the degree of agreement between the simulation results and the real event statistics is researched through empirical comparative experiments. Experimental results show that the proposed CISIR model is reasonable and effective. It provides a new scientific method and research approach to solve the problem of competitive propagation of different information types in OSN, and it has high theoretical value and application value.

本文提出了一种新颖的在线社交网络（OSN）中的竞争信息传播宏模型CISIR（竞争信息易感感染恢复）。首先，利用马尔可夫链理论分析了网络上不同类型信息之间的竞争机制与节点状态转移和信息传播演化规律之间的内在联系。从概率的角度构造了节点状态转换的宏观概率模型，从统计的角度构造了网络系统的宏观信息扩散。其次，解决了所提模型的平衡点和稳定性，以确保模型合理合理。最后，通过数值仿真实验分析了模型中各参数之间的关系，并进行了动态仿真。分析了信息竞争和传播的过程，并通过经验比较实验研究了模拟结果与真实事件统计之间的一致性程度。实验结果表明，所提出的CISIR模型是合理有效的。它为解决OSN中不同信息类型的竞争性传播问题提供了一种新的科学方法和研究方法，具有较高的理论价值和应用价值。通过实证比较实验研究了仿真结果与真实事件统计量的吻合程度。实验结果表明，所提出的CISIR模型是合理有效的。它为解决OSN中不同信息类型的竞争性传播问题提供了一种新的科学方法和研究方法，具有较高的理论价值和应用价值。通过实证比较实验研究了仿真结果与真实事件统计量的吻合程度。实验结果表明，所提出的CISIR模型是合理有效的。它为解决OSN中不同信息类型的竞争性传播问题提供了一种新的科学方法和研究方法，具有较高的理论价值和应用价值。