Online Spreading of Topic Tags and Social Behavior
Fuzhong Nian, Jinhu Ren, Xuelong Yu
Abstract
This article explores information spreading in modern online social networks. According to the law of information spread in real social networks, information retweeting is divided into two types: topic retweeting and relationship retweeting. Social behavior is considered as a higher order interaction, and the nodal influence effect in the traditional approach is abstracted as part of it for analysis. The process of topic communities being subjected to social behavior is simulated by the social behavior model, and the dynamic retweeting rate is established. A network evolution model is constructed based on the centrality and noncontinuity characteristics of topic communities in the spread process. The social reinforcement effect in information spreading is described in two dimensions by defining topic expansion rate and topic diffusion rate. This work conducts multiple views of analysis and visualization, which provide more results of quantitative aspect. The validity of the model is verified by comparing the model simulation results with real cases and the generalization ability experiments.