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Research on emotional tendency of earthquake disaster based on E-Trans model: take the topic of “Sichuan Earthquake” on microblog as an example

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Abstract

With the rapid development of network technology, research on the emotional tendencies of online disasters has increasingly played a significant role in responding to disaster public opinion events. This paper collected data from microblogs and selects three relatively large earthquakes that occurred in Sichuan in 2022 as research samples, namely the Lushan earthquake that occurred on June 1, the Malkang earthquake that occurred on June 10, and the Luding earthquake that occurred on September 5. By calling the E-Trans emotional analysis model, the emotional tendency values were obtained. Using Python language and a time–frequency of 15 min, the original content posted by microblog users within 24 h after the earthquake was analyzed for emotional tendencies, and research conclusions were drawn on the characteristics of emotional tendencies in earthquake disasters and related factors. The results show that the emotional tendencies of the three earthquakes conform to the “Three·Three” emotional tendency model for disasters, and the emotional tendency values in the three periods show a significant marginal diminishing effect. Changes in emotional tendencies during earthquake disasters are mainly related to the magnitude of the earthquake, the number of aftershocks, issues that the public is more concerned about on microblogs, and earthquake prevention and mitigation department microblog promotions. This study can provide a reference for post-disaster emergency response and network public opinion guidance for earthquake prevention and mitigation.

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Data availability

All data, models, and code that support the findings of this study are available from the corresponding author upon reasonable request.

References

  • Alaparthi S, Mishra M (2021) BERT: a sentiment analysis odyssey. J Mark Anal 9(2):118–126. https://doi.org/10.1057/s41270-021-00109-8

    Article  Google Scholar 

  • Bai H, Yu G (2016) A Weibo-based approach to disaster informatics: incidents monitor in post-disaster situation via Weibo text negative sentiment analysis. Nat Hazards 83(2):1177–1196. https://doi.org/10.1007/s11069-016-2370-5

    Article  Google Scholar 

  • Chen HH, Chen T (2015) A study on the characteristics of the dissemination time period and the government early warning model of online public opinion on emergencies. Res Libr Sci 01:24–30

    Google Scholar 

  • Chen J, Liu Y, Deng S (2017) Research on user reviews of government rumor-refuting information and factors influencing their emotional tendencies. Inf Sci 35(12):61–65

    Google Scholar 

  • Chen JY, Xia LX, Shu YX (2022) Research on the recognition method of network public opinion events from the perspective of sudden natural disasters. J Mod Inf (06):17–26+93

  • Fang J, Hu J, Shi X, Zhao L (2019) Assessing disaster impacts and response using social media data in China: a case study of 2016 Wuhan rainstorm. Int J Disaster Risk Reduct 34:275–282. https://doi.org/10.1016/j.ijdrr.2018.11.027

    Article  Google Scholar 

  • Guo HM, Zhao Z, Zhang Y, Zhang Y (2021) Analysis and evaluation of the characteristics of seismic disaster risk in the eastern region of Sichuan Daofu to Sichuan-Yunnan. J Nat Disasters 30:208–216

    Google Scholar 

  • Ilieva RT, McPhearson T (2018) Social-media data for urban sustainability. Nat Sustain 1(10):553–565. https://doi.org/10.1038/s41893-018-0153-6

    Article  Google Scholar 

  • Ji Y, Ma YF (2023) The robust maximum expert consensus model with risk aversion. Inf Fusion 99:101866. https://doi.org/10.1016/j.inffus.2023.101866

    Article  Google Scholar 

  • Ji Y, Li HH, Zhang HJ (2022) Risk-averse two-stage stochastic minimum cost consensus models with asymmetric adjustment cost. Group Decis Negot 31(2):261–291. https://doi.org/10.1007/s10726-021-09752-z

    Article  Google Scholar 

  • Kryvasheyeu Y, Chen H, Obradovich N et al (2016) Rapid assessment of disaster damage using social media activity. Sci Adv 2(3):e1500779. https://doi.org/10.1126/sciadv.1500779

    Article  Google Scholar 

  • Li SP, Zhao F, Zhou YQ, Tian XL, Huang H (2022) Analysis of public opinion and disaster loss estimates from typhoons based on Microblog data. J Tsinghua Univ (sci Technol) 62(1):43–51

    Google Scholar 

  • Liu Y (2007) Introduction to online public opinion research. Tianjin People's Publishing House

  • Liu Y, Yang H (2018) Internet public opinion information monitoring platform for natural disasters based on big data. J Catastrophology 33(4):13–17

    Google Scholar 

  • Liu LQ, Liu WJ, Dong WL (2017) Issue evolution of microblog public opinion in earthquake events. Jiangxi Soc Sci 08:236–242

    Google Scholar 

  • Liu L, Zhao DS, Zhu Y, Zheng D (2021) Spatiotemporal characteristics of earthquake hazard losses in mainland China during 1993–2017. J Nat Disasters 03:14–23

    Google Scholar 

  • Liu YH, Liu WT, Zhang WZ, Wei BY, Zheng GQ, Jin FX (2022) Spatiotemporal characteristics of public opinion and emotion analysis of MS 6.4 Yunnan Yangbi earthquake based on Sina Weibo data. J Nat Hazards 01:168–178

    Google Scholar 

  • Luo TY (2017) Identifying online hot topics based on Poisson distribution and Gamma distribution. J Mod Inf 01:77–80

    Google Scholar 

  • Lv XF, Chen SY (2016) Review of natural disaster network public opinion information analysis and management. Geogr Geo-Inf Sci 04:49–56

    Google Scholar 

  • National Committee for Disaster Reduction (2022) Notice of the national disaster reduction committee on issuing the “14th Five-Year Plan” national comprehensive disaster prevention and reduction plan. Ministry of Emergency Management of the People's Republic of China. https://www.mem.gov.cn/gk/zfxxgkpt/fdzdgknr/202207/t20220721_418698.shtml. Accessed 21 July 2022

  • Qiao YW, Zhong ZJ, Xu SK, Cao RM (2021) Emotional polarity and contagion pattern in unexpected public opinion: from the perspective of network analysis. J Jishou Univ (soc Sci Ed) 42(6):131

    Google Scholar 

  • Qu SJ, Shu LL, Yao JY (2022) Optimal pricing and service level in supply chain considering misreport behavior and fairness concern. Comput Ind Eng 174:108759. https://doi.org/10.1016/j.cie.2022.108759

    Article  Google Scholar 

  • Shan S, Zhao F, Wei Y, Liu M (2019) Disaster management 2.0: a real-time disaster damage assessment model based on mobile social media data—a case study of Weibo (Chinese Twitter). Saf Sci 115:393–413. https://doi.org/10.1016/j.ssci.2019.02.029

    Article  Google Scholar 

  • State Council (2022) Notice of the state council on the issuance of the “14th Five-Year Plan” national emergency response system planning. Ministry of Emergency Management of the People's Republic of China. https://www.mem.gov.cn/gk/zfxxgkpt/fdzdgknr/202208/t20220818_420530.shtml. Accessed 18 Aug 2022

  • Sun B (2021) Research on language emergency service strategies in public emergencies based on big data. J Catastrophology 36(4):146–151

    Google Scholar 

  • Wang YF, Ding XQ (2020) Modeling and analysis of the effect of frequent aftershocks on psychological stress of the masses after earthquake disaster. J Catastrophology 1:167–171

    Google Scholar 

  • Zhang B, Dong R (2022) How natural language processing technology empowers the AIED: the perspective of AI scientist. J East China Norm Univ (educ Sci) 40(9):19

    Google Scholar 

  • Zhang L, Peng TQ, Zhang YP, Wang XH, Zhu JJ (2014) Content or context: which matters more in information processing on microblogging sites. Comput Hum Behav 31:242–249. https://doi.org/10.1016/j.chb.2013.10.031

    Article  Google Scholar 

  • Zhang P, Zhang H, Kong F, Kong YL (2023) A study on public opinion characteristics of rainstorm flooding disasters based on Sina Weibo data: take the three rainstorm flooding disasters in China in 2021 as an example. Water Resour Hydropower Eng 54(2):47–59

    Google Scholar 

  • Zhao F, Liao YF (2021) Research on the dissemination characteristics and influencing factors of network public opinion of sudden natural disaster events. J Geo-Inf Sci 23(6):992–1001

    Google Scholar 

Download references

Funding

This study was supported by the National Social Science Fund of China (Grant No. 20BJY265) and Science and Technology Innovation Program for Postgraduate students in IDP subsidized by Fundamental Research Funds for the Central Universities (Grant No. ZY20240345).

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All authors contributed to the study of concept and design. Material preparation, data collation and analysis were carried out by Qinglu Yuan and Shujuan Wang, and data collection was completed by Nan Li. The first draft of the manuscript was written by Shujuan Wang, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Shujuan Wang.

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Yuan, Q., Wang, S. & Li, N. Research on emotional tendency of earthquake disaster based on E-Trans model: take the topic of “Sichuan Earthquake” on microblog as an example. Nat Hazards 120, 5057–5074 (2024). https://doi.org/10.1007/s11069-024-06421-7

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  • DOI: https://doi.org/10.1007/s11069-024-06421-7

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