Research on Deduction of Social Media Public Opinion Based on UCIGR Model

Abstract

As the number of Internet users grows, Internet public opinion spreads rapidly on social media, presenting significant challenges for government and related agencies in managing public sentiment. In light of this, it is crucial to propose a public opinion deduction method that aligns with the current era. This paper combines communication dynamics research with real-world observation and analysis of public opinion communication processes. Building upon the UCIR model, the study introduces the UCIGR (unknown-contact-infective-guided-recovered) communication model, incorporating the instructor-accepted state into the model. The model establishes a law of secondary propagation of public opinion based on this state. Compared to the UCIR model, the UCIGR model proposed in this research demonstrates a 27% improvement in performance, offering a more realistic restoration of the dissemination process of public opinion information. Furthermore, leveraging the rules of the UCIGR model, the study deduces and analyzes public opinion from two dimensions, providing a new framework for governments and related institutions to formulate effective public opinion guidance strategies.