A Social Commerce Marketing Topic Propagation Model Based on Factor Analysis and Stackelberg Game

This article investigates an emerging social commerce model that integrates social networks and e-commerce, highlighting the pivotal role of topic marketing in influencing consumers’ purchasing decisions. Within the framework of user-generated content (UGC), and accounting for the complex interplay among factors influencing topic propagation as well as the cooperative and competitive dynamics between topics, this study proposes a marketing topic propagation model based on factor analysis and the Stackelberg game. First, factor analysis was employed to extract two primary factors influencing topic propagation—user behavior and external environmental factors—thereby reducing the dimensionality of the variables. A multiple linear regression model was subsequently applied to quantify the contribution of these factors, establishing a robust quantitative foundation for optimizing marketing strategies. Second, to account for the competitive and cooperative interactions among topics, the behavior of ordinary users and influencers was analyzed using a Stackelberg game framework. This analysis reveals the complex interdependencies between cooperative and competitive dynamics in topic propagation. Finally, to address the behavioral variability and time-varying nature of users’ active participation or neglect (of topics), the traditional susceptible-infectious-recovered (SIR) model was refined by subdividing the infectious state into “marketing” and “neglected” states, resulting in the susceptible-marketing-neglected-recovered (SMNR) dynamic propagation model. By incorporating a temporal dimension, the model captures dynamic changes in states and parameters during topic propagation, thereby enhancing the accuracy of predictions regarding propagation paths and trends. Experimental results demonstrate that the proposed model effectively measures dynamic changes in influencing factors, incorporates game-theoretic relationships, and accurately predicts the temporal evolution of marketing topics. Its dynamic characteristics provide a robust theoretical foundation for optimizing social commerce marketing strategies and offer precise guidance for data-driven marketing decisions.