大数据环境下网络舆情演化规律及预警模型问题探讨

Exploration on Evolution Law and Early Warning Model of Network Public Opinion

大数据环境下的网络舆情事件可以分为突发型、持续型、混合型等几类。通过对网络舆情真实案例的数据提取、挖掘、分析,可得到结构化数据,在此基础上,再对新闻报道量与时间序列的离散点进行函数拟合——基于多项式函数拟合,可建立预警数学模型。利用其图像特征分析所得出的突发型舆情事件符合指数函数分布,持续型舆情事件符合多峰值的高斯分布函数,混合型舆情事件符合分段函数的概率分布特征。据此有助于做好网络舆情监控、对策制定、预警机制建立等工作。

Online public opinion events can be classified into emergent, persistent and mixed types in the big data envi. ronment. Structured data are obtained through extraction, excavation and analysis of the data from the real cases. An early warning mathematical model is established based on polynomial function fitting of discrete points of news reporting volume and time series with these structured data.Analysis from the image features shows that the emergencies public opinion event conforms to the exponential function distribution, the persistent public opinion event conforms to the Gaussian distribution function with multiple peaks, and the mixed public opinion event conforms to the probability distribution of the segmented function. It is helpful to monitoring network public opinion,making corresponding countermeasures, and establishing an ear. ly warning mechanism.