Zhu Jiefan, Yao Yiping, Tang Wenjie, Zhang Haoming
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China.
Entropy (Basel). 2022 Aug 12;24(8):1112. doi: 10.3390/e24081112.
In the past decade, various opinion dynamics models have been built to depict the evolutionary mechanism of opinions and use them to predict trends in public opinion. However, model-based predictions alone cannot eliminate the deviation caused by unforeseeable external factors, nor can they reduce the impact of the accumulated random error over time. To solve this problem, we propose a dynamic framework that combines a genetic algorithm and a particle filter algorithm to dynamically calibrate the parameters of the opinion dynamics model. First, we design a fitness function in accordance with public opinion and search for a set of model parameters that best match the initial observation. Second, with successive observations, we tracked the state of the opinion dynamic system by the average distribution of particles. We tested the framework by using several typical opinion dynamics models. The results demonstrate that the proposed method can dynamically calibrate the parameters of the opinion dynamics model to predict public opinion more accurately.
在过去十年中，已经构建了各种舆论动态模型来描述观点的演化机制，并利用它们来预测舆论趋势。然而，仅基于模型的预测无法消除不可预见的外部因素所导致的偏差，也无法减少随着时间积累的随机误差的影响。为了解决这个问题，我们提出了一个动态框架，该框架结合了遗传算法和粒子滤波算法，以动态校准舆论动态模型的参数。首先，我们根据舆论设计了一个适应度函数，并搜索一组与初始观测最匹配的模型参数。其次，通过连续观测，我们利用粒子的平均分布来跟踪舆论动态系统的状态。我们使用几个典型的舆论动态模型对该框架进行了测试。结果表明，所提出的方法可以动态校准舆论动态模型的参数，从而更准确地预测舆论。
