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Title: The emergency network public opinion risk identification and early warning model from BP neural network

Authors: Shuling Chen; Shuai Yuan

Addresses: School of Social and Public Administration, Lingnan Normal University, Zhanjiang, 524000, China ' Zhanjiang Customs Anti_Smuggling Bureau, Zhanjiang, 524000, China

Abstract: This paper aims to solve the problem of emergency network public opinion (NPO) risk identification (RI) and early warning (EW). Firstly, the back propagation neural network (BPNN) optimised by Genetic Algorithm (GA) is used to process and model the data obtained on the network, identify the public opinion risk of emergencies, and realise the risk prediction and early warning. Secondly, through the analysis and mining of NPO data of emergencies, the factors affecting the risk of NPO, such as social media platforms, user characteristics, and text content, are explored. These factors are incorporated into the model to improve the predictive ability of the model. Finally, through the research, effective Risk Management (RM) and countermeasures of NPO in emergencies are proposed to provide feasible RM schemes for governments, enterprises, and the public to ensure social stability and security.

Keywords: BPNN; back propagation neural network; data mining; emergencies; NPO; network public opinion; risk identification and early warning.

DOI: 10.1504/IJDS.2025.151182

International Journal of Data Science, 2025 Vol.10 No.7, pp.93 - 113

Received: 03 Apr 2025
Accepted: 26 May 2025
Published online: 16 Jan 2026 *