Research on the evolution of college online public opinion risk based on improved Grey Wolf Optimizer combined with LSTM model.

ABSTRACT: Since the dissemination of information is more rapid and the scale of users on online platforms is enormous, the public opinion risk is more visible and harder to tackle for universities and authorities. Improving the accuracy of predictions regarding online public opinion crises, especially those related to campuses, is crucial for maintaining social stability. This research proposes a public opinion crisis prediction model that applies the Grey Wolf Optimizer (GWO) algorithm combined with long short-term memory (LSTM) and implements it to analyze a trending topic on Sina Weibo to validate its prediction accuracy. A full-chain analytical framework for online public opinion prediction is established in this study, which enables the model to illustrate the level of risk related to public op

SUBMITTER: Cao C 

PROVIDER: S-EPMC11761667 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature