Zhu Rongbo, Ding Qianao, Yu Mai, Wang Jun, Ma Maode
College of Informatics, Huazhong Agricultural University, Wuhan, China.
College of Computer Science, South-Central University for Nationalities, Wuhan, China.
Sustain Cities Soc. 2021 Nov;74:103141. doi: 10.1016/j.scs.2021.103141. Epub 2021 Jul 10.
Internet public opinion is affected by many factors corresponding to insufficient data in the very short period, especially for emergency events related to the outbreak of coronavirus disease 2019 (COVID-19). To effectively support real-time analysis and accurate prediction, this paper proposes an early warning scheme, which comprehensively considers the multiple factors of Internet public opinion and the dynamic characteristics of burst events. A hybrid relevance vector machine and logistic regression (RVM-L) model is proposed that incorporates multivariate analysis, which adopts Lagrange interpolation to fill in the gaps and improve the forecasting effect based on insufficient data for COVID-19-related events. In addition, a novel metric critical interval is introduced to improve the early warning performance. Detailed experiments show that compared with existing schemes, the proposed RVM-L-based early warning scheme can achieve the prediction accuracy up to 96%, and the intervention within the critical interval can reduce the number of public opinions by 60%.
网络舆论受到许多因素的影响，在极短时间内数据存在不足，特别是对于与2019冠状病毒病（COVID-19）爆发相关的突发事件。为了有效支持实时分析和准确预测，本文提出了一种预警方案，该方案综合考虑了网络舆论的多种因素以及突发舆情事件的动态特征。提出了一种混合相关向量机和逻辑回归（RVM-L）模型，该模型结合了多变量分析，采用拉格朗日插值法来填补数据空白，并基于COVID-19相关事件的数据不足来提高预测效果。此外，引入了一种新的指标关键区间来提高预警性能。详细实验表明，与现有方案相比，所提出的基于RVM-L的预警方案预测准确率可达96%，且关键区间内的干预可使舆情数量减少60%。
