Article: Web public opinion prediction of public events based on graph convolutional neural networks in big data Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.36 pp.87 - 102 Abstract: Aiming at the problems of complex topology and strong temporal dynamics in the prediction of online public opinion on public events in big data environment, this paper proposes the spatio-temporal graph convolutional public opinion prediction model that fuses heterogeneous graph convolution and time domain convolution. The framework employs spectral graph convolution for topology modelling and dilated temporal convolution for dynamic dependency capturing. A multi-entity graph is constructed based on public health emergency management ecosystem and microblog rumour database, and feature fusion is achieved through cross-platform attention mechanism. Experiments show that the model has an F1 value of 89.2% in public opinion detection and a heat prediction RMSE of 6.31, outperforming state-of-the-art baselines by 12.7% and 31.5% respectively. It can warn 93% of high-risk events 52 minutes in advance, enabling proactive intervention for public governance. Inderscience Publishers - linking academia, business and industry through research

Title: Web public opinion prediction of public events based on graph convolutional neural networks in big data

Authors: Xianli Zeng; Hui Deng; Qizhong Luo; Lina Xiong

Addresses: School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China ' Student Affairs Department (Office), Guilin University of Electronic Technology, Guilin, 541004, China ' Youth League Committee, Guilin University of Electronic Technology, Guilin, 541004, China ' School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin, 541004, China

Abstract: Aiming at the problems of complex topology and strong temporal dynamics in the prediction of online public opinion on public events in big data environment, this paper proposes the spatio-temporal graph convolutional public opinion prediction model that fuses heterogeneous graph convolution and time domain convolution. The framework employs spectral graph convolution for topology modelling and dilated temporal convolution for dynamic dependency capturing. A multi-entity graph is constructed based on public health emergency management ecosystem and microblog rumour database, and feature fusion is achieved through cross-platform attention mechanism. Experiments show that the model has an F1 value of 89.2% in public opinion detection and a heat prediction RMSE of 6.31, outperforming state-of-the-art baselines by 12.7% and 31.5% respectively. It can warn 93% of high-risk events 52 minutes in advance, enabling proactive intervention for public governance.

Keywords: graph convolutional neural network; opinion prediction; heterogeneous graph neural network; spatio-temporal modelling.

DOI: 10.1504/IJICT.2025.149055

International Journal of Information and Communication Technology, 2025 Vol.26 No.36, pp.87 - 102

Received: 11 Jul 2025
Accepted: 29 Aug 2025
Published online: 10 Oct 2025 *

Title: Web public opinion prediction of public events based on graph convolutional neural networks in big data

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