Multi-Scale Sliding-Window Fitting Model for Evolution of Information Dissemination in Weibo

Abstract

With the development of information technology, the speed and scope of dissemination have been greatly improved, the content of information directly affects the social order, and the mining of communication laws has become a hot issue. At present, there are three common analytical models, which are based on the popularity curve fitting, interpersonal networks simulation, and machine learning models. Although these models play a major role in diffusion effects analysis, there are also problems such as the data analysis results are too dependent on the data and the versatility is weak. Based on the classical Schramm's eight elements theory and previous research results, in this study, we identified evolution curve usually has multiple peaks, and assumed a composite decomposition function to establish a mathematical abstraction. Draws on the denoising autoencoder neural network model, a multi-scale sliding-window model is proposed for fitting the energy variety curve with the number of microblogs as the influence index. The theoretical basis of the model makes it interpretable, and because of the use of a large number of known news dissemination data for training, so the versatility is better, and the denoising characteristics of the autoencoder model weaken the intensity of data cleaning. Finally, compared with the SIR function and SEIR function, the experiment shows that the fitting error of the function obtained from the model is significantly reduced. However, the model’s algorithm needs to be improved in parallel processing, and the model itself needs to be further optimized.