在自媒体时代，信息来源更多、扩散速度更快、扩散范围更广，这使得网络舆论的监控和管理更加困难。为提高网络舆情的管控能力，需深入了解网络中信息的扩散过程及重要特征。本文首先从微观角度分析信息的扩散机理，考虑网络的结构特点以及舆情监控的时效性，引入与用户是否在线相对应的节点状态，将传统的独立级联模型扩展为基于离散时间的双概率独立级联扩散模型。接着，本文从宏观角度对信息的扩散过程进行分析并定量表示，结合信息自身质量和用户网络特征两个客观要素，并考虑外部平台的影响，进而建立有关事件的动态扩散方程。与以往研究只利用模型模拟或纯粹用数值拟合相比，本文实验给出了社会网络中的信息扩散过程的一个更精准的刻画。实验结果展现了信息传播的多种形态，同时发现用户规模越大、用户关联越紧密以及信息质量越高时，信息爆发所需的时间越短。这些发现有利于预测信息扩散的趋势，同时为舆情管控的时效性和网络用户群体提供参考。
With more sources, higher speed and broader spreading, the public opinions in the network are more difficult to be monitored and controlled. In order to improve the ability of controlling the online public opinions, it is necessary to model the process of information diffusion quantitatively so that we can understand the characteristics of information dissemination. In this paper' the mechanism of information diffusion is first analyzed from the micro perspective, considering both the structural characteristics of networks and the time efficiency of controlling. By taking the state of node into consideration, which corresponds to a user's online or offline, the traditional independent cascade mode is developed into discrete-time oriented bi-probability cascade model. Then, the process of information diffusion is analyzed and quantified from the macro perspective. The dynamic diffusion equation with the quality of information, features of the network and the impact from external platforms is established. Compared with previous studies using only model simulations or numerical fitting, the process of information diffusion in social networks is descrbed more accurately. The experimental results show various forms of information dissemination with different characteristics. Besides, it also shows that less time would be taken for information to burst out with larger size of user network, closer relationship of users and higher quality of information. These findings are helpful to predict the trend of information diffusion and make it easier to recognize important propagators in time when managing the public opinions in social networks.
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