A Review of Intelligent Public Opinion Governance in Security and Protection Engineering
DOI:
https://doi.org/10.54097/tpgn2135Keywords:
Security and Protection Engineering, Social Media, Intelligent Public Opinion Governance, Deep LearningAbstract
This paper systematically explores the research methods and practical frameworks of intelligent public opinion governance in the context of social media, centered on the theory of public opinion governance within the secondary discipline of Security and Protection Engineering. Firstly, it analyzes the theoretical connotations of public opinion governance and its role in social risk prevention and control, clarifying the role and challenges faced by public security organs in public issue governance. Secondly, it constructs a fundamental framework for intelligent public opinion governance covering pre-event warning, early identification, situation prediction, control intervention, and fact-checking, providing a systematic and structured methodological foundation for public opinion governance. Based on this framework, and focusing on the disciplinary hotspot of intelligent public opinion governance on social media, the paper provides an in-depth review of recent advancements in related frontier research. Representative technical methods and solution pathways are systematically summarized according to the five governance stages, including: pre-event warning based on large language models, early identification through multimodal signal fusion, situation prediction using temporal graph neural networks, control intervention driven by reinforcement learning, and fact-checking combining retrieval-augmented generation with causal inference. Through this systematic review, this paper aims to provide theoretical references, methodological support, and technical insights for research on intelligent public opinion governance under the discipline of Security and Protection Engineering, promoting a paradigm shift in public opinion governance from experience-driven to data-driven, model-driven, and intelligent decision-making.
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