The College of Literature and Journalism, Sichuan University, Chengdu, Sichuan Province, China.
The College of Movie and Media, Sichuan Normal University, Chengdu, Sichuan Province, China.
PLoS One. 2021 Dec 31;16(12):e0261009. doi: 10.1371/journal.pone.0261009. eCollection 2021.
A conventional model of public opinion analysis is no longer suitable when the internet is the primary arena of information dissemination. Thus, a more practical approach is urgently needed to deal with this dynamic and complicated phenomenon of propagating public opinion. This paper proposes that the outbreak of internet public opinion and its negative impacts, such as the occurrence of major security incidents, are a result of coupling and the complex interaction of many factors. The Functional Resonance Analysis Method model is composed of those factors and considers the stages of network information dissemination, the unique propagation rule, and textual sentiment resonance on the internet. Moreover, it is the first public opinion governance method that simultaneously highlights the complex system, functional identification, and functional resonance. It suggests a more effective method to shorten the dissipation time of negative public opinion and is a considerable improvement over previous models for risk-prediction. Based on resonance theory and deep learning, this study establishes public opinion resonance functions, which made it possible to analyze public opinion triggers and build a simulation model to explore the patterns of public opinion development through long-term data capture. The simulation results of the Functional Resonance Analysis Method suggest that the resonance in the model is consistent with the evolution of public opinion in real situations and that the components of the resonance of public opinion can be separated into eleven subjective factors and three objective factors. In addition, managing the subjective factors can significantly accelerate the dissipation of negative opinions.
当互联网成为信息传播的主要领域时，传统的舆论分析模型不再适用。因此，迫切需要一种更实用的方法来应对这种传播舆论的动态和复杂现象。本文提出，互联网舆论的爆发及其负面影响，如重大安全事件的发生，是许多因素耦合和复杂相互作用的结果。功能共振分析方法模型由这些因素组成，并考虑了网络信息传播的阶段、独特的传播规律以及互联网上的文本情感共振。此外，它是同时突出复杂系统、功能识别和功能共振的第一个舆论治理方法。它提出了一种更有效的方法来缩短负面舆论的消散时间，并且是对以前风险预测模型的重大改进。基于共振理论和深度学习，本研究建立了舆论共振函数，可以分析舆论触发因素，并通过长期数据捕获构建一个模拟模型来探索舆论发展模式。功能共振分析方法的模拟结果表明，模型中的共振与实际情况下的舆论演变一致，并且舆论共振的组成部分可以分为十一个主观因素和三个客观因素。此外，管理主观因素可以显著加速负面意见的消散。
