Research on the Early-Warning Model of Network Public Opinion of Major Emergencies

The rapid development of Internet in recent years has led to a proliferation of social media networks as people who can gather online to share information, knowledge, and opinions. However, the network public opinion tends to generate strongly misleading and a large number of messages can cause shoc...

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Autores principales:	Li-Jie Peng, Xi-Gao Shao, Wan-Ming Huang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Major emergencies internet public opinion early warning index system COVID-19 CRITIC GA-BP neural network Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/7cbe37670dc145399b32bcb9b810c16e
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spelling	oai:doaj.org-article:7cbe37670dc145399b32bcb9b810c16e2021-11-19T00:06:34ZResearch on the Early-Warning Model of Network Public Opinion of Major Emergencies2169-353610.1109/ACCESS.2021.3066242https://doaj.org/article/7cbe37670dc145399b32bcb9b810c16e2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9380384/https://doaj.org/toc/2169-3536The rapid development of Internet in recent years has led to a proliferation of social media networks as people who can gather online to share information, knowledge, and opinions. However, the network public opinion tends to generate strongly misleading and a large number of messages can cause shocks to the public once major emergencies appear. Therefore, we need to make correct prediction regarding and timely identify a potential crisis in the early warning of network public opinion. In view of this, this study fully considers the features of development and the propagation characteristics, so as to construct a network public opinion early warning index system that includes 4 first-level indicators and 13 second-level indicators. The weight of each indicator is calculated by the “CRITIC” method, so that the comprehensive evaluation value of each time point can be obtained and the early warning level of internet public opinion can be divided. Then, the Back Propagation neural network based on Genetic Algorithm (GA-BP) is used to establish a network public opinion early warning model. Finally, the major public health emergency, COVID-19 pandemic, is taken as a case for empirical analysis. The results show that by comparing with the traditional classification methods, such as BP neural network, decision tree, random forest, support vector machine and naive Bayes, GA-BP neural network has a higher accuracy rate for early warning of network public opinion. Consequently, the index system and early warning model constructed in this study have good feasibility and can provide references for related research on internet public opinion.Li-Jie PengXi-Gao ShaoWan-Ming HuangIEEEarticleMajor emergenciesinternet public opinionearly warning index systemCOVID-19CRITICGA-BP neural networkElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 44162-44172 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Major emergencies internet public opinion early warning index system COVID-19 CRITIC GA-BP neural network Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Major emergencies internet public opinion early warning index system COVID-19 CRITIC GA-BP neural network Electrical engineering. Electronics. Nuclear engineering TK1-9971 Li-Jie Peng Xi-Gao Shao Wan-Ming Huang Research on the Early-Warning Model of Network Public Opinion of Major Emergencies
description	The rapid development of Internet in recent years has led to a proliferation of social media networks as people who can gather online to share information, knowledge, and opinions. However, the network public opinion tends to generate strongly misleading and a large number of messages can cause shocks to the public once major emergencies appear. Therefore, we need to make correct prediction regarding and timely identify a potential crisis in the early warning of network public opinion. In view of this, this study fully considers the features of development and the propagation characteristics, so as to construct a network public opinion early warning index system that includes 4 first-level indicators and 13 second-level indicators. The weight of each indicator is calculated by the “CRITIC” method, so that the comprehensive evaluation value of each time point can be obtained and the early warning level of internet public opinion can be divided. Then, the Back Propagation neural network based on Genetic Algorithm (GA-BP) is used to establish a network public opinion early warning model. Finally, the major public health emergency, COVID-19 pandemic, is taken as a case for empirical analysis. The results show that by comparing with the traditional classification methods, such as BP neural network, decision tree, random forest, support vector machine and naive Bayes, GA-BP neural network has a higher accuracy rate for early warning of network public opinion. Consequently, the index system and early warning model constructed in this study have good feasibility and can provide references for related research on internet public opinion.
format	article
author	Li-Jie Peng Xi-Gao Shao Wan-Ming Huang
author_facet	Li-Jie Peng Xi-Gao Shao Wan-Ming Huang
author_sort	Li-Jie Peng
title	Research on the Early-Warning Model of Network Public Opinion of Major Emergencies
title_short	Research on the Early-Warning Model of Network Public Opinion of Major Emergencies
title_full	Research on the Early-Warning Model of Network Public Opinion of Major Emergencies
title_fullStr	Research on the Early-Warning Model of Network Public Opinion of Major Emergencies
title_full_unstemmed	Research on the Early-Warning Model of Network Public Opinion of Major Emergencies
title_sort	research on the early-warning model of network public opinion of major emergencies
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/7cbe37670dc145399b32bcb9b810c16e
work_keys_str_mv	AT lijiepeng researchontheearlywarningmodelofnetworkpublicopinionofmajoremergencies AT xigaoshao researchontheearlywarningmodelofnetworkpublicopinionofmajoremergencies AT wanminghuang researchontheearlywarningmodelofnetworkpublicopinionofmajoremergencies
_version_	1718420618555162624

Research on the Early-Warning Model of Network Public Opinion of Major Emergencies

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