Early Warning Scheme of COVID-19 related Internet Public Opinion based on RVM-L Model

Rongbo Zhu; Qianao Ding; Mai Yu; Jun Wang; Maode Ma

doi:10.1016/j.scs.2021.103141

Early Warning Scheme of COVID-19 related Internet Public Opinion based on RVM-L Model

Sustain Cities Soc. 2021 Nov:74:103141. doi: 10.1016/j.scs.2021.103141. Epub 2021 Jul 10.

Authors

Rongbo Zhu¹, Qianao Ding², Mai Yu², Jun Wang², Maode Ma³

Affiliations

¹ College of Informatics, Huazhong Agricultural University, Wuhan, China.
² College of Computer Science, South-Central University for Nationalities, Wuhan, China.
³ School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore.

Abstract

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%.

Keywords: COVID-19; Early warning scheme; Lagrange interpolation; Logistic regression; Relevance vector machine; critical interval.