关键词: 网络舆情, 传播预测, 协同学, 元胞自动机, 计算机建模

Abstract: 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 Model (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 results 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.

Key words: network public sentiment, dissemination prediction, synergistic theory, cellular automata, computer modelling