Analysis and Prediction of Microblog Hot Topics Propagation Based on Deep Learning

Abstract

Sina Microblog, China’s most popular social media platform, has a massive amount of data and users. The prediction of microblog forwarding has become a hot research topic in the current academic circle. The majority of current microblog forwarding research relies on traditional models that only use certain data properties or statistical features akin to term frequency-inverse document frequency (TF-IDF) for training but fail to extract microblog semantic-level information. In light of the popularity of evolutionary analysis and forecasting your own research only on particular moment popularity or predict popularity, finally, we only analyzed the influence of different factors on the final popularity, without considering the various factors in the popularity of the role played by different evolutionary stages and the popularity of evolutionary problems such as insufficient understanding. This article proposes a three-dimensional feature model. Average, trend, and cycle were used to fit and predict the prevalence, as well as the prevalence prediction method based on deep learning. We analyzed the hot topic evolvement popularity and defined a 3D character model. Regarding average, trend, and cycle and based on 3D feature model to create time series model to fit hot subject popularity evolution and forecast the short-term popularity of hot topic evolution, comparing Spike M and SH model, this work puts forward the model fit and greater accuracy. From the analysis and quantification of influencing factors in the three key stages of epidemic evolution, outbreak, peak, and decline, a prediction model of the epidemic based on a deep neural network was proposed, the different effects of each influencing factor in the evolution process of the epidemic were analyzed in detail, and the active period of the epidemic was predicted. In comparison with the Spike M and SVR models, the proposed technique has a greater effect and performance in terms of predictability and timeliness.