面向群体极化的网络舆情演化研究
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摘要
随着互联网的应用越来越普及,公共事件传播变得十分迅速,大众参与积极性空前高涨,网络舆情中良莠不齐、情绪化和非理性化的信息泛滥,容易引发公共危机,即出现群体极化现象。网络舆情已经成为社会民意快捷的反馈渠道,成为社会情绪丰富的展示窗口。
面对如此庞杂的网络舆情,政府如何了解网络舆情背后的社情民意,探索应对策略,是网络舆情研究亟待解决的问题。国内外研究者运用多种定性和定量的方法围绕网络舆情演化展开了卓有成效的研究,这些成果为研究探讨具有中国特性的网络舆情演化提供了重要的参考。
本文基于前人的研究成果,定性分析网络舆情虚拟社区的特性,构建网络舆情演化模型,分析网络舆情演化影响因素,找出影响因素在网络舆情群体极化中的影响规律,为网络舆情的监管提供方法性指导和理论依据。
首先分析网络舆情演化过程中的群体极化,总结并提出网络舆情群体极化的四种现象：单极聚化、两极分化、多极裂化和零极淡化。分析网络舆情演化的影响因素,把影响因素分归纳为：事件透明度、网民理性度和舆情热度,其中舆情热度又包括：环境热度、时间热度、关注热度、发帖热度,以及时间。找出影响因素对群体极化的影响关系。构建影响因素的统计方法,分析演化影响因素之间的动力关系。
其次,针对网络舆情虚拟社区的元胞自动机特性、小世界效应和无标度特性,分析面向群体极化的网络舆情演化发生、发展和变化的过程,构建反映演化规律的相关模型,通过仿真验证模型的合理性。主要包括以下三个方面：
1)构建迁移元胞遍历的迁移元胞演化模型。分析了网络舆情演化中的观点交互过程,建立网络舆情虚拟社区；基于元胞自动机原理,分析网络舆情虚拟社区的元胞自动机特性,构造迁移元胞；根据影响权重建立倾向度和自信度两个参数,研究倾向度和自信度对网络舆情演化的影响；提出观点交互的标准化自信度多数规则倾向度转换函数,构建迁移元胞遍历的迁移元胞演化模型。
2)构建小世界效应下的网络舆情演化迁移元胞模型。基于复杂网络的小世界效应原理,分析网络舆情中观点关系的小世界效应特性,建立小世界效应网络舆情虚拟社区,构造迁移元胞和移动元胞；选择倾向度和自信度影响参数,研究倾向度和自信度对网络舆情演化的影响；提出小世界网络中的倾向度转换规则,构建小世界效应下的网络舆情演化迁移元胞模型。
3)构建无标度特性下的网络舆情演化二阶段模型。基于复杂网络的无标度特性原理,采用实证分析网络舆情中观点形成与观点交互的无标度特性,建立无标度网络舆情虚拟社区；分析无标度网络舆情虚拟社区中舆情演化过程的观点形成阶段和观点交互阶段,提出观点形成算法和观点交互算法；通过对舆情演化中观点形成阶段和观点交互阶段的分析,构建无标度特性下的网络舆情演化二阶段模型。
最后通过对群体极化下网络舆情演化的影响因素和演化模型的分析,探索应对策略,提出了群体极化下网络舆情的管理策略。通过这些管理策略的实施,能在一定程度上疏导控制反面或不利的群体极化的产生,防止反面或不利的群体极化的加剧,加强党的执政能力,提高各级政府应对网络突发事件的主动性,营造促进社会发展的良好舆论环境。
With the growing popularity of Internet applications, public events quickly spread, and the public participate in these events with unprecedented enthusiasm. In Internet, a lot of information is bad as well as good, emotional and irrational. These factors are apt to cause a public crisis—group polarization. The Internet public opinion has become a fast feedback channel of social public opinion and rich display window of social emotional.
Faced with such a vast and complex Internet public opinion, how to understand the public opinion and explore coping strategies are problems demanding prompt solution for the government. On these problems, some research has been done by many scholars at home and abroad with a variety of qualitative and quantitative methods. These research results provide very rich reference value for the study of Internet public opinion evolution whit Chinese characteristics.
Based on the results of previous studies, in this paper, the characteristics are qualitative analyzed of virtual community of Internet public opinion, and model is made for Internet public opinion evolution. The model is used to analyze influence factors of Internet public opinion evolution, and to find the influence law for influence factors affecting group polarization. The research provides methodological guidance and theoretical basis for management of Internet public opinion.
First, analyzing the group polarization in Internet public opinion evolution, a result is got that Internet public opinion evolution has four kinds of group polarization: uni-polarization, bi-polarization, multi-polarization and non-polarization. Analyzing influence factors of Internet public opinion evolution, these influence factors are summarized as follows:transparency of event, rationality of Internet users and heat of public opinion. Heat of public opinion includes heat of environment, heat of time, heat of attention, heat of posting, and time. The relationship is identified these factors and group polarization. By constructing a statistical method, the motivation relationship is analyzed within these factors.
Second, it is done to analyze the Internet public opinion evolution emerge, development, change with cellular automata feature, small-world effect and scale-free property in virtual community of Internet public opinion. Related model are made, and simulated to testify whether the model is reasonable
1) Building evolutionary model of cell migration on cell migration traversing.
By analyzing the view interaction in Internet public opinion evolution, virtual community is built. Based on cellular automata theory, feature of the cellular automata is analyzed in Internet public opinion virtual community, and migration cell is put forward. By weight of affect, two parameters is established, namely tendency and confidence. The influence is analyzed between two parameters and Internet public opinion evolution. It is put forward that cellular transformation confident standardized majority rules. The migration cellular automata model of Internet public opinion is constructed.
2) Building migration cellular model in Internet public opinion evolution based on small world effect.
Based on the small-world effect theory in a complex network, the characteristics of small-world effect are analyzed and virtual community is built with small-world effect in Internet public opinion. Migration cell and Moving cell are put forward. The influence is analyzed between two parameters and Internet public opinion evolution, using two parameters tendency and confidence. It is put forward that tendency transformation rules with small-world effect. The migration cellular model in Internet public opinion evolution based on small world effect is constructed.
3) Building tow stages model in Internet public opinion evolution based on scale-free characteristic.
Based on the scale-free characteristic theory in a complex network, scale-free characteristic is analyzed by empirical in the view form stage and the view interactive stage. Virtual community is built with scale-free characteristic in Internet public opinion. View formation and interactive algorithms are constructed and two stages model is built in Internet public opinion evolution based on scale-free characteristic.
Finally, by analyzing factors and evolution model of Internet public opinion under group polarization, some management strategies are put forward about Internet public opinion under group polarization. To a certain extent, implementing these strategies, this can give guidance to the control of negative or unfavorable group polarization generation, prevent aggravation of negative or unfavorable group polarization, improve the party's ability, enhance initiative of responding to emergency at all levels of government, and create a good public opinion environment which can promote social development.
面对如此庞杂的网络舆情,政府如何了解网络舆情背后的社情民意,探索应对策略,是网络舆情研究亟待解决的问题。国内外研究者运用多种定性和定量的方法围绕网络舆情演化展开了卓有成效的研究,这些成果为研究探讨具有中国特性的网络舆情演化提供了重要的参考。
本文基于前人的研究成果,定性分析网络舆情虚拟社区的特性,构建网络舆情演化模型,分析网络舆情演化影响因素,找出影响因素在网络舆情群体极化中的影响规律,为网络舆情的监管提供方法性指导和理论依据。
首先分析网络舆情演化过程中的群体极化,总结并提出网络舆情群体极化的四种现象：单极聚化、两极分化、多极裂化和零极淡化。分析网络舆情演化的影响因素,把影响因素分归纳为：事件透明度、网民理性度和舆情热度,其中舆情热度又包括：环境热度、时间热度、关注热度、发帖热度,以及时间。找出影响因素对群体极化的影响关系。构建影响因素的统计方法,分析演化影响因素之间的动力关系。
其次,针对网络舆情虚拟社区的元胞自动机特性、小世界效应和无标度特性,分析面向群体极化的网络舆情演化发生、发展和变化的过程,构建反映演化规律的相关模型,通过仿真验证模型的合理性。主要包括以下三个方面：
1)构建迁移元胞遍历的迁移元胞演化模型。分析了网络舆情演化中的观点交互过程,建立网络舆情虚拟社区；基于元胞自动机原理,分析网络舆情虚拟社区的元胞自动机特性,构造迁移元胞；根据影响权重建立倾向度和自信度两个参数,研究倾向度和自信度对网络舆情演化的影响；提出观点交互的标准化自信度多数规则倾向度转换函数,构建迁移元胞遍历的迁移元胞演化模型。
2)构建小世界效应下的网络舆情演化迁移元胞模型。基于复杂网络的小世界效应原理,分析网络舆情中观点关系的小世界效应特性,建立小世界效应网络舆情虚拟社区,构造迁移元胞和移动元胞；选择倾向度和自信度影响参数,研究倾向度和自信度对网络舆情演化的影响；提出小世界网络中的倾向度转换规则,构建小世界效应下的网络舆情演化迁移元胞模型。
3)构建无标度特性下的网络舆情演化二阶段模型。基于复杂网络的无标度特性原理,采用实证分析网络舆情中观点形成与观点交互的无标度特性,建立无标度网络舆情虚拟社区；分析无标度网络舆情虚拟社区中舆情演化过程的观点形成阶段和观点交互阶段,提出观点形成算法和观点交互算法；通过对舆情演化中观点形成阶段和观点交互阶段的分析,构建无标度特性下的网络舆情演化二阶段模型。
最后通过对群体极化下网络舆情演化的影响因素和演化模型的分析,探索应对策略,提出了群体极化下网络舆情的管理策略。通过这些管理策略的实施,能在一定程度上疏导控制反面或不利的群体极化的产生,防止反面或不利的群体极化的加剧,加强党的执政能力,提高各级政府应对网络突发事件的主动性,营造促进社会发展的良好舆论环境。
With the growing popularity of Internet applications, public events quickly spread, and the public participate in these events with unprecedented enthusiasm. In Internet, a lot of information is bad as well as good, emotional and irrational. These factors are apt to cause a public crisis—group polarization. The Internet public opinion has become a fast feedback channel of social public opinion and rich display window of social emotional.
Faced with such a vast and complex Internet public opinion, how to understand the public opinion and explore coping strategies are problems demanding prompt solution for the government. On these problems, some research has been done by many scholars at home and abroad with a variety of qualitative and quantitative methods. These research results provide very rich reference value for the study of Internet public opinion evolution whit Chinese characteristics.
Based on the results of previous studies, in this paper, the characteristics are qualitative analyzed of virtual community of Internet public opinion, and model is made for Internet public opinion evolution. The model is used to analyze influence factors of Internet public opinion evolution, and to find the influence law for influence factors affecting group polarization. The research provides methodological guidance and theoretical basis for management of Internet public opinion.
First, analyzing the group polarization in Internet public opinion evolution, a result is got that Internet public opinion evolution has four kinds of group polarization: uni-polarization, bi-polarization, multi-polarization and non-polarization. Analyzing influence factors of Internet public opinion evolution, these influence factors are summarized as follows:transparency of event, rationality of Internet users and heat of public opinion. Heat of public opinion includes heat of environment, heat of time, heat of attention, heat of posting, and time. The relationship is identified these factors and group polarization. By constructing a statistical method, the motivation relationship is analyzed within these factors.
Second, it is done to analyze the Internet public opinion evolution emerge, development, change with cellular automata feature, small-world effect and scale-free property in virtual community of Internet public opinion. Related model are made, and simulated to testify whether the model is reasonable
1) Building evolutionary model of cell migration on cell migration traversing.
By analyzing the view interaction in Internet public opinion evolution, virtual community is built. Based on cellular automata theory, feature of the cellular automata is analyzed in Internet public opinion virtual community, and migration cell is put forward. By weight of affect, two parameters is established, namely tendency and confidence. The influence is analyzed between two parameters and Internet public opinion evolution. It is put forward that cellular transformation confident standardized majority rules. The migration cellular automata model of Internet public opinion is constructed.
2) Building migration cellular model in Internet public opinion evolution based on small world effect.
Based on the small-world effect theory in a complex network, the characteristics of small-world effect are analyzed and virtual community is built with small-world effect in Internet public opinion. Migration cell and Moving cell are put forward. The influence is analyzed between two parameters and Internet public opinion evolution, using two parameters tendency and confidence. It is put forward that tendency transformation rules with small-world effect. The migration cellular model in Internet public opinion evolution based on small world effect is constructed.
3) Building tow stages model in Internet public opinion evolution based on scale-free characteristic.
Based on the scale-free characteristic theory in a complex network, scale-free characteristic is analyzed by empirical in the view form stage and the view interactive stage. Virtual community is built with scale-free characteristic in Internet public opinion. View formation and interactive algorithms are constructed and two stages model is built in Internet public opinion evolution based on scale-free characteristic.
Finally, by analyzing factors and evolution model of Internet public opinion under group polarization, some management strategies are put forward about Internet public opinion under group polarization. To a certain extent, implementing these strategies, this can give guidance to the control of negative or unfavorable group polarization generation, prevent aggravation of negative or unfavorable group polarization, improve the party's ability, enhance initiative of responding to emergency at all levels of government, and create a good public opinion environment which can promote social development.
引文
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