School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
School of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
Chaos. 2017 Aug;27(8):083113. doi: 10.1063/1.4998736.
To reveal heterogeneous behaviors of opinion evolution in different scenarios, we propose an opinion model with topic interactions. Individual opinions and topic features are represented by a multidimensional vector. We measure an agent's action towards a specific topic by the product of opinion and topic feature. When pairs of agents interact for a topic, their actions are introduced to opinion updates with bounded confidence. Simulation results show that a transition from a disordered state to a consensus state occurs at a critical point of the tolerance threshold, which depends on the opinion dimension. The critical point increases as the dimension of opinions increases. Multiple topics promote opinion interactions and lead to the formation of macroscopic opinion clusters. In addition, more topics accelerate the evolutionary process and weaken the effect of network topology. We use two sets of large-scale real data to evaluate the model, and the results prove its effectiveness in characterizing a real evolutionary process. Our model achieves high performance in individual action prediction and even outperforms state-of-the-art methods. Meanwhile, our model has much smaller computational complexity. This paper provides a demonstration for possible practical applications of theoretical opinion dynamics.
为了揭示不同场景下意见演化的异质行为，我们提出了一个具有主题交互的意见模型。个体意见和主题特征由多维向量表示。我们通过意见和主题特征的乘积来衡量个体对特定主题的行为。当一对代理针对某个主题进行交互时，他们的行为将通过有界置信度引入意见更新。仿真结果表明，在容忍阈值的临界点处，无序状态向共识状态的转变会发生，该临界点取决于意见的维度。随着意见维度的增加，临界点会增加。多个主题会促进意见的相互作用，并导致宏观意见群的形成。此外，更多的主题会加速演化过程并削弱网络拓扑结构的影响。我们使用两组大规模的真实数据来评估模型，结果证明了它在描述真实演化过程方面的有效性。我们的模型在个体行为预测方面表现出色，甚至超过了最先进的方法。同时，我们的模型计算复杂度较小。本文为理论意见动力学的可能实际应用提供了一个例证。
