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Sentiment Evolution of Online Public Opinion of Emergency Situations in Railway Stations: A Case Study of Wuhan Railway Stations

Author

Listed:
  • Yifan Wu

    (Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China)

  • Fan Zhang

    (Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong SAR, China)

  • Albert P. C. Chan

    (Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong SAR, China)

  • Dezhi Li

    (School of Civil Engineering, Southeast University, Nanjing 211189, China)

Abstract

Preventing secondary crises resulting from emergency incidents in engineering projects is a crucial and complex task for project operation management. Public opinion and its underlying sentiment can act as reliable indicators, reflecting the progression of emergency incidents, and warrant serious consideration. With the advent of Web 2.0, the management of online public opinion (OPO) through social platforms has advanced significantly. However, previous research has overlooked the diverse categories of participants contributing to OPO evolution. This article proposes an optimised bounded confidence model (BCM) for sentiment OPO evolution under emergency situations at railway stations, incorporating multiple participant categories. A conceptual model based on eleven assumptions is developed, involving four key participants (netizens, media, opinion leaders, and government) structured into four sub-processes. To illustrate this model, the case of the Wuhan railway stations’ blockade during the COVID-19 outbreak is examined. This case study demonstrates the initial data acquisition and simulation process. The standard simulation results are recorded, followed by a multiple-sensitivity analysis to investigate the impact of various critical factor combinations on OPO evolution. Finally, policy recommendations are provided to government departments to enhance their response to emergency situations, particularly those involving railway stations, thereby ensuring public safety.

Suggested Citation

  • Yifan Wu & Fan Zhang & Albert P. C. Chan & Dezhi Li, 2025. "Sentiment Evolution of Online Public Opinion of Emergency Situations in Railway Stations: A Case Study of Wuhan Railway Stations," Sustainability, MDPI, vol. 17(2), pages 1-29, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:2:p:613-:d:1567054
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    References listed on IDEAS

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    1. Xiangdong Liu & Axiao Cao & Chuyang Li, 2021. "Novel Network Public Opinion Prediction and Guidance Model Based on “S-Curve”: Taking the Loss of Contact with “Malaysia Airlines”," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-13, July.
    2. Shan Gao & Ye Zhang & Wenhui Liu, 2021. "How Does Risk-Information Communication Affect the Rebound of Online Public Opinion of Public Emergencies in China?," IJERPH, MDPI, vol. 18(15), pages 1-14, July.
    3. Li Zhang & Peinan Wang, 2022. "Public Attitude and Opinion Leaders: Mapping Chinese Discussion of EU's Energy Role on Social Media," Journal of Common Market Studies, Wiley Blackwell, vol. 60(6), pages 1777-1796, November.
    4. Cheng, Chun & Yu, Changbin, 2019. "Opinion dynamics with bounded confidence and group pressure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 532(C).
    5. Javier Gómez-Serrano & Jean-Yves Le Boudec, 2012. "Comment On "Mixing Beliefs Among Interacting Agents"," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 15(07), pages 1-7.
    6. Elizabeth Hunter & Brian Mac Namee & John D. Kelleher, 2017. "A Taxonomy for Agent-Based Models in Human Infectious Disease Epidemiology," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(3), pages 1-2.
    7. Dietrich Stauffer, 2002. "The Sznajd Model Of Consensus Building With Limited Persuasion," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 13(03), pages 315-317.
    8. Firestone, Jeremy & Kempton, Willett, 2007. "Public opinion about large offshore wind power: Underlying factors," Energy Policy, Elsevier, vol. 35(3), pages 1584-1598, March.
    9. Guillaume Deffuant & David Neau & Frederic Amblard & Gérard Weisbuch, 2000. "Mixing beliefs among interacting agents," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 3(01n04), pages 87-98.
    10. Cheng’ai Sun & Caixia Jing & Xiaodan Zhou & Kun Li & Tangjun Li, 2023. "Research on SnCIR multi-opinion competitive communication model," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 34(10), pages 1-16, October.
    11. Chen, Shuwei & Glass, David H. & McCartney, Mark, 2016. "Characteristics of successful opinion leaders in a bounded confidence model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 426-436.
    12. Thomas Moore & Patrick Finley & Nancy Brodsky & Theresa Brown & Benjamin Apelberg & Bridget Ambrose & Robert Glass, 2015. "Modeling Education and Advertising with Opinion Dynamics," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(2), pages 1-7.
    13. Xin Wan & Yantong Zhang & Rubing Wang & Jingfeng Yuan & Mengliu Hu & Ruyu Li & Minye Wang & Ziyao Huang & Cheng Tu & Fujian Zhong & Wenjing Cui & Siew Ann Cheong, 2021. "Causation of Metro Operation Accidents in China: Calculation of Network Node Importance Based on DEMATEL and ISM," Complexity, Hindawi, vol. 2021, pages 1-16, November.
    14. Chung-Yuan Huang & Tzai-Hung Wen, 2014. "A Novel Private Attitude and Public Opinion Dynamics Model for Simulating Pluralistic Ignorance and Minority Influence," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 17(3), pages 1-8.
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