Orchestrating Public Opinion Risks in Dynamic Social Networks with Dissipative Theory

With the prevalence of social media and digital in-formation dissemination, the dynamism and uncertainty of public opinion risks have become increasingly pronounced, which has posed challenges to public opinion risk management. Traditional monitoring and intervention methods are increasingly inadequate for the nuanced demands of digital information flow, necessitating the adoption of more sophisticated and effective approaches by risk management entities. In this paper, we present our approach to managing public opinion risks in dynamic social networks through the integration of machine learning and dissipative theory. We recognize that the dynamic nature of public opinion, exacerbated by the rise of social media, requires more advanced and adaptive strategies for risk management. Our proposed method leverages machine learning, particularly the Naive Bayes classifier, to process large datasets and predict public sentiment trends with high accuracy. Additionally, we explore the use of dissipative theory to create a flexible and collaborative control mechanism for public opinion risks. This approach allows for real-time monitoring, risk assessment, and timely interventions, thus improving the effectiveness of public sentiment management. By combining these methodologies, we aim to develop a more resilient and responsive system for mitigating the impact of negative public sentiment in the digital age.