Improving the Government’s Rumor–Refutation Effectiveness in Major Public Health Emergencies for Urban Resilience Enhancement: A Case Study of China during COVID-19
Abstract
Accelerated by the widespread use of social media, Internet rumors in major public health emergencies will destroy urban resilience. To find the path to improve the effectiveness of government rumor–refutation in major public health emergencies to enhance urban resilience, this paper creatively establishes an assessment research structure of the government’s rumor–refutation effectiveness in major public health emergencies, and an assessment criteria system from four perspectives of source, message, channel, and reviewer is constructed, an assessment method incorporating multicriteria decision-making and machine learning methods, i.e., optimal clustering-VIKORSort with hesitant fuzzy linguistic term sets based on combinatorial weighting is proposed. Subsequently, all 102 cases of government rumor–refutation during the COVID-19 lockdown in Wuhan in 2020 are taken as alternatives and assessed. The results show that Wuhan’s rumor–refutation effectiveness was not strong. Then, the investigated factors that constrain Wuhan’s emergency rumor–refutation effectiveness are diversified. Furthermore, this paper assesses the rumor–refutation effectiveness in Shanghai during the 2022 epidemic, obtains similar problems to Wuhan, and demonstrates the generalizability and robustness of the proposed method. Finally, based on the results, this paper proposes suggestions for improving the government’s rumor–refutation effectiveness in major public health emergencies to enhance urban resilience, which is a crucial contribution to combating Internet rumors and improving urban resilience in major public health emergencies.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the General Open Subject for Hubei Innovation and Development Research Institute, China (Grant No. CX2023-2-3).
References
Berlo, D. K. 1960. The process of communication: An introduction of theory and practice. Ann Arbor, MI: Univ. of Michigan.
Deli, I., and F. Karaaslan. 2021. “Generalized trapezoidal hesitant fuzzy numbers and their applications to multi criteria decision-making problems.” Soft Comput. 25 (2): 1017–1032. https://doi.org/10.1007/s00500-020-05201-2.
Demir, L., M. E. Akpınar, C. Araz, and M. A. Ilgın. 2018. “A green supplier evaluation system based on a new multi-criteria sorting method: VIKORSORT.” Expert Syst. Appl. 114 (Dec): 479–487. https://doi.org/10.1016/j.eswa.2018.07.071.
Diakoulaki, D., G. Mavrotas, and L. Papayannakis. 1995. “Determining objective weights in multiple criteria problems: The critic method.” Comput. Oper. Res. 22 (7): 217–231. https://doi.org/10.1016/j.ins.2019.04.033.
Eneh, N. E., A. O. Adeniyi, C. U. Akpuokwe, S. S. Bakare, and M. C. Titor-Addingi. 2024. “Urban resilience against environmental disasters: Comparing Lagos and New York.” World J. Adv. Res. Rev. 21 (2): 1909–1917. https://doi.org/10.30574/wjarr.2024.21.2.0637.
Feng, X., K. Hui, X. Deng, and G. Jiang. 2021. “Understanding how the semantic features of contents influence the diffusion of government microblogs: Moderating role of content topics.” Inf. Manage. 58 (8): 103547. https://doi.org/10.1016/j.im.2021.103547.
Gkontzis, A. F., S. Kotsiantis, G. Feretzakis, and V. S. Verykios. 2024. “Enhancing urban resilience: Smart city data analyses, forecasts, and digital twin techniques at the neighborhood level.” Future Internet 16 (2): 47. https://doi.org/10.3390/fi16020047.
Godschalk, D. R. 2003. “Urban hazard mitigation: Creating resilient cities.” Nat. Hazard. Rev. 4 (3): 136–143. https://doi.org/10.1061/(ASCE)1527-6988(2003)4:3(136).
Kim, H., E. M. Khan, J. Huh, and J. Srivastava. 2023. “Fighting negative rumors with trust-based rumor-refutation strategy.” J. Marketing Commun. 29 (7): 676–700. https://doi.org/10.1080/13527266.2022.2066155.
Li, W. 2024. “‘Amusing ourselves to death’: Mechanisms in cyberbullying prompted by rumors and denigration amidst the COVID-19 pandemic in China.” Technol. Soc. 76 (Mar): 102446. https://doi.org/10.1016/j.techsoc.2023.102446.
Li, Z., X. Du, Y. Zhao, Y. Tu, B. Lev, and L. Gan. 2022. “Lifecycle research of social media rumor refutation effectiveness based on machine learning and visualization technology.” Inf. Process. Manage. 59 (6): 103077. https://doi.org/10.1016/j.ipm.2022.103077.
Li, Z., Q. Zhang, X. Du, Y. Ma, and S. Wang. 2021. “Social media rumor refutation effectiveness: Evaluation, modelling and enhancement.” Inf. Process. Manage. 58 (1): 102420. https://doi.org/10.1016/j.ipm.2020.102420.
Li, Z., Y. Zhao, T. Duan, and J. Dai. 2023. “Configurational patterns for COVID-19 related social media rumor refutation effectiveness enhancement based on machine learning and fsQCA.” Inf. Process. Manage. 60 (3): 103303. https://doi.org/10.1016/j.ipm.2023.103303.
Liu, J., L. Liu, Y. Tu, S. Li, and Z. Li. 2022a. “Multi-stage internet public opinion risk grading analysis of public health emergencies: An empirical study on Microblog in COVID-19.” Inf. Process. Manage. 59 (1): 102796. https://doi.org/10.1016/j.ipm.2021.102796.
Liu, L., Y. Tu, and X. Zhou. 2022b. “How local outbreak of COVID-19 affect the risk of internet public opinion: A Chinese social media case study.” Technol. Soc. 71 (Nov): 102113. https://doi.org/10.1016/j.techsoc.2022.102113.
Liu, X., Q. Hu, J. Wang, X. Wu, and D. Hu. 2024a. “Difference in rumor dissemination and debunking before and after the relaxation of COVID-19 prevention and control measures in China: Infodemiology study.” J. Med. Internet Res. 26 (May): e48564. https://doi.org/10.2196/48564.
Liu, X., L. Zhang, L. Sun, and R. Liu. 2024b. “Survival analysis of the duration of rumors during the COVID-19 pandemic.” BMC Public Health 24 (1): 519. https://doi.org/10.1186/s12889-024-17991-3.
Mergel, I., A. Kleibrink, and J. Sörvik. 2018. “Open data outcomes: US cities between product and process innovation.” Gov. Inf. Q. 35 (4): 622–632. https://doi.org/10.1016/j.giq.2018.09.004.
Naeem, M., and W. Ozuem. 2022. “Understanding misinformation and rumors that generated panic buying as a social practice during COVID-19 pandemic: Evidence from Twitter, YouTube and focus group interviews.” Inf. Technol. People 35 (7): 2140–2166. https://doi.org/10.1108/ITP-01-2021-0061.
Oh, H. J., and H. Lee. 2019. “When do people verify and share health rumors on social media? The effects of message importance, health anxiety, and health literacy.” J. Health Commun. 24 (11): 837–847. https://doi.org/10.1080/10810730.2019.1677824.
Ozturk, P., H. Li, and Y. Sakamoto. 2015. “Combating rumor spread on social media: The effectiveness of refutation and warning.” In Proc., 2015 48th Hawaii Int. Conf. on System Sciences, 2406–2414. New York: IEEE. https://doi.org/10.1109/HICSS.2015.288.
Pertwee, E., C. Simas, and H. J. Larson. 2022. “An epidemic of uncertainty: Rumors, conspiracy theories and vaccine hesitancy.” Nat. Med. 28 (3): 456–459. https://doi.org/10.1038/s41591-022-01728-z.
Qian, J., et al. 2024. “Evaluating resilience of urban lifelines against flooding in China using social media data.” Int. J. Disaster Risk Reduct. 106 (May): 104453. https://doi.org/10.1016/j.ijdrr.2024.104453.
Simonds, S. 1995. “Communication theory and the search for effective feedback.” J. Hum. Hypertens. 9 (1): 5–10.
Tafesse, W. 2020. “YouTube marketing: How marketers’ video optimization practices influence video views.” Internet Res. 30 (6): 1689–1707. https://doi.org/10.1108/INTR-10-2019-0406.
Tu, Y., L. Cheng, L. Liu, Z. Li, and B. Lev. 2023. “How to improve the rumor-confutation ability of official rumor-refuting account on social media: A Chinese case study.” Comput. Commun. 209 (Sep): 331–348. https://doi.org/10.1016/j.comcom.2023.07.021.
Wang, Z., H. Liu, W. Liu, and S. Wang. 2020. “Understanding the power of opinion leaders’ influence on the diffusion process of popular mobile games: Travel Frog on Sina Weibo.” Comput. Human Behav. 109 (Aug): 106354. https://doi.org/10.1016/j.chb.2020.106354.
Wu, C., J. Cenci, W. Wang, and J. Zhang. 2022. “Resilient city: Characterization, challenges and outlooks.” Buildings 12 (5): 516. https://doi.org/10.3390/buildings12050516.
Wu, Y., M. Hu, M. Liao, F. Liu, and C. Xu. 2021. “Risk assessment of renewable energy-based island microgrid using the HFLTS-cloud model method.” J. Cleaner Prod. 284 (Feb): 125362. https://doi.org/10.1016/j.jclepro.2020.125362.
Xiang, T., Q. Li, W. Li, and Y. Xiao. 2023. “A rumor heat prediction model based on rumor and anti-rumor multiple messages and knowledge representation.” Inf. Process. Manage. 60 (3): 103337. https://doi.org/10.1016/j.ipm.2023.103337.
Xiao, Y., Y. Zhang, C. Zeng, T. Li, R. Wang, Q. Li, and C. Jia. 2024. “A prediction model for rumor user propagation behavior based on sparse representation and transfer learning.” Inf. Sci. 670 (Jun): 120590. https://doi.org/10.1016/j.ins.2024.120590.
Yin, M., L. Liu, L. Cheng, Z. Li, and Y. Tu. 2023. “A novel group multi-criteria sorting approach integrating social network analysis for ability assessment of health rumor-refutation accounts.” Expert Syst. Appl. 238 (Mar): 121894. https://doi.org/10.1016/j.eswa.2023.121894.
Zhang, Y., J. Xu, and Y. Wu. 2020. “A rumor control competition model considering intervention of the official rumor-refuting information.” Int. J. Modern Phys. C 31 (9): 2050123. https://doi.org/10.1142/S0129183120501235.
Information & Authors
Information
Published In
Natural Hazards Review
Volume 26 • Issue 1 • February 2025
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Mar 3, 2024
Accepted: Oct 1, 2024
Published online: Nov 26, 2024
Published in print: Feb 1, 2025
Discussion open until: Apr 26, 2025
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.