Analysis of the Driving Factors for Fan economy Growth on Social Media Platforms Based on the Push-Pull Theory: Taking the Largest Platform in China as an Example

As a characteristic barrage video website with ACG-related content as the dominant factor, Bilibili, the largest platform in China, exhibits a high level of interactivity. From the perspective of "people - objects - scenarios," this study employs a two-stage SEM-ANN method based on the push-pull theory and combines artificial neural networks. It utilizes the structural equation model to explore the driving influence factors of the growth in the number of Bilibili video fans. The total number of likes, coins, forwards, barrages, collections, and comments are employed as push influence factors, encompassing the testing of hypotheses regarding linear relationships in the compensation model and non-linear non-compensation relationships in the neural network model, along with multiple regression analysis. Python is utilized to obtain relevant data to study the impact of the average video playback volume on the growth of the number of fans. The average video update time, the section where the UP owner is located, the average video duration, and the total number of videos are used as pull factors to explore the impact of the total video playback volume on the growth of the number of fans. The research indicates that the above-mentioned push-pull influence factors are valid in driving the growth of the number of fans. Additionally, variable video recommendation indexes and the number of videos with likes or playback volumes exceeding 4% are added to the research model to explore the most significant and least influential growth driving factors among the push-pull factors. This provides a reference for the research on the fan economy of all platforms.