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Research Article | Open Access

Exploring the evolutionary characteristics of social media data in metro emergencies: A case study of Zhengzhou Metro flood

Yiqi Zhou1Fucai Hua2Junfeng Chen1Maohua Zhong1,3( )
Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing 100084, China
Beijing Urban Construction Design & Development Group Co., Limited, Beijing 100037, China
Tsinghua University (Department of Engineering Physics)–Beijing Urban Construction Design & Development Group Co., Limited Joint Research Center for Urban Disaster Prevention and Safety, Beijing 100084, China
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Abstract

With the development of urban transportation, metros have become an important means of travel for residents. However, casualty and economic loss might occur in metro systems due to various emergencies. Social media has gradually become the main way to express people’s needs, which provides a new analysis perspective for risk management in metros. This study takes the Zhengzhou Metro flood as an example and collects relevant social media data. Then, the analysis method of social media data evolution characteristics in metro emergencies is proposed. Finally, the evolution characteristics of social media data are analyzed from three aspects: spatiotemporal distribution, emotional distribution, and hot topics classification. The results show that: The temporal distribution of social media data is affected by the emergency process and official media; the spatial distribution of social media data reflects the distribution of stations affected by emergency and temporary shelters; timely and appropriate official media reports are conducive to guiding public emotions toward positive; and the key hot topics can be divided into disaster environment (DE), disaster impact (DI), disaster carrier (DC), emergency management (EM), positive comment (PC), and negative comment (NC). The proposed method can provide support for public opinion analysis and risk management in metro emergencies.

References

[1]
China Association of Metros. Annual statistical analysis report of urban rail transit in 2021 [Online]. https://www.camet.org.cn/tjxx/9944. (accessed 2023-09-18). (in Chinese)
[2]

H. M. Lyu, G. F. Wang, J. S. Shen, et al. Analysis and GIS mapping of flooding hazards on 10 May 2016, Guangzhou, China. Water, 2016, 8: 447.

[3]

X. H. Han, J. L. Wang. Using social media to mine and analyze public sentiment during a disaster: A case study of the 2018 Shouguang City flood in China. ISPRS Int J Geo-Inf, 2019, 8: 185.

[4]

K. M. Carley, M. Malik, P. M. Landwehr, et al. Crowd sourcing disaster management: The complex nature of Twitter usage in Padang Indonesia. Saf Sci, 2016, 90: 48–61.

[5]

P. M. Landwehr, W. Wei, M. Kowalchuck, et al. Using tweets to support disaster planning, warning and response. Saf Sci, 2016, 90: 33–47.

[6]

S. Q. Shan, F. Zhao, Y. G. Wei, et al. Disaster management 20: A real-time disaster damage assessment model based on mobile social media data—A case study of Weibo (Chinese Twitter). Saf Sci, 2019, 115: 393–413.

[7]

K. J. Wu, J. D. Wu, W. Ding, et al. Extracting disaster information based on Sina Weibo in China: A case study of the 2019 typhoon Lekima. Int J Disaster Risk Reduct, 2021, 60: 102304.

[8]

Y. D. Wang, T. Wang, X. Y. Ye, et al. Using social media for emergency response and urban sustainability: A case study of the 2012 Beijing rainstorm. Sustainability, 2016, 8: 25.

[9]

D. W. Li, Y. J. Zhang, C. Li. Mining public opinion on transportation systems based on social media data. Sustainability, 2019, 11: 4016.

[10]

S. L. Luo, S. Y. He. Using data mining to explore the spatial and temporal dynamics of perceptions of metro services in China: The case of Shenzhen. Environ Plann B:Urban Anal City Sci, 2021, 48: 449–466.

[11]

H. He. Research on prediction of internet public opinion based on grey system theory and fuzzy neural network. J Intell Fuzzy Syst, 2018, 35: 325–332.

[12]

F. Zhao, Y. F. Liao. Research on the dissemination characteristics and influencing factors of network public opinion of sudden natural disaster events. J Geo-Inf Sci, 2021, 23: 992–1001. (in Chinese)

[13]

J. T. Tang, S. N. Yang, Y. M. Liu, et al. Typhoon risk perception: A case study of typhoon Lekima in China. Int J Disaster Risk Sci, 2022, 13: 261–274.

[14]

Z. H. Peng, R. Wang, L. B. Liu, et al. Exploring urban spatial features of COVID-19 transmission in Wuhan based on social media data. Int J Geo-Inf, 2020, 9: 402.

[15]

Z. Y. Xing, X. D. Zhang, X. L. Zan, et al. Crowdsourced social media and mobile phone signaling data for disaster impact assessment: A case study of the 8.8 Jiuzhaigou earthquake. Int J Disaster Risk Reduct, 2021, 58: 102200.

[16]

S. I. Garske, S. Elayan, M. Sykora, et al. Space–time dependence of emotions on Twitter after a natural disaster. Int J Environ Res Public Health, 2021, 18: 5292.

[17]

O. Gruebner, S. R. Lowe, M. Sykora, et al. Spatio–temporal distribution of negative emotions in New York City after a natural disaster as seen in social media. Int J Environ Res Public Health, 2018, 15: 2275.

[18]

C. Villavicencio, J. J. Macrohon, X. A. Inbaraj, et al. Twitter sentiment analysis towards COVID-19 vaccines in the Philippines using Naive Bayes. Information, 2021, 12: 204.

[19]

V. Balakrishnan, M. Kaity, H. A. Rahim, et al. Social media analytics using sentiment and content analyses on the 2018 Malaysia’s general election. Malays J Comput Sci, 2021, 34: 171–183.

[20]

T. Y. Wang, K. Lu, K. P. Chow, et al. COVID-19 sensing: Negative sentiment analysis on social media in China via BERT model. IEEE Access, 2020, 8: 138162–138169.

[21]

H. T. Zhang, D. Wang, H. L. Xu, et al. Sentiment classification of Micro-blog public opinion based on convolution neural network. J China Soc Sci Tech Inf, 2018, 37: 695–702. (in Chinese)

[22]

G. Wang, J. S. Sun, J. Ma, et al. Sentiment classification: The contribution of ensemble learning. Decis Support Syst, 2014, 57: 77–93.

[23]

H. Bai, G. Yu. A Weibo-based approach to disaster informatics: Incidents monitor in post-disaster situation via Weibo text negative sentiment analysis. Nat Hazards, 2016, 83: 1177–1196.

[24]

G. X. Xu, Y. T. Meng, Z. Chen, et al. Research on topic detection and tracking for online news texts. IEEE Access, 2019, 7: 58407–58418.

[25]

Y. X. Li, Z. K. Wang, X. P. Feng, et al. Micro-blog hot-spot topic discovery based on real-time word co-occurrence network. J Comput Appl, 2016, 36: 1302–1306. (in Chinese)

[26]

S. P. Li, F. Zhao, Y. Q. Zhou, et al. Analysis of public opinion and disaster loss estimates from typhoons based on Microblog data. J Tsinghua Univ (Sci Technol), 2022, 62: 43–51. (in Chinese)

[27]

N. W. Xue, F. Xia, F. D. Chiou, et al. The Penn Chinese TreeBank: Phrase structure annotation of a large corpus. Nat Lang Eng, 2005, 11: 207–238.

[28]

K. Zhao, N. Shi, Z. Sa, et al. Text mining and analysis of treatise on febrile diseases based on natural language processing. World J Tradit Chin Med, 2020, 6: 67–73.

[29]

R. Rani, D. K. Lobiyal. Performance evaluation of text-mining models with Hindi stopwords lists. J King Saud Univ—Com, 2022, 34: 2771–2786.

[30]

Y. Luo, S. L. Zhao, X. C. Li, et al. Text keyword extraction method based on word frequency statistics. J Comput Appl, 2016, 36: 718–725. (in Chinese)

[31]

W. C. Fan, Y. Liu, W. G. Weng. Triangular framework and “4 + 1” methodology for public security science and technology. Sci Technol Rev, 2009, 27: 3. (in Chinese)

[32]

W. M. Xue, X. Hou, N. Li. A text categorization method based on word2vec. J Beijing Inform Sci Technol Univ, 2018, 33: 71–75. (in Chinese)

[33]
Ministry of Emergency Management of the People’s Republic of China. Investigation report on “7·20” extremely heavy rainstorm disaster in Zhengzhou, Henan Province [Online]. https://www.mem.gov.cn/gk/sgcc/tbzdsgdcbg/202201/P020220121639049697767.pdf (accessed 2023-09-18). (in Chinese)
[34]

Z. Y. Luo, J. Zeng. Analysis on urbanization quality, urban resilience and disaster risk of typhoon rainstorm: Take 7 southeast coastal provinces and cities for example. Sci Technol Rev, 2021, 39: 124–134. (in Chinese)

[35]

Y. F. Zhou, Z. H. Li, Y. Y. Meng, et al. Analyzing spatio–temporal impacts of extreme rainfall events on metro ridership characteristics. Physica A, 2021, 577: 126053.

[36]

Z. J. Ren, P. Zhang, S. C. Li, et al. Analysis of emotion evolution of emergencies based on Weibo data mining: Taking “8·12 accident in Tianjin” as an example. J Intell, 2019, 38: 140–148. (in Chinese)

Journal of Intelligent Construction
Pages 9180027-9180027
Cite this article:
Zhou Y, Hua F, Chen J, et al. Exploring the evolutionary characteristics of social media data in metro emergencies: A case study of Zhengzhou Metro flood. Journal of Intelligent Construction, 2023, 1(4): 9180027. https://doi.org/10.26599/JIC.2023.9180027
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Received: 18 September 2023
Revised: 26 October 2023
Accepted: 27 October 2023
Published: 18 December 2023
© The Author(s) 2023. Published by Tsinghua University Press.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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