Derivative Topic Dissemination Model Based on Multitopic Iterative Derivation and Social Psychology
Rong Wang, Kexin Ma, Xiaole Guo, Shihong Wei, Zhiwei Wang, Tun Li, Yunpeng Xiao · IEEE Transactions on Computational Social Systems · 2024
As topics evolve during the dissemination process, their derivative characteristics play an important role in revealing the mechanism of topic dissemination in social networks. Due to users’ cognitive inertia, followers of antecedent topics tend to pay more attention to derivative topics than ordinary users, and this cognitive inertia is directly related to the correlation between these two topics. Based on this finding, we propose a derivation topic dissemination model based on iterative multitopic derivation and social psychology, taking into full consideration users’ emotional accumulation of antecedent topics and repeated derivation of topics. First, a multiple linear regression model is used to construct a metric algorithm for user antecedent sentiment and to effectively analyze the dynamics of antecedent sentiment accumulation affecting the spread of derivative topics. Second, to analyze the interactions between and within multitopic layers in full, an iterative multitopic dissemination model iterative-susceptible infectious recovery (SIR) is proposed. In addition, the association degree is introduced to define the cross-model state transition equation by considering the association and differences among multitopics. Last, considering the influence of cognitive inertia and “continuous attention psychology” in social psychology, we construct a user psychology-based driving force model which can further improve the cross-model state transition equation. According to the experiments, the model can effectively reveal the influence of different factors on the dissemination trend of derivative topics in social networks, as well as depict the dissemination dynamics of derivative topics.