一、方向简介
随着互联网平台（尤其是短视频与社交媒体）的快速发展，网络舆情呈现出传播速度快、影响范围广、演化复杂性高等特征，已成为影响社会稳定与公共安全的重要因素。当前舆情风险不仅来源于传统信息传播，还受到多模态信息融合与AIGC（生成式人工智能）内容生成的深刻影响。
本研究方向面向复杂网络环境下的舆情风险问题，围绕 “舆情感知—风险识别—干预决策”全过程，综合运用多模态感知分析技术、计算机视觉分析、数据分析与挖掘、人工智能预测等技术，构建数据驱动与智能建模相结合的网络舆情安全理论体系与关键技术，实现舆情风险的精准识别、动态预测与智能防控。
I. Introduction to the Direction
With the rapid development of internet platforms, particularly short-video and social media platforms, online public opinion has become characterized by rapid dissemination, broad influence, and high evolutionary complexity, and has emerged as an important factor affecting social stability and public safety. Current public opinion risks not only originate from traditional information dissemination but are also deeply influenced by multimodal information fusion and AIGC (Artificial Intelligence Generated Content).
This research direction addresses public opinion risk issues in complex online environments. Focusing on the entire process of “public opinion perception—risk identification—intervention decision-making,” it comprehensively applies multimodal perception and analysis technologies, computer vision analysis, data analysis and mining, and artificial intelligence prediction technologies to develop a theoretical framework and key technologies for online public opinion security that integrates data-driven approaches with intelligent modeling, enabling accurate identification, dynamic prediction, and intelligent prevention and control of public opinion risks.
二、研究内容
II. Research Content
网络舆情风险感知与智能决策总体框架
Overall Framework for Online Public Opinion Risk Perception and Intelligent Decision-Making
网络舆情风险识别与演化预测方法研究
面向网络空间中舆情信息传播复杂、风险演化快速的问题，融合文本、图像、视频、音频等多模态内容数据，以及用户行为信息与传播结构数据，开展舆情风险识别与发展趋势预测研究。通过构建多源数据驱动的联合分析模型，实现舆情内容真实性判别、异常信息识别及舆情发展趋势预测，支撑对舆情风险的动态监测与前瞻性预警，为舆情安全管理提供数据支撑。
Research on Online Public Opinion Risk Identification and Evolution Prediction Methods
Addressing the complexity of public opinion information dissemination and the rapid evolution of risks in cyberspace, research is conducted on public opinion risk identification and development trend prediction by integrating multimodal content data such as text, images, videos, and audio, as well as user behavior information and dissemination structure data. By constructing a joint analysis model driven by multi-source data, the research enables the identification of public opinion content authenticity, detection of abnormal information, and prediction of public opinion development trends, supporting dynamic monitoring and forward-looking early warning of public opinion risks and providing data support for public opinion security management.
网络舆情风险识别与演化预测方法研究
Research on Online Public Opinion Risk Identification and Evolution Prediction Methods
课题组围绕舆情风险分析中的关键问题，重点研究融合表征、判别与时序建模的综合方法。针对多模态信息表达差异及传播过程动态变化带来的识别与预测难题，构建跨模态语义建模与时序分析相结合的舆情分析框架，实现舆情内容特征与传播特征的协同建模；同时引入语义关联分析与动态演化建模方法，对舆情信息一致性与发展趋势进行联合刻画。进一步通过特征融合与模型优化机制，建立舆情风险识别与趋势预测一体化模型，实现对异常舆情的精准识别及未来发展方向的有效预判。
The research group focuses on key issues in public opinion risk analysis, with an emphasis on integrated methods that combine representation, discrimination, and temporal modeling. To address the challenges of identification and prediction caused by differences in multimodal information representation and dynamic changes in the dissemination process, a public opinion analysis framework combining cross-modal semantic modeling and temporal analysis has been developed to achieve joint modeling of public opinion content features and dissemination features. At the same time, semantic association analysis and dynamic evolution modeling methods are introduced to jointly characterize the consistency and development trends of public opinion information. Furthermore, through feature fusion and model optimization mechanisms, an integrated model for public opinion risk identification and trend prediction is established, enabling accurate identification of abnormal public opinion and effective prediction of its future development direction.
网络舆情干预机制与智能决策方法研究
课题组围绕舆情治理过程中的决策复杂性与动态性问题，重点研究数据驱动的智能决策方法。针对多主体参与及复杂传播环境下的舆情调控需求，引入优化模型与知识推理方法，构建舆情干预策略生成机制；同时结合舆情演化模型与实时反馈信息，对干预效果进行动态评估与调整。进一步通过策略优化与过程建模，建立从舆情分析、趋势预测到干预执行的闭环决策体系，实现舆情治理过程的智能化与精细化管理，为网络空间安全与公共治理提供技术支撑。
Research on Online Public Opinion Intervention Mechanisms and Intelligent Decision-Making Methods
The research group focuses on the complexity and dynamic nature of decision-making in the public opinion governance process, with an emphasis on data-driven intelligent decision-making methods. To address the needs for public opinion regulation involving multiple stakeholders and complex dissemination environments, optimization models and knowledge reasoning methods are introduced to develop a mechanism for generating public opinion intervention strategies. At the same time, public opinion evolution models and real-time feedback information are incorporated to dynamically evaluate and adjust intervention effectiveness. Furthermore, through strategy optimization and process modeling, a closed-loop decision-making system is established, covering public opinion analysis, trend prediction, and intervention execution, enabling intelligent and refined management of the public opinion governance process and providing technical support for cyberspace security and public governance.
