• 学术研究 •

### 基于LDA-DeepHawkes模型的信息级联预测

1. 1.云南大学 信息学院，昆明 650504
2.云南大学 公共管理学院，昆明 650504
• 出版日期:2020-03-01 发布日期:2020-03-13

### LDA-DeepHawkes Model for Predicting Information Cascade

WANG Shijie, ZHOU Lihua, KONG Bing, ZHOU Junhua

1. 1.School of Information Science & Engineering, Yunnan University, Kunming 650504, China
2.School of Public Administration, Yunnan University, Kunming 650504, China
• Online:2020-03-01 Published:2020-03-13

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.