Influence Evaluation Model of Microblog User Based on Gaussian Bayesian Derivative Classifier

EasyChair Preprint 739

Abstract

In order to depict the influence of Weibo user, an evaluation model is proposed with Gaussian Bayesian derivatives. At first, the influence indexes of Weibo user is presented in this model with activity degree, relation degree and coverage degree. Combining the relationship characteristics between users and behavioral characteristics of user, the solved method for this model is given by Gaussian Bayesian derivatives. At last, a simulation is conducted to study the influence factor with experiment data of Sina Weibo users. The results show that, compared to other algorithm, this method has good adaptability.

Keyphrases: Gaussian Bayesian Derivatives, Weibo User, influence

@booklet{EasyChair:739,
  author    = {Zhe Zheng and Chunliang Zhou and Weipeng Zhang},
  title     = {Influence Evaluation Model of Microblog User Based on Gaussian Bayesian Derivative Classifier},
  howpublished = {EasyChair Preprint 739},
  year      = {EasyChair, 2019}}