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Risk identification of public opinion on social media: a new approach based on cross-spatial network analysis

Yiming Li (Business School, Hohai University, Nanjing, China)
Xukan Xu (Business School, Hohai University, Nanjing, China)
Muhammad Riaz (School of International Trade and Economic, University of International Business and Economics, Beijing, China)
Yifan Su (Business School, Hohai University, Nanjing, China)

The Electronic Library

ISSN: 0264-0473

Article publication date: 20 May 2024

Issue publication date: 26 July 2024

166

Abstract

Purpose

This study aims to use geographical information on social media for public opinion risk identification during a crisis.

Design/methodology/approach

This study constructs a double-layer network that associates the online public opinion with geographical information. In the double-layer network, Gaussian process regression is used to train the prediction model for geographical locations. Second, cross-space information flow is described using local government data availability and regional internet development indicators. Finally, the structural characteristics and information flow of the double-layer network are explored to capture public opinion risks in a fine-grained manner. This study used the early stages of the COVID-19 outbreak for validation analyses, and it collected more than 90,000 pieces of public opinion data from microblogs.

Findings

In the early stages of the COVID-19 outbreak, the double-layer network exhibited a radiating state, and the information dissemination was more dependent on the nodes with higher in-degree. Moreover, the double-layer network structure showed geographical differences. The risk contagion was more significant in areas where information flow was prominent, but the influence of nodes was reduced.

Originality/value

Public opinion risk identification that incorporates geographical scenarios contributes to enhanced situational awareness. This study not only effectively extends geographical information on social media, but also provides valuable insights for accurately responding to public opinion.

Keywords

Acknowledgements

Funding: National Social Science Fund of China; 20&ZD125.

Citation

Li, Y., Xu, X., Riaz, M. and Su, Y. (2024), "Risk identification of public opinion on social media: a new approach based on cross-spatial network analysis", The Electronic Library, Vol. 42 No. 4, pp. 576-597. https://doi.org/10.1108/EL-09-2023-0208

Publisher

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Emerald Publishing Limited

Copyright © 2024, Emerald Publishing Limited

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