基于改进GM(1,n)的动态网络舆情预警模型  

Dynamic network public opinion early warning model based on improved GM(1,n)

作　　者：谢康 姜国庆[1] 郭杭鑫 刘峥[2] XIE Kang;JIANG Guoqing;GUO Hangxin;LIU Zheng(Network Security Technology Research and Development Center,The Third Research Institute of Ministry of Public Security,Shanghai 200031,China;School of Management,Shanghai University of Engineering Science,Shanghai 201620,China)

机构地区：[1]公安部第三研究所网络安全技术研发中心,上海200031 [2]上海工程技术大学管理学院,上海201620

出　　处：《计算机应用》2023年第1期299-305,共7页journal of Computer Applications

基　　金：信息网络安全公安部重点实验室开放课题(C20609)。

摘　　要：舆情的自由传播会导致网络集群行为的发生,易产生负面社会影响,威胁公共安全,因此建立网络舆情监控及预警机制是防控舆情传播、维护社会稳定的必要措施。首先,通过分析谣言的形成机制,构建了舆情发展预测指标体系;其次,通过建立多因素GM(1,n)模型对舆情发展的走向进行预测;然后,分别结合新陈代谢理论与马尔可夫理论改进上述预测模型;最后,以微博“新疆棉”事件和“成都四十九中”事件为例,对GM(1,n)模型、马尔可夫GM(1,n)模型和新陈代谢马尔可夫GM(1,n)模型预测舆情发展的能力进行对比,并比较了新陈代谢马尔可夫GM(1,n)模型与随机森林模型。实验结果表明,相较于原始模型与随机森林模型,新陈代谢马尔可夫GM(1,n)模型的平均预测精度分别提高了10.6和5.8%。可见,新陈代谢马尔可夫GM(1,n)模型在预测网络舆情发展趋势问题上具有良好的性能。The free spread of public opinions may lead to the occurrence of cyber collective behaviors, which are easy to cause negative social impacts and threaten public security. Therefore, the establishment of network public opinion monitoring and early warning mechanism is necessary to prevent and control the spread of public opinions and maintain social stability. Firstly, by analyzing the formation mechanism of rumors, a prediction index system of public opinion development was constructed. Secondly, the multifactor GM(1,n) model was established to predict the development trend of the public opinion. Then, the prediction model was improved by combining with metabolism theory and Markov theory. Finally, using the “Xinjiang cotton” event and “Chengdu No. 49 middle school” event in Weibo as examples, the abilities of the GM(1,n) model, the Markov GM(1,n) model and the metabolic Markov GM(1, n) model to predict the development of public opinions were compared,and the metabolic Markov GM(1, n) model was also compared with the random forest model.Experimental results show that the average prediction accuracy of the metabolic Markov GM(1, n) model is increased by 10. 6% and 5. 8% compared with those of the original GM(1, n) model and random forest model respectively. It can be seen that the metabolic Markov GM(1, n) model has good performance in predicting the development trend of network public opinions.

关 键 词：网络舆情 GM(1 n)模型 新陈代谢理论 马尔可夫理论 预警机制 随机森林 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]