党政研究Issue(4)：4-13,10.
利用机器学习技术防范网络意识形态风险的理论模型与逻辑进路
Theoretical Model and Logical Approach for Mitigating Online Ideological Risks through Machine Learning Techniques
摘要
Abstract
In the era of new media,the rapid development of the internet presents significant challenges to Chi-na's ideological security and governance.Modern machine learning techniques,particularly those based on deep neural networks,have the ability to extract meaning from vast amounts of unstructured textual data.Their appli-cations have expanded from simple image recognition to complex video content analysis,providing deeper in-sights into understanding and predicting public behavior and underlying ideologies.Given that predicting network dissemination paths relies on forecasting information forwarding,which reflects individual user behavior differ-ences,we propose a behavioral dissemination model for identifying online ideological risks based on machine learning.This system consists of an information collection layer,a data analysis layer,and an early warning lay-er.It enhances real-time awareness of the development and evolution of online ideologies,enabling relevant stakeholders to promptly assess and make decisions regarding ideological risks.This advances the intelligent management of online ideological risks.关键词
机器学习/网络空间/意识形态/数据分析/风险管理Key words
Machine Learning/Cyberspace/Ideology/Data Analysis/Risk Management分类
社会科学引用本文复制引用
秦博,徐浩铭..利用机器学习技术防范网络意识形态风险的理论模型与逻辑进路[J].党政研究,2024,(4):4-13,10.基金项目
国家社科基金项目"中国科技形象在推特的计算传播对策研究"(20CXW016) （20CXW016）
