因特网舆情传播的协同元胞自动机模型

Synergistic cellular automata model for dissemination of Internet public opinion

现有的因特网舆情传播研究或者从话题文本增长—消亡过程的数学统计或智能学习出发,或者采用元胞自动机或隐马尔可夫模型(HMM)分析主题倾向度的演化过程。但这些研究均缺乏有关舆情主体属性对因特网舆情传播影响的分析。基于因特网舆情空间的系统协同性,首先计算元胞状态协同转移概率,同时将整体协同转移概率与中心元胞之九邻居局域状态概率比较,确定中心元胞状态是否转换。经过若干次时间序列的迭代计算,获得舆情整体传播趋向"+"或"-"的程度(磁化率)。通过观察磁化率—时间变化曲线,能清楚地了解舆情传播的演化。在此基础上,提出一个扩展的协同元胞自动机模型及算法。仿真结果表明环境适应度参变量表达了网络舆情主体从众心理,其变化影响磁化率向多数人意见靠拢;而偏好参变量使网络舆情整体快速向"偏好"方向传播。该模型比较接近现实社会网络的舆情传播方式。

As for the present research on the dissemination of Internet Public Opinion （IPO）, some research use mathematic statistics or intelligent learning to analyze the growing or descending process of a topic related text, and some use cellular automata or Hidden Markov Mode]. （HMM） to find the tendency propagation of the subject of IPO. However, all of them lack the analyses of the impacts of the subject attributes in IPO on its tendency propagation. Based on the systematic synergy of IPO space, the synergistic transition probability between states on whole cells space of discussed IPO was computed firstly, and then it was compared with a local state probability in 9 neighbors of a central cell to decide whether the state of central cell should be converted. After several iterative operations, the degree （magnetisability） which expressed the tendency propagation upon to ＂ ＋ ＂ or ＂ - ＂ was obtained. Through observing the magnetisability-time variable curve, one can clearly handle its evolution. Therefore, a new model and an algorithm of extensive synergistic cellular automata model were presented. The simulation resuhs show that the order-variable parameters of society adaptability can express the subject＇s group psychology, and it goes towards the majority opinion. Similarly, the order-variable parameters of preference fast make tendency propagation close to the direction of preference, i. e ＂ ＋ ＂ or ＂ - ＂. The model is relatively closer to the real situation of dissemination of IPO.