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The Functional Resonance Analysis Method (FRAM) offers a new way to understand internet public opinion. It shows managing subjective factors can speed up the decline of negative public sentiment.

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Area of Science:

  • Social Sciences
  • Computational Social Science
  • Information Science

Background:

  • Traditional public opinion models are inadequate for the internet era.
  • Internet public opinion is a complex phenomenon influenced by numerous interacting factors.
  • Negative public opinion can lead to significant security incidents.

Purpose of the Study:

  • To propose a novel model for analyzing and governing internet public opinion.
  • To address the dynamic and complex nature of online information dissemination.
  • To improve risk prediction and shorten the dissipation time of negative public opinion.

Main Methods:

  • Development of the Functional Resonance Analysis Method (FRAM) model.
  • Integration of network information dissemination stages, propagation rules, and textual sentiment resonance.
  • Application of resonance theory and deep learning to establish public opinion resonance functions.
  • Creation of a simulation model for analyzing public opinion triggers and development patterns.

Main Results:

  • The FRAM model effectively simulates real-world public opinion evolution.
  • Public opinion resonance comprises eleven subjective and three objective factors.
  • Managing subjective factors significantly accelerates the dissipation of negative online opinions.

Conclusions:

  • The FRAM model provides a more effective approach to public opinion governance.
  • This study highlights the importance of complex systems, functional identification, and functional resonance.
  • The findings offer a considerable improvement over previous risk-prediction models for online sentiment.