LDA-DeepHawkes Model for Predicting Information Cascade

Abstract

It is an important research point of social network analysis to predict future propagation range of infor-mation based on its early propagation characteristics. DeepHawkes model combines Hawkes model with deep learning, which not only inherits clear interpretability of Hawkes model to characterize and model the information diffusion process, but also carries on the high prediction power of end-to-end deep learning by automatically learning the latent representations of the input data, bridging the gap between prediction and understanding of information cascades. However, DeepHawkes model ignores the effect of the text content on the propagation. The LDA-Deep-Hawkes model takes cascade factors as well as text content into account, and models the process of information diffusion in a more comprehensive way, so as to further improve the prediction accuracy while inheriting the high interpretability of DeepHawkes model. The prediction accuracy of LDA-DeepHawkes model is compared with other models on two real data sets from Sina Weibo, and the influence of parameters of the model on the prediction accuracy is analyzed. The experimental results show that the LDA-DeepHawkes model has better prediction accuracy, indicating that the text content of information is also an important factor affecting the information diffusion.

Keywords

• prevalence prediction
• information cascade
• hawkes process
• deep learning
• latent drichlet allocation (lda) topic model