ArticleSustainable cities and society2021
Early Warning Scheme of COVID-19 related Internet Public Opinion based on RVM-L Model.
Article in Sustainable cities and society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Article
- Article
- Study on the mechanism of public attention to a major event: The outbreak of COVID-19 in China.Sustainable cities and society · 2022Article
- An efficient hardware architecture based on an ensemble of deep learning models for COVID -19 prediction.Sustainable cities and society · 2022Article
- Internet Rumors During the COVID-19 Pandemic: Dynamics of Topics and Public Psychologies.Frontiers in public health · 2021Article
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5 authors.
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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%.
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