Construction of the Social Network Information Dissemination Index System Based on CNNs

Weihong Han, Linhe Xiao, Xiaobo Wu, Daihai He, Zhen Wang, Shudong Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

The information dissemination index system is an effective way to measure the dissemination of public opinion events in social networks. Due to the complexity, variability, and asymmetry of information, the construction of traditional information dissemination index systems demands excessive reliance on manual intervention, has large deviations, and is applied in a limited range. Such shortcomings cannot meet the requirements of constructing an objective, comprehensive, and highly credible index system. Therefore, we propose a method of constructing a multilevel and multigranular information dissemination index system with complex perspectives. In addition, we use the deep learning method of the convolutional neural network to extract the rich convolution features of public opinion events in the information dissemination process. Then, we train the weight, and it forms the corresponding weight of the information dissemination index systems. The experimental results prove that the method we use is superior to other methods and has better performance on the data set of a specific field.

Original languageEnglish
Article number807099
Pages (from-to)1-8
Number of pages8
JournalFrontiers in Physics
Volume10
DOIs
Publication statusPublished - 7 Mar 2022

Keywords

  • convolutional neural network
  • deep learning
  • index system
  • information dissemination
  • social network

ASJC Scopus subject areas

  • Biophysics
  • Materials Science (miscellaneous)
  • Mathematical Physics
  • Physics and Astronomy(all)
  • Physical and Theoretical Chemistry

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